Giving you information on all things Data Analytics!

Analyst Builder’s Substack

Do all of these things successfully and you will land a job as a data analyst.

<div class="captioned-image-container"><figure><a class="image-link image2 is-viewable-img" target="_blank" href="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3843ec9f-0b68-4267-b45f-faf5a8899e53_5184x3456.jpeg" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3843ec9f-0b68-4267-b45f-faf5a8899e53_5184x3456.jpeg 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3843ec9f-0b68-4267-b45f-faf5a8899e53_5184x3456.jpeg 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3843ec9f-0b68-4267-b45f-faf5a8899e53_5184x3456.jpeg 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3843ec9f-0b68-4267-b45f-faf5a8899e53_5184x3456.jpeg 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3843ec9f-0b68-4267-b45f-faf5a8899e53_5184x3456.jpeg" width="1456" height="971" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/3843ec9f-0b68-4267-b45f-faf5a8899e53_5184x3456.jpeg&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:971,&quot;width&quot;:1456,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:1057898,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/jpeg&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:null,&quot;isProcessing&quot;:false}" class="sizing-normal" alt="" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3843ec9f-0b68-4267-b45f-faf5a8899e53_5184x3456.jpeg 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3843ec9f-0b68-4267-b45f-faf5a8899e53_5184x3456.jpeg 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3843ec9f-0b68-4267-b45f-faf5a8899e53_5184x3456.jpeg 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3843ec9f-0b68-4267-b45f-faf5a8899e53_5184x3456.jpeg 1456w" sizes="100vw" fetchpriority="high"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><div class="pencraft pc-reset icon-container restack-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-refresh-cw"><path d="M3 12a9 9 0 0 1 9-9 9.75 9.75 0 0 1 6.74 2.74L21 8"></path><path d="M21 3v5h-5"></path><path d="M21 12a9 9 0 0 1-9 9 9.75 9.75 0 0 1-6.74-2.74L3 16"></path><path d="M8 16H3v5"></path></svg></div><div class="pencraft pc-reset icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></div></div></div></div></a></figure></div><p>There are lots of great Data Analyst Roadmaps out there (like <a href="https://www.analystbuilder.com/roadmaps/data-analyst-roadmap">THIS ONE</a> on Analyst Builder), but here is one that will take you less than 30 seconds to read.</p><h4><strong>1. Learn the Fundamentals</strong></h4><ul><li><p>Understand <strong>data basics</strong> (structured vs. unstructured data, databases).</p></li><li><p>Develop <strong>critical thinking</strong> and <strong>problem-solving</strong> skills.</p></li></ul><h4><strong>2. Master Essential Technical Skills</strong></h4><ul><li><p><strong><a href="https://www.analystbuilder.com/courses/excel-for-data-analytics">Excel</a></strong>: Data cleaning, pivot tables, formulas, Power Query.</p></li><li><p><strong><a href="https://www.analystbuilder.com/courses/mysql-for-data-analytics">SQL</a></strong>: Writing queries, joins, aggregations, window functions.</p></li><li><p><strong><a href="https://www.analystbuilder.com/courses/microsoft-power-bi-desktop-for-business-intelligence">Power BI</a>/<a href="https://www.analystbuilder.com/courses/tableau-for-data-visualization">Tableau</a></strong>: Building dashboards, storytelling with data.</p></li><li><p><strong><a href="https://www.analystbuilder.com/courses/python-programming-for-beginners">Python</a>/R</strong>: Data manipulation with Pandas/dplyr, visualization, statistics.</p></li></ul><h4><strong>3. Work on Real-World Projects</strong></h4><ul><li><p>Use <strong>public datasets</strong> (Kaggle, Google Dataset Search).</p></li><li><p>Build <strong>SQL projects</strong> (e.g., analyzing sales data).</p></li><li><p>Create <strong>Power BI/Tableau dashboards</strong>.</p></li></ul><h4><strong>4. Learn Statistics &amp; Business Intelligence</strong></h4><ul><li><p>Descriptive statistics, probability, hypothesis testing.</p></li><li><p>A/B testing, regression analysis, forecasting.</p></li><li><p>Understanding KPIs and business metrics.</p></li></ul><h4><strong>5. Build a Portfolio</strong></h4><ul><li><p>Publish projects on <strong>GitHub, Medium, Kaggle, or a personal website</strong>.</p></li><li><p>Show SQL queries, Python notebooks, and dashboards.</p></li><li><p>Write case studies explaining your data insights.</p></li></ul><h4><strong>6. Get Hands-On Experience</strong></h4><ul><li><p>Find <strong>internships, freelance gigs, or entry-level jobs</strong>.</p></li><li><p>Volunteer for <strong>non-profits or small businesses</strong> to analyze data.</p></li></ul><h4><strong>7. Prepare for Job Applications</strong></h4><ul><li><p>Craft a <strong><a href="https://www.analystbuilder.com/resources">strong resume</a> and LinkedIn profile</strong>.</p></li><li><p>Practice <strong><a href="https://www.analystbuilder.com/questions">technical interview questions</a></strong> (SQL, Python, business cases).</p></li><li><p>Network with <strong>data professionals</strong> on LinkedIn and attend meetups.</p></li></ul><h4><strong>8. Apply to Jobs &amp; Reach Out to Recruiters</strong></h4><ul><li><p><strong>Optimize your LinkedIn profile</strong> with relevant keywords and a strong headline.</p></li><li><p><strong>Apply to multiple jobs daily</strong>, tailoring your resume for each position.</p></li><li><p><strong>Message recruiters on LinkedIn</strong>, expressing interest in data roles.</p></li><li><p><strong>Use job boards</strong> (LinkedIn, Indeed, Glassdoor, company websites).</p></li><li><p><strong>Follow up after interviews</strong> with a thank-you email.</p></li></ul><h4><strong>9. Keep Learning &amp; Stay Updated</strong></h4><ul><li><p>Follow <strong>industry trends</strong> (AI, cloud analytics, automation).</p></li><li><p>Take <strong>advanced courses</strong> (big data, machine learning, cloud platforms).</p></li><li><p>Join <strong>data communities</strong> (Kaggle, Reddit, Analyst Builder Discord).</p></li></ul><p>I hope that helps give you some direction to follow to help you become a Data Analyst faster!</p>

There are lots of great Data Analyst Roadmaps out there (like THIS ONE on Analyst Builder), but here is one that will take you less than 30 seconds to read.
1. Learn the Fundamentals

Understand data basics (structured vs. unstructured data, databases).

Develop critical thinking and problem-solving skills.

2. Master Essential Technical Skills

Excel: Data cleaning, pivot tables, formulas, Power Query.

SQL: Writing queries, joins, aggregations, window functions.

Power BI/Tableau: Building dashboards, storytelling with data.

Python/R: Data manipulation with Pandas/dplyr, visualization, statistics.

3. Work on Real-World Projects

Use public datasets (Kaggle, Google Dataset Search).

Build SQL projects (e.g., analyzing sales data).

Create Power BI/Tableau dashboards.

4. Learn Statistics & Business Intelligence

Descriptive statistics, probability, hypothesis testing.

A/B testing, regression analysis, forecasting.

Understanding KPIs and business metrics.

5. Build a Portfolio

Publish projects on GitHub, Medium, Kaggle, or a personal website.

Show SQL queries, Python notebooks, and dashboards.

Write case studies explaining your data insights.

6. Get Hands-On Experience

Find internships, freelance gigs, or entry-level jobs.

Volunteer for non-profits or small businesses to analyze data.

7. Prepare for Job Applications

Craft a strong resume and LinkedIn profile.

Practice technical interview questions (SQL, Python, business cases).

Network with data professionals on LinkedIn and attend meetups.

8. Apply to Jobs & Reach Out to Recruiters

Optimize your LinkedIn profile with relevant keywords and a strong headline.

Apply to multiple jobs daily, tailoring your resume for each position.

Message recruiters on LinkedIn, expressing interest in data roles.

Use job boards (LinkedIn, Indeed, Glassdoor, company websites).

Follow up after interviews with a thank-you email.

9. Keep Learning & Stay Updated

Follow industry trends (AI, cloud analytics, automation).

Take advanced courses (big data, machine learning, cloud platforms).

Join data communities (Kaggle, Reddit, Analyst Builder Discord).

I hope that helps give you some direction to follow to help you become a Data Analyst faster!

The Best Data Analyst Roadmap to Follow

Landing your first data analyst job is tough because you don&#8217;t have any data analyst jobs to put on your resume.

<div class="captioned-image-container"><figure><a class="image-link image2 is-viewable-img" target="_blank" href="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F66655d61-0d0a-402d-89e7-8a59a4a7051a_1470x980.avif" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F66655d61-0d0a-402d-89e7-8a59a4a7051a_1470x980.avif 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F66655d61-0d0a-402d-89e7-8a59a4a7051a_1470x980.avif 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F66655d61-0d0a-402d-89e7-8a59a4a7051a_1470x980.avif 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F66655d61-0d0a-402d-89e7-8a59a4a7051a_1470x980.avif 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F66655d61-0d0a-402d-89e7-8a59a4a7051a_1470x980.avif" width="1456" height="971" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/66655d61-0d0a-402d-89e7-8a59a4a7051a_1470x980.avif&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:971,&quot;width&quot;:1456,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:52357,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/avif&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:null,&quot;isProcessing&quot;:false}" class="sizing-normal" alt="" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F66655d61-0d0a-402d-89e7-8a59a4a7051a_1470x980.avif 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F66655d61-0d0a-402d-89e7-8a59a4a7051a_1470x980.avif 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F66655d61-0d0a-402d-89e7-8a59a4a7051a_1470x980.avif 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F66655d61-0d0a-402d-89e7-8a59a4a7051a_1470x980.avif 1456w" sizes="100vw" fetchpriority="high"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><div class="pencraft pc-reset icon-container restack-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-refresh-cw"><path d="M3 12a9 9 0 0 1 9-9 9.75 9.75 0 0 1 6.74 2.74L21 8"></path><path d="M21 3v5h-5"></path><path d="M21 12a9 9 0 0 1-9 9 9.75 9.75 0 0 1-6.74-2.74L3 16"></path><path d="M8 16H3v5"></path></svg></div><div class="pencraft pc-reset icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></div></div></div></div></a></figure></div><p>Landing your first data analyst job is tough because you don&#8217;t have any data analyst jobs to put on your resume.</p><p>However, here are 4 tips to make your resume stand out to recruiters for an entry-level data analyst job.</p><ol><li><p><strong>Include links to your LinkedIn and project portfolio in your header</strong></p></li></ol><p>If you want to become a data analyst, having a great LinkedIn profile and project portfolio is crucial.</p><p>Once you&#8217;ve built these, they should make you look like a great data analyst, so let them do the selling.</p><p>If a recruiter spends 15 seconds looking at your resume, make it easy for them to look at your portfolio to see data projects you&#8217;ve worked on and check out your LinkedIn to learn more able you.</p><p>Include links to both in your header.</p><ol start="2"><li><p><strong>Include a project section in your resume</strong></p></li></ol><p>Sure, you don&#8217;t have any experience working as a data analyst, but if you&#8217;ve built projects using data tools, that experience is valuable.</p><p>Include a section near the top of your resume that details a couple projects you&#8217;ve worked on, tools and techniques you&#8217;ve used, and insights you&#8217;ve derived.</p><p>Pro tip: include hyperlinks to those projects in your portfolio so recruiters and hiring managers can dig deeper if they&#8217;d like.</p><ol start="3"><li><p><strong>Highlight transferrable skills</strong></p></li></ol><p>Even if you haven&#8217;t had the job title of data analyst, if you have any work experience at all, you have skills that are also important for data analytics!</p><p>These skills include good communication, time management, project management, and any kind of spreadsheet work.</p><p>You can sometimes even manufacture data projects at work by offering to create a spreadsheet/data visualization or something similar, and you can show that on your resume.</p><p>The key here is you have skills that will help you as a data analyst.</p><p>Highlight them!</p><ol start="4"><li><p><strong>Use a good template</strong></p></li></ol><p>If you have no idea where to start with building a resume, check out the templates available on the Analyst Builder resources page.</p><p>I put everything I know about resumes into these templates, and I hope they can help you land a job.</p><p>We also have a &#8220;<a href="https://www.analystbuilder.com/courses/landing-a-data-job">Landing a Data Job</a>&#8221; course that walks through exactly how to craft the perfect Data Analyst Resume!</p><p>Click the button below to go to the Analyst Builder resource page, then scroll down to the resume templates.</p><p class="button-wrapper" data-attrs="{&quot;url&quot;:&quot;https://www.analystbuilder.com/resources&quot;,&quot;text&quot;:&quot;Snag a resume template!&quot;,&quot;action&quot;:null,&quot;class&quot;:null}" data-component-name="ButtonCreateButton"><a class="button primary" href="https://www.analystbuilder.com/resources"><span>Snag a resume template!</span></a></p><p></p>

Landing your first data analyst job is tough because you don’t have any data analyst jobs to put on your resume.
However, here are 4 tips to make your resume stand out to recruiters for an entry-level data analyst job.

Include links to your LinkedIn and project portfolio in your header

If you want to become a data analyst, having a great LinkedIn profile and project portfolio is crucial.
Once you’ve built these, they should make you look like a great data analyst, so let them do the selling.
If a recruiter spends 15 seconds looking at your resume, make it easy for them to look at your portfolio to see data projects you’ve worked on and check out your LinkedIn to learn more able you.
Include links to both in your header.

Include a project section in your resume

Sure, you don’t have any experience working as a data analyst, but if you’ve built projects using data tools, that experience is valuable.
Include a section near the top of your resume that details a couple projects you’ve worked on, tools and techniques you’ve used, and insights you’ve derived.
Pro tip: include hyperlinks to those projects in your portfolio so recruiters and hiring managers can dig deeper if they’d like.

Highlight transferrable skills

Even if you haven’t had the job title of data analyst, if you have any work experience at all, you have skills that are also important for data analytics!
These skills include good communication, time management, project management, and any kind of spreadsheet work.
You can sometimes even manufacture data projects at work by offering to create a spreadsheet/data visualization or something similar, and you can show that on your resume.
The key here is you have skills that will help you as a data analyst.
Highlight them!

Use a good template

If you have no idea where to start with building a resume, check out the templates available on the Analyst Builder resources page.
I put everything I know about resumes into these templates, and I hope they can help you land a job.
We also have a “Landing a Data Job” course that walks through exactly how to craft the perfect Data Analyst Resume!
Click the button below to go to the Analyst Builder resource page, then scroll down to the resume templates.
Snag a resume template!

Landing your first data analyst job is tough because you don’t have any data analyst jobs to put on your resume.

4 Resume Tips to Land Your First Data Analyst Job 

If you want to become a data analyst, you need to learn technical skills like SQL and Excel, practice those skills, and build projects.

<div class="captioned-image-container"><figure><a class="image-link image2 is-viewable-img" target="_blank" href="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F74022e7d-5d8c-451b-8b4f-b40237ea50d7_1182x831.png" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F74022e7d-5d8c-451b-8b4f-b40237ea50d7_1182x831.png 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F74022e7d-5d8c-451b-8b4f-b40237ea50d7_1182x831.png 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F74022e7d-5d8c-451b-8b4f-b40237ea50d7_1182x831.png 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F74022e7d-5d8c-451b-8b4f-b40237ea50d7_1182x831.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F74022e7d-5d8c-451b-8b4f-b40237ea50d7_1182x831.png" width="554" height="389.48730964467006" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/74022e7d-5d8c-451b-8b4f-b40237ea50d7_1182x831.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:831,&quot;width&quot;:1182,&quot;resizeWidth&quot;:554,&quot;bytes&quot;:null,&quot;alt&quot;:&quot;Kaggle Datasets Tutorial: Kaggle Notebooks | DataCamp&quot;,&quot;title&quot;:null,&quot;type&quot;:null,&quot;href&quot;:null,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:null,&quot;isProcessing&quot;:false}" class="sizing-normal" alt="Kaggle Datasets Tutorial: Kaggle Notebooks | DataCamp" title="Kaggle Datasets Tutorial: Kaggle Notebooks | DataCamp" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F74022e7d-5d8c-451b-8b4f-b40237ea50d7_1182x831.png 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F74022e7d-5d8c-451b-8b4f-b40237ea50d7_1182x831.png 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F74022e7d-5d8c-451b-8b4f-b40237ea50d7_1182x831.png 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F74022e7d-5d8c-451b-8b4f-b40237ea50d7_1182x831.png 1456w" sizes="100vw" fetchpriority="high"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><div class="pencraft pc-reset icon-container restack-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-refresh-cw"><path d="M3 12a9 9 0 0 1 9-9 9.75 9.75 0 0 1 6.74 2.74L21 8"></path><path d="M21 3v5h-5"></path><path d="M21 12a9 9 0 0 1-9 9 9.75 9.75 0 0 1-6.74-2.74L3 16"></path><path d="M8 16H3v5"></path></svg></div><div class="pencraft pc-reset icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></div></div></div></div></a></figure></div><p><strong>If you want to become a data analyst, you need to learn technical skills like SQL and Excel, practice those skills, and build projects.</strong>&nbsp;</p><p>To do all three of those, you&#8217;re gonna need some <strong>data</strong>.</p><p>To help you out, here are 10 websites you can download datasets for free.</p><p>1. <strong>Kaggle</strong>&nbsp;(https://www.kaggle.com/datasets)</p><p>2. <strong>iPums</strong>&nbsp;(https://usa.ipums.org/usa/)</p><p>3. <strong>Tableau Public</strong>&nbsp;(https://www.tableau.com/learn/articles/free-public-data-sets)</p><p>4. <strong>Google Dataset Search</strong>&nbsp;(https://datasetsearch.research.google.com/)</p><p>5. <strong>FiveThirtyEight</strong>&nbsp;(https://data.fivethirtyeight.com/)</p><p>6. <strong>Data.gov</strong>&nbsp;(https://data.gov/)</p><p>7. <strong>Datahub.io</strong>&nbsp;(https://datahub.io/docs)</p><p>8. <strong>WHO Global Health Observatory</strong>&nbsp;(https://apps.who.int/gho/data/node.main)</p><p>9. <strong>UC Irvine Machine Learning Repository</strong>&nbsp;(https://archive.ics.uci.edu/datasets)</p><p>10. <strong>Data.world</strong> &nbsp;(https://data.world/)</p><p>I recommend starting with datasets like Kaggle which are already pre-cleaned. This will help you learn basic aggregations and simple analysis. After that you can work your way up to working with raw data on websites like Data.gov which can be quite dirty and are more challenging to work with. This is more representative of what you&#8217;ll find in the real world though.</p><p>Enjoy!</p>

If you want to become a data analyst, you need to learn technical skills like SQL and Excel, practice those skills, and build projects. 
To do all three of those, you’re gonna need some data.
To help you out, here are 10 websites you can download datasets for free.
1. Kaggle (https://www.kaggle.com/datasets)
2. iPums (https://usa.ipums.org/usa/)
3. Tableau Public (https://www.tableau.com/learn/articles/free-public-data-sets)
4. Google Dataset Search (https://datasetsearch.research.google.com/)
5. FiveThirtyEight (https://data.fivethirtyeight.com/)
6. Data.gov (https://data.gov/)
7. Datahub.io (https://datahub.io/docs)
8. WHO Global Health Observatory (https://apps.who.int/gho/data/node.main)
9. UC Irvine Machine Learning Repository (https://archive.ics.uci.edu/datasets)
10. Data.world  (https://data.world/)
I recommend starting with datasets like Kaggle which are already pre-cleaned. This will help you learn basic aggregations and simple analysis. After that you can work your way up to working with raw data on websites like Data.gov which can be quite dirty and are more challenging to work with. This is more representative of what you’ll find in the real world though.
Enjoy!

10 Best FREE Dataset Websites to Get Data!

Do you need Python to become a data analyst?

<div class="captioned-image-container"><figure><a class="image-link image2 is-viewable-img" target="_blank" href="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85cd3d08-0fe2-4bd3-9e92-2579ffdb4192_1200x600.jpeg" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85cd3d08-0fe2-4bd3-9e92-2579ffdb4192_1200x600.jpeg 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85cd3d08-0fe2-4bd3-9e92-2579ffdb4192_1200x600.jpeg 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85cd3d08-0fe2-4bd3-9e92-2579ffdb4192_1200x600.jpeg 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85cd3d08-0fe2-4bd3-9e92-2579ffdb4192_1200x600.jpeg 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85cd3d08-0fe2-4bd3-9e92-2579ffdb4192_1200x600.jpeg" width="614" height="307" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/85cd3d08-0fe2-4bd3-9e92-2579ffdb4192_1200x600.jpeg&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:600,&quot;width&quot;:1200,&quot;resizeWidth&quot;:614,&quot;bytes&quot;:null,&quot;alt&quot;:&quot;Top 10 Coding Mistakes in Python &amp; How to Avoid Them - ActiveState&quot;,&quot;title&quot;:null,&quot;type&quot;:null,&quot;href&quot;:null,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:null,&quot;isProcessing&quot;:false}" class="sizing-normal" alt="Top 10 Coding Mistakes in Python &amp; How to Avoid Them - ActiveState" title="Top 10 Coding Mistakes in Python &amp; How to Avoid Them - ActiveState" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85cd3d08-0fe2-4bd3-9e92-2579ffdb4192_1200x600.jpeg 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85cd3d08-0fe2-4bd3-9e92-2579ffdb4192_1200x600.jpeg 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85cd3d08-0fe2-4bd3-9e92-2579ffdb4192_1200x600.jpeg 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85cd3d08-0fe2-4bd3-9e92-2579ffdb4192_1200x600.jpeg 1456w" sizes="100vw" fetchpriority="high"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><div class="pencraft pc-reset icon-container restack-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-refresh-cw"><path d="M3 12a9 9 0 0 1 9-9 9.75 9.75 0 0 1 6.74 2.74L21 8"></path><path d="M21 3v5h-5"></path><path d="M21 12a9 9 0 0 1-9 9 9.75 9.75 0 0 1-6.74-2.74L3 16"></path><path d="M8 16H3v5"></path></svg></div><div class="pencraft pc-reset icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></div></div></div></div></a></figure></div><p>Do you need Python to become a data analyst?</p><p>The short answer is <strong>no</strong>, but let&#8217;s dive in a bit deeper</p><p>Most entry-level jobs require some combination of SQL, spreadsheets, and data visualization with Tableau or Power BI.&nbsp;Here are three reasons why you can still be successful without Python:</p><ol><li><p><strong>Excel Proficiency</strong>: Excel is a powerful tool for data manipulation, analysis, and visualization. Its advanced functions and add-ins can handle a wide range of analytical tasks.</p></li><li><p><strong>SQL Knowledge</strong>: SQL is essential for querying databases and extracting data. It&#8217;s a powerful tool for data analysis and is often more critical than Python in many data analyst roles.</p></li><li><p><strong>BI Tools</strong>: Business Intelligence (BI) tools like Tableau, Power BI, and QlikView are widely used for data visualization and reporting. These tools often have user-friendly interfaces that don&#8217;t require coding knowledge.</p></li></ol><p>So while you may not need it to get a job, I still really recommend learning it!</p><p>Here&#8217;s why:</p><ol><li><p><strong>Automation</strong>: Python can automate repetitive tasks, saving you time and reducing the risk of errors. This can be particularly useful for data cleaning and preprocessing.</p></li><li><p><strong>Advanced Analytics</strong>: Python has a rich ecosystem of libraries (like Pandas, NumPy, and Scikit-learn) that enable advanced statistical analysis and machine learning, which can enhance your analytical capabilities.</p></li><li><p><strong>Career Growth</strong>: Knowing Python can make you more competitive in the job market and open up opportunities for roles that require more technical skills, such as data science or machine learning engineering.</p></li></ol><p>I&#8217;ve found Python extremely helpful in all of these areas and I use it a lot more than I ever thought I would - especially for automating a ton of things.</p><p>If you want to check out my full Python Programming for Beginners course or Pandas for Data Analysis course you can check them out here:</p><p>https://www.analystbuilder.com/courses/python-programming-for-beginners</p><p>https://www.analystbuilder.com/courses/pandas-for-data-analysis</p><p></p>

Do you need Python to become a data analyst?
The short answer is no, but let’s dive in a bit deeper
Most entry-level jobs require some combination of SQL, spreadsheets, and data visualization with Tableau or Power BI. Here are three reasons why you can still be successful without Python:

Excel Proficiency: Excel is a powerful tool for data manipulation, analysis, and visualization. Its advanced functions and add-ins can handle a wide range of analytical tasks.

SQL Knowledge: SQL is essential for querying databases and extracting data. It’s a powerful tool for data analysis and is often more critical than Python in many data analyst roles.

BI Tools: Business Intelligence (BI) tools like Tableau, Power BI, and QlikView are widely used for data visualization and reporting. These tools often have user-friendly interfaces that don’t require coding knowledge.

So while you may not need it to get a job, I still really recommend learning it!
Here’s why:

Automation: Python can automate repetitive tasks, saving you time and reducing the risk of errors. This can be particularly useful for data cleaning and preprocessing.

Advanced Analytics: Python has a rich ecosystem of libraries (like Pandas, NumPy, and Scikit-learn) that enable advanced statistical analysis and machine learning, which can enhance your analytical capabilities.

Career Growth: Knowing Python can make you more competitive in the job market and open up opportunities for roles that require more technical skills, such as data science or machine learning engineering.

I’ve found Python extremely helpful in all of these areas and I use it a lot more than I ever thought I would - especially for automating a ton of things.
If you want to check out my full Python Programming for Beginners course or Pandas for Data Analysis course you can check them out here:
https://www.analystbuilder.com/courses/python-programming-for-beginners
https://www.analystbuilder.com/courses/pandas-for-data-analysis

Do You Need Python to Become a Data Analyst?

Projects are one of the most powerful tools to showcase your skills to a potential employers, but still not many people do them.

<div class="captioned-image-container"><figure><a class="image-link image2 is-viewable-img" target="_blank" href="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85e29009-c0b8-464f-8019-397e45fd1e60_1400x788.png" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85e29009-c0b8-464f-8019-397e45fd1e60_1400x788.png 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85e29009-c0b8-464f-8019-397e45fd1e60_1400x788.png 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85e29009-c0b8-464f-8019-397e45fd1e60_1400x788.png 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85e29009-c0b8-464f-8019-397e45fd1e60_1400x788.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85e29009-c0b8-464f-8019-397e45fd1e60_1400x788.png" width="1400" height="788" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/85e29009-c0b8-464f-8019-397e45fd1e60_1400x788.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:788,&quot;width&quot;:1400,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:105352,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:null,&quot;isProcessing&quot;:false}" class="sizing-normal" alt="" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85e29009-c0b8-464f-8019-397e45fd1e60_1400x788.png 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85e29009-c0b8-464f-8019-397e45fd1e60_1400x788.png 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85e29009-c0b8-464f-8019-397e45fd1e60_1400x788.png 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85e29009-c0b8-464f-8019-397e45fd1e60_1400x788.png 1456w" sizes="100vw" fetchpriority="high"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><div class="pencraft pc-reset icon-container restack-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-refresh-cw"><path d="M3 12a9 9 0 0 1 9-9 9.75 9.75 0 0 1 6.74 2.74L21 8"></path><path d="M21 3v5h-5"></path><path d="M21 12a9 9 0 0 1-9 9 9.75 9.75 0 0 1-6.74-2.74L3 16"></path><path d="M8 16H3v5"></path></svg></div><div class="pencraft pc-reset icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></div></div></div></div></a></figure></div><blockquote><p>Projects are one of the most powerful tools to showcase your skills to a potential employers, but still not many people do them.</p></blockquote><p>If you&#8217;ve spent any amount of time looking into becoming a data analyst, you&#8217;ve probably noticed a large emphasis on building projects to put into a portfolio.&nbsp;</p><p>I agree that this is a crucial part of becoming a data analyst, but this begs the question:&nbsp;</p><p>Why?&nbsp;</p><p>Projects and portfolios serve 3 main purposes:</p><div><hr></div><h3><strong>1. Projects help you apply your data learning to real-life scenarios.</strong>&nbsp;</h3><p>There is only so much you can learn from watching SQL videos and even doing practice problems.&nbsp;</p><p>Your real value as a data analyst comes from using critical thinking to apply your technical skills to solve unique problems.&nbsp;</p><p>When you&#8217;re on the job, you&#8217;re not going to be given explicit instructions of what functions to perform in tools like SQL and Tableau.&nbsp;</p><p>Rather, you&#8217;ll be given a request to analyze data, and it&#8217;s up to you to figure out how to use your skills to complete that analysis.&nbsp;</p><p>And, before you have a job, a project is the best way to mimic these on-the-job requests. &nbsp;</p><div><hr></div><h3><strong>2. Projects are used to show recruiters and hiring managers that you have the skills they are looking for.</strong>&nbsp;</h3><p>No one is born with experience.&nbsp;</p><p>But even entry level jobs want to see some level of competence before hiring you.&nbsp;</p><p>Therefore, you should build projects to show that you are able to use common data tools to solve problems and that you are able to effectively communicate the results.&nbsp;</p><p>Put them on your resume and be sure to have a link to your portfolio website so they can check them out! This will give them more confidence in your skills and help you land interviews.</p><div><hr></div><h3>3. You can talk about them as experience using a skill during an interview</h3><p>A common question you&#8217;ll get asked is: &#8220;How have you used SQL before?&#8221;</p><p>For most people they&#8217;ll answer that they&#8217;ve never used it before, but they&#8217;ve taken some courses and feel confident using it. Unfortunately, that doesn&#8217;t sound great to an interviewer&#8230;</p><p>Here&#8217;s what sounds much better: &#8220;I was recently working on a project where I used SQL to clean and analyze Housing Market data. I created a Stored Procedure to clean the data, and wrote SQL scripts to find trends and pattern in the data.&#8221;</p><p>This sounds a lot more impressive to an interviewer! </p><div><hr></div><p>With all that being said, I can&#8217;t recommend projects enough! That&#8217;s why do so many of them in our courses on AnalystBuilder.com! </p>

Projects are one of the most powerful tools to showcase your skills to a potential employers, but still not many people do them.

If you’ve spent any amount of time looking into becoming a data analyst, you’ve probably noticed a large emphasis on building projects to put into a portfolio. 
I agree that this is a crucial part of becoming a data analyst, but this begs the question: 
Why? 
Projects and portfolios serve 3 main purposes:


1. Projects help you apply your data learning to real-life scenarios. 
There is only so much you can learn from watching SQL videos and even doing practice problems. 
Your real value as a data analyst comes from using critical thinking to apply your technical skills to solve unique problems. 
When you’re on the job, you’re not going to be given explicit instructions of what functions to perform in tools like SQL and Tableau. 
Rather, you’ll be given a request to analyze data, and it’s up to you to figure out how to use your skills to complete that analysis. 
And, before you have a job, a project is the best way to mimic these on-the-job requests.  


2. Projects are used to show recruiters and hiring managers that you have the skills they are looking for. 
No one is born with experience. 
But even entry level jobs want to see some level of competence before hiring you. 
Therefore, you should build projects to show that you are able to use common data tools to solve problems and that you are able to effectively communicate the results. 
Put them on your resume and be sure to have a link to your portfolio website so they can check them out! This will give them more confidence in your skills and help you land interviews.


3. You can talk about them as experience using a skill during an interview
A common question you’ll get asked is: “How have you used SQL before?”
For most people they’ll answer that they’ve never used it before, but they’ve taken some courses and feel confident using it. Unfortunately, that doesn’t sound great to an interviewer…
Here’s what sounds much better: “I was recently working on a project where I used SQL to clean and analyze Housing Market data. I created a Stored Procedure to clean the data, and wrote SQL scripts to find trends and pattern in the data.”
This sounds a lot more impressive to an interviewer! 


With all that being said, I can’t recommend projects enough! That’s why do so many of them in our courses on AnalystBuilder.com!

What Are Data Analyst Projects For?

In today&#8217;s data-driven world, the role of a data analyst is more crucial than ever.

<div class="captioned-image-container"><figure><a class="image-link image2 is-viewable-img" target="_blank" href="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcabd43ce-c83a-4a0f-8acf-4e6ff5f9e004_992x711.webp" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcabd43ce-c83a-4a0f-8acf-4e6ff5f9e004_992x711.webp 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcabd43ce-c83a-4a0f-8acf-4e6ff5f9e004_992x711.webp 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcabd43ce-c83a-4a0f-8acf-4e6ff5f9e004_992x711.webp 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcabd43ce-c83a-4a0f-8acf-4e6ff5f9e004_992x711.webp 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcabd43ce-c83a-4a0f-8acf-4e6ff5f9e004_992x711.webp" width="448" height="321.0967741935484" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/cabd43ce-c83a-4a0f-8acf-4e6ff5f9e004_992x711.webp&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:711,&quot;width&quot;:992,&quot;resizeWidth&quot;:448,&quot;bytes&quot;:null,&quot;alt&quot;:&quot;Essential Top Data Analyst Skills: Your Key to Success&quot;,&quot;title&quot;:null,&quot;type&quot;:null,&quot;href&quot;:null,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:null,&quot;isProcessing&quot;:false}" class="sizing-normal" alt="Essential Top Data Analyst Skills: Your Key to Success" title="Essential Top Data Analyst Skills: Your Key to Success" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcabd43ce-c83a-4a0f-8acf-4e6ff5f9e004_992x711.webp 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcabd43ce-c83a-4a0f-8acf-4e6ff5f9e004_992x711.webp 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcabd43ce-c83a-4a0f-8acf-4e6ff5f9e004_992x711.webp 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcabd43ce-c83a-4a0f-8acf-4e6ff5f9e004_992x711.webp 1456w" sizes="100vw" fetchpriority="high"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><div class="pencraft pc-reset icon-container restack-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-refresh-cw"><path d="M3 12a9 9 0 0 1 9-9 9.75 9.75 0 0 1 6.74 2.74L21 8"></path><path d="M21 3v5h-5"></path><path d="M21 12a9 9 0 0 1-9 9 9.75 9.75 0 0 1-6.74-2.74L3 16"></path><path d="M8 16H3v5"></path></svg></div><div class="pencraft pc-reset icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></div></div></div></div></a></figure></div><p>In today&#8217;s data-driven world, the role of a data analyst is more crucial than ever. Businesses rely on data analysts to interpret complex data and provide actionable insights that drive decision-making. But what does it take to excel in this field? </p><p>Here are the top five essential skills every data analyst needs to succeed.</p><h4>1. <strong>Proficiency in Data Analysis Tools and Techniques</strong></h4><p>At the core of any data analyst&#8217;s skill set is the ability to effectively use data analysis tools. These tools help in collecting, cleaning, analyzing, and visualizing data. The most commonly used tools include:</p><ul><li><p><strong><a href="https://www.analystbuilder.com/courses/mysql-for-data-analytics">SQL</a></strong>: SQL (Structured Query Language) is the backbone of data management. It&#8217;s essential for querying databases, joining tables, and performing aggregations to extract meaningful insights from large datasets.</p></li><li><p><strong><a href="https://www.analystbuilder.com/courses/excel-for-data-analytics">Excel</a></strong>: While Excel might seem basic, it remains a powerful tool for data analysis, especially for quick calculations, pivot tables, and basic visualizations. Its versatility makes it indispensable for data analysts.</p></li><li><p><strong><a href="https://www.analystbuilder.com/courses/python-programming-for-beginners">Python</a>/R</strong>: Python and R are programming languages widely used in data analysis. They offer advanced statistical analysis, machine learning capabilities, and powerful libraries for data manipulation (like <a href="https://www.analystbuilder.com/courses/pandas-for-data-analysis">Pandas </a>in Python and dplyr in R).</p></li><li><p><strong><a href="https://www.analystbuilder.com/courses/tableau-for-data-visualization">Tableau</a>/Power BI</strong>: Data visualization tools like Tableau and Power BI are crucial for creating interactive dashboards that allow stakeholders to easily understand and explore the data.</p></li></ul><h4>2. <strong>Basic Statistical Knowledge</strong></h4><p>Understanding statistics is fundamental to analyzing data and drawing accurate conclusions. A data analyst must be comfortable with various statistical concepts, including:</p><ul><li><p><strong>Descriptive Statistics</strong>: Understanding measures like mean, median, mode, variance, and standard deviation helps in summarizing and describing data.</p></li><li><p><strong>Inferential Statistics</strong>: Concepts like hypothesis testing, confidence intervals, and regression analysis are useful for making predictions and informed decisions based on data samples.</p></li><li><p><strong>Probability</strong>: Probability theory helps in assessing the likelihood of events and is foundational for more advanced techniques like Bayesian analysis.</p></li></ul><p>Having a solid grasp of these statistical methods allows data analysts to make sense of the data, identify trends, and derive meaningful insights.</p><h4>3. <strong>Critical Thinking and Problem-Solving Skills</strong></h4><p>Data analysis is not just about crunching numbers; it&#8217;s about solving real-world problems. Critical thinking and problem-solving are vital skills that help data analysts:</p><ul><li><p><strong>Identify Key Issues</strong>: Understanding the business problem and knowing which data points are relevant is essential for effective analysis.</p></li><li><p><strong>Ask the Right Questions</strong>: Being curious and asking the right questions leads to deeper insights. For example, instead of simply looking at sales numbers, a good data analyst might ask, &#8220;What factors are driving changes in sales?&#8221;</p></li><li><p><strong>Make Data-Driven Decisions</strong>: Analysts must interpret data accurately and provide recommendations that are actionable and aligned with business goals.</p></li></ul><p>These skills are crucial for connecting the dots between data and actionable business strategies.</p><h4>4. <strong>Attention to Detail</strong></h4><p>In data analysis, even a small error can lead to incorrect conclusions. Attention to detail is a crucial skill that ensures accuracy in data handling and analysis:</p><ul><li><p><strong>Data Cleaning</strong>: Before any analysis, data must be clean and free of errors. This involves identifying and correcting inconsistencies, handling missing values, and ensuring the data is accurate.</p></li><li><p><strong>Validation and Verification</strong>: Double-checking calculations, ensuring consistency across datasets, and verifying the results are all part of maintaining data integrity.</p></li><li><p><strong>Documentation</strong>: Proper documentation of your methods and processes helps in maintaining transparency and allows others to replicate or understand your work.</p></li></ul><p>Meticulous attention to detail helps prevent costly mistakes and ensures the reliability of your analysis.</p><h4>5. <strong>Effective Communication Skills</strong></h4><p>The ability to communicate findings clearly and effectively is perhaps one of the most important skills for a data analyst. No matter how thorough your analysis is, it&#8217;s of little value if you can&#8217;t explain your insights to non-technical stakeholders. This involves:</p><ul><li><p><strong>Data Visualization</strong>: Creating clear and compelling visualizations that convey the key insights from your analysis. Good visualizations tell a story and make complex data accessible to everyone.</p></li><li><p><strong>Report Writing</strong>: Writing clear, concise reports that summarize your analysis and recommendations. These reports should be easy to understand, even for those without a data background.</p></li><li><p><strong>Presentation Skills</strong>: Being able to present your findings confidently, whether in meetings or through formal presentations, is crucial. You need to be able to translate complex data into actionable insights that resonate with your audience.</p></li></ul><p>Effective communication bridges the gap between data analysis and decision-making, making your insights valuable to the organization.</p><p>The role of a data analyst is both challenging and rewarding. By mastering these five essential skills&#8212;proficiency in data tools, strong statistical knowledge, critical thinking, attention to detail, and effective communication&#8212;you can set yourself apart and excel in this ever-evolving field. Remember, the key to success as a data analyst is not just in the technical skills you bring to the table, but also in your ability to apply those skills to solve real business problems and communicate your findings effectively.</p><p></p>

In today’s data-driven world, the role of a data analyst is more crucial than ever. Businesses rely on data analysts to interpret complex data and provide actionable insights that drive decision-making. But what does it take to excel in this field? 
Here are the top five essential skills every data analyst needs to succeed.
1. Proficiency in Data Analysis Tools and Techniques
At the core of any data analyst’s skill set is the ability to effectively use data analysis tools. These tools help in collecting, cleaning, analyzing, and visualizing data. The most commonly used tools include:

SQL: SQL (Structured Query Language) is the backbone of data management. It’s essential for querying databases, joining tables, and performing aggregations to extract meaningful insights from large datasets.

Excel: While Excel might seem basic, it remains a powerful tool for data analysis, especially for quick calculations, pivot tables, and basic visualizations. Its versatility makes it indispensable for data analysts.

Python/R: Python and R are programming languages widely used in data analysis. They offer advanced statistical analysis, machine learning capabilities, and powerful libraries for data manipulation (like Pandas in Python and dplyr in R).

Tableau/Power BI: Data visualization tools like Tableau and Power BI are crucial for creating interactive dashboards that allow stakeholders to easily understand and explore the data.

2. Basic Statistical Knowledge
Understanding statistics is fundamental to analyzing data and drawing accurate conclusions. A data analyst must be comfortable with various statistical concepts, including:

Descriptive Statistics: Understanding measures like mean, median, mode, variance, and standard deviation helps in summarizing and describing data.

Inferential Statistics: Concepts like hypothesis testing, confidence intervals, and regression analysis are useful for making predictions and informed decisions based on data samples.

Probability: Probability theory helps in assessing the likelihood of events and is foundational for more advanced techniques like Bayesian analysis.

Having a solid grasp of these statistical methods allows data analysts to make sense of the data, identify trends, and derive meaningful insights.
3. Critical Thinking and Problem-Solving Skills
Data analysis is not just about crunching numbers; it’s about solving real-world problems. Critical thinking and problem-solving are vital skills that help data analysts:

Identify Key Issues: Understanding the business problem and knowing which data points are relevant is essential for effective analysis.

Ask the Right Questions: Being curious and asking the right questions leads to deeper insights. For example, instead of simply looking at sales numbers, a good data analyst might ask, “What factors are driving changes in sales?”

Make Data-Driven Decisions: Analysts must interpret data accurately and provide recommendations that are actionable and aligned with business goals.

These skills are crucial for connecting the dots between data and actionable business strategies.
4. Attention to Detail
In data analysis, even a small error can lead to incorrect conclusions. Attention to detail is a crucial skill that ensures accuracy in data handling and analysis:

Data Cleaning: Before any analysis, data must be clean and free of errors. This involves identifying and correcting inconsistencies, handling missing values, and ensuring the data is accurate.

Validation and Verification: Double-checking calculations, ensuring consistency across datasets, and verifying the results are all part of maintaining data integrity.

Documentation: Proper documentation of your methods and processes helps in maintaining transparency and allows others to replicate or understand your work.

Meticulous attention to detail helps prevent costly mistakes and ensures the reliability of your analysis.
5. Effective Communication Skills
The ability to communicate findings clearly and effectively is perhaps one of the most important skills for a data analyst. No matter how thorough your analysis is, it’s of little value if you can’t explain your insights to non-technical stakeholders. This involves:

Data Visualization: Creating clear and compelling visualizations that convey the key insights from your analysis. Good visualizations tell a story and make complex data accessible to everyone.

Report Writing: Writing clear, concise reports that summarize your analysis and recommendations. These reports should be easy to understand, even for those without a data background.

Presentation Skills: Being able to present your findings confidently, whether in meetings or through formal presentations, is crucial. You need to be able to translate complex data into actionable insights that resonate with your audience.

Effective communication bridges the gap between data analysis and decision-making, making your insights valuable to the organization.
The role of a data analyst is both challenging and rewarding. By mastering these five essential skills—proficiency in data tools, strong statistical knowledge, critical thinking, attention to detail, and effective communication—you can set yourself apart and excel in this ever-evolving field. Remember, the key to success as a data analyst is not just in the technical skills you bring to the table, but also in your ability to apply those skills to solve real business problems and communicate your findings effectively.

In today’s data-driven world, the role of a data analyst is more crucial than ever.

Top 5 Essential Skills Every Data Analyst Needs to Succeed

If you're preparing for a data analyst interview, understanding the process is crucial.

<div class="captioned-image-container"><figure><a class="image-link image2" target="_blank" href="https://www.analystbuilder.com/courses/mysql-interview-crash-course" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb2ea14c7-2f96-49dd-b914-caf1eb1c94af_1080x660.png 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb2ea14c7-2f96-49dd-b914-caf1eb1c94af_1080x660.png 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb2ea14c7-2f96-49dd-b914-caf1eb1c94af_1080x660.png 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb2ea14c7-2f96-49dd-b914-caf1eb1c94af_1080x660.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb2ea14c7-2f96-49dd-b914-caf1eb1c94af_1080x660.png" width="538" height="328.77777777777777" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/b2ea14c7-2f96-49dd-b914-caf1eb1c94af_1080x660.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:660,&quot;width&quot;:1080,&quot;resizeWidth&quot;:538,&quot;bytes&quot;:null,&quot;alt&quot;:&quot;The Ideal Technical Interview Process and Tools&quot;,&quot;title&quot;:null,&quot;type&quot;:null,&quot;href&quot;:&quot;https://www.analystbuilder.com/courses/mysql-interview-crash-course&quot;,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:null,&quot;isProcessing&quot;:false}" class="sizing-normal" alt="The Ideal Technical Interview Process and Tools" title="The Ideal Technical Interview Process and Tools" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb2ea14c7-2f96-49dd-b914-caf1eb1c94af_1080x660.png 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb2ea14c7-2f96-49dd-b914-caf1eb1c94af_1080x660.png 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb2ea14c7-2f96-49dd-b914-caf1eb1c94af_1080x660.png 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb2ea14c7-2f96-49dd-b914-caf1eb1c94af_1080x660.png 1456w" sizes="100vw" fetchpriority="high"></picture><div></div></div></a></figure></div><p>If you're preparing for a data analyst interview, understanding the process is crucial. The interview process for a data analyst role typically consists of several stages, each designed to assess different aspects of your skills and suitability for the position. Below is a detailed breakdown of what you can expect during this process:</p><h4>1. <strong>Recruiter Screening Call</strong></h4><p>Your journey begins with a screening call from a recruiter. This initial conversation is often more about getting to know you on a surface level rather than diving deep into technical skills. Here's what to expect:</p><ul><li><p><strong>Experience Overview</strong>: The recruiter will ask you to walk through your resume, focusing on your relevant experience. This is your chance to highlight the key projects and roles that align with the position you're applying for.</p></li><li><p><strong>Interest and Motivation</strong>: You'll likely be asked why you're interested in the specific role and the company. Be prepared to discuss your career goals and how they align with the company's mission.</p></li><li><p><strong>Salary Expectations</strong>: The recruiter may ask about your salary expectations. It's wise to research the typical salary range for the role beforehand so you can provide a realistic figure.</p></li><li><p><strong>Role Details</strong>: The recruiter will also provide you with more information about the position, including team structure, responsibilities, and company culture.</p></li></ul><h4>2. <strong>Hiring Manager Interview</strong></h4><p>If you pass the recruiter screen, you'll move on to an interview with the hiring manager. This conversation is more in-depth and is typically focused on understanding how your skills and experience align with the needs of the team.</p><ul><li><p><strong>Detailed Experience Discussion</strong>: The hiring manager will likely ask for more specific examples of your past work, particularly those that are relevant to the role. Be prepared to discuss your thought process, challenges faced, and the outcomes of your projects.</p></li><li><p><strong>Role Alignment</strong>: They might probe into why you're interested in this particular role and how it fits into your career trajectory. They want to ensure that you're genuinely excited about the position and that your long-term goals align with the company's direction.</p></li><li><p><strong>Team Dynamics</strong>: You may also be asked how you handle working in teams, your communication style, and how you deal with feedback. The hiring manager is looking to gauge whether you'd be a good fit for the team's dynamic.</p></li></ul><h4>3. <strong>Technical Screen</strong></h4><p>The technical screen is often the most daunting part of the interview process, but it's where you can truly showcase your skills. This stage can vary depending on the company but generally involves one or more of the following:</p><ul><li><p><strong>SQL and Python Questions</strong>: You may be asked to solve problems or write queries in SQL and Python. These questions typically test your ability to manipulate data, perform aggregations, and apply various functions.</p></li><li><p><strong>Take-home Test</strong>: Some companies opt for a take-home assignment where you're given a dataset and asked to perform analysis. This allows them to see your problem-solving process, how you document your work, and the insights you draw from the data.</p></li><li><p><strong>Live Coding Challenge</strong>: In some cases, you may have to complete a coding challenge during a live interview. This tests your ability to think on your feet and solve problems under pressure.</p></li></ul><p>If you haven&#8217;t already, the <a href="https://www.analystbuilder.com/courses/mysql-interview-crash-course">MySQL Interview Crash Course</a> is an amazing place to be ready for your technical interviews!</p><h4>4. <strong>On-site Interview</strong></h4><p>The on-site interview (or final interview) is the last hurdle. This stage often includes multiple interviews, each focusing on different aspects of your abilities:</p><ul><li><p><strong>Business Acumen</strong>: You'll likely be asked to solve a business case or discuss how you would approach a real-world business problem. This is your chance to show that you can not only analyze data but also translate your findings into actionable business insights.</p></li><li><p><strong>Cultural Fit</strong>: You may meet with various team members who will assess whether you're a good fit for the company culture. This might include more casual conversations about your interests, work style, and how you handle work-life balance.</p></li></ul><h4>5. <strong>Waiting for an Offer</strong></h4><p>Once you've completed the interview stages, the waiting game begins. The company will review your performance across all interviews before making a decision. If you&#8217;re successful, you&#8217;ll receive an offer. If not, you might still receive feedback that can be valuable for your future interviews.</p><p>The data analyst interview process is thorough and can be challenging, but it's designed to ensure that you're the right fit for the role and the company. By understanding each stage and preparing accordingly, you can approach your interviews with confidence. Remember, every interview is a learning experience that brings you one step closer to landing your dream job.</p><p>Good luck!</p>

If you're preparing for a data analyst interview, understanding the process is crucial. The interview process for a data analyst role typically consists of several stages, each designed to assess different aspects of your skills and suitability for the position. Below is a detailed breakdown of what you can expect during this process:
1. Recruiter Screening Call
Your journey begins with a screening call from a recruiter. This initial conversation is often more about getting to know you on a surface level rather than diving deep into technical skills. Here's what to expect:

Experience Overview: The recruiter will ask you to walk through your resume, focusing on your relevant experience. This is your chance to highlight the key projects and roles that align with the position you're applying for.

Interest and Motivation: You'll likely be asked why you're interested in the specific role and the company. Be prepared to discuss your career goals and how they align with the company's mission.

Salary Expectations: The recruiter may ask about your salary expectations. It's wise to research the typical salary range for the role beforehand so you can provide a realistic figure.

Role Details: The recruiter will also provide you with more information about the position, including team structure, responsibilities, and company culture.

2. Hiring Manager Interview
If you pass the recruiter screen, you'll move on to an interview with the hiring manager. This conversation is more in-depth and is typically focused on understanding how your skills and experience align with the needs of the team.

Detailed Experience Discussion: The hiring manager will likely ask for more specific examples of your past work, particularly those that are relevant to the role. Be prepared to discuss your thought process, challenges faced, and the outcomes of your projects.

Role Alignment: They might probe into why you're interested in this particular role and how it fits into your career trajectory. They want to ensure that you're genuinely excited about the position and that your long-term goals align with the company's direction.

Team Dynamics: You may also be asked how you handle working in teams, your communication style, and how you deal with feedback. The hiring manager is looking to gauge whether you'd be a good fit for the team's dynamic.

3. Technical Screen
The technical screen is often the most daunting part of the interview process, but it's where you can truly showcase your skills. This stage can vary depending on the company but generally involves one or more of the following:

SQL and Python Questions: You may be asked to solve problems or write queries in SQL and Python. These questions typically test your ability to manipulate data, perform aggregations, and apply various functions.

Take-home Test: Some companies opt for a take-home assignment where you're given a dataset and asked to perform analysis. This allows them to see your problem-solving process, how you document your work, and the insights you draw from the data.

Live Coding Challenge: In some cases, you may have to complete a coding challenge during a live interview. This tests your ability to think on your feet and solve problems under pressure.

If you haven’t already, the MySQL Interview Crash Course is an amazing place to be ready for your technical interviews!
4. On-site Interview
The on-site interview (or final interview) is the last hurdle. This stage often includes multiple interviews, each focusing on different aspects of your abilities:

Business Acumen: You'll likely be asked to solve a business case or discuss how you would approach a real-world business problem. This is your chance to show that you can not only analyze data but also translate your findings into actionable business insights.

Cultural Fit: You may meet with various team members who will assess whether you're a good fit for the company culture. This might include more casual conversations about your interests, work style, and how you handle work-life balance.

5. Waiting for an Offer
Once you've completed the interview stages, the waiting game begins. The company will review your performance across all interviews before making a decision. If you’re successful, you’ll receive an offer. If not, you might still receive feedback that can be valuable for your future interviews.
The data analyst interview process is thorough and can be challenging, but it's designed to ensure that you're the right fit for the role and the company. By understanding each stage and preparing accordingly, you can approach your interviews with confidence. Remember, every interview is a learning experience that brings you one step closer to landing your dream job.
Good luck!

The Typical Data Analyst Interview Process: A Comprehensive Guide

<div class="captioned-image-container"><figure><a class="image-link image2" target="_blank" href="https://www.analystbuilder.com/courses/mysql-interview-crash-course" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd1cbc229-313f-41df-b154-b76e22a47e90_618x412.jpeg 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd1cbc229-313f-41df-b154-b76e22a47e90_618x412.jpeg 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd1cbc229-313f-41df-b154-b76e22a47e90_618x412.jpeg 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd1cbc229-313f-41df-b154-b76e22a47e90_618x412.jpeg 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd1cbc229-313f-41df-b154-b76e22a47e90_618x412.jpeg" width="618" height="412" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/d1cbc229-313f-41df-b154-b76e22a47e90_618x412.jpeg&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:412,&quot;width&quot;:618,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:null,&quot;alt&quot;:&quot;What is a Technical Interview - Technical Interview Questions | iHire&quot;,&quot;title&quot;:null,&quot;type&quot;:null,&quot;href&quot;:&quot;https://www.analystbuilder.com/courses/mysql-interview-crash-course&quot;,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:null,&quot;isProcessing&quot;:false}" class="sizing-normal" alt="What is a Technical Interview - Technical Interview Questions | iHire" title="What is a Technical Interview - Technical Interview Questions | iHire" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd1cbc229-313f-41df-b154-b76e22a47e90_618x412.jpeg 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd1cbc229-313f-41df-b154-b76e22a47e90_618x412.jpeg 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd1cbc229-313f-41df-b154-b76e22a47e90_618x412.jpeg 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd1cbc229-313f-41df-b154-b76e22a47e90_618x412.jpeg 1456w" sizes="100vw" fetchpriority="high"></picture><div></div></div></a></figure></div><h2><strong>Typical Data Analyst Interview Process</strong></h2><p>The data analyst interview process typically involves the following steps:&nbsp;</p><ol><li><p><strong>Recruiter Screening Call</strong>: Your first step will often involve a screening call with a recruiter to get a sense of your experience, interest, and salary expectations, as well as provide you with details about the position.&nbsp;</p></li><li><p><strong>Hiring Manager Interview</strong>: The next call is normally with the hiring manager. They might ask more about your direct experience, as well as why you are interested in the position.&nbsp;</p></li><li><p><strong>Technical Screen</strong>: This part is specific to data analyst roles. The technical interview can involve SQL and Python questions or a take-home test.&nbsp;</p></li><li><p><strong>On-site interview</strong>: The final step tends to focus on your business acumen.</p></li></ol><p>Once you have passed through these core parts of the interview process, you may have to wait for an offer. Regardless of whether you get the job or not, you may get a chance for feedback, which can be helpful for your career.</p><h2><strong>General Data Analyst Interview Questions and Answers&nbsp;&nbsp;</strong></h2><p>These are some of the questions you might encounter early on during the interview process, usually in the HR interview. They are often the data analyst behavioral interview questions used to determine what kind of professional you are and how much you understand about the role and the company:&nbsp;</p><h4>1. Tell me about yourself</h4><p>Despite being a relatively simple question, this one can be hard for many people to answer. Essentially, the interviewer is looking for a relatively concise and focused answer about what&#8217;s brought you to the field of data analytics and what interests you about this role.&nbsp;</p><p>You should focus on why data analytics is meaningful to you, what excites you about this specific role, and what you&#8217;re hoping to gain from it.&nbsp;</p><h4>2. What makes you the best candidate for the job?&nbsp;</h4><p>Although this can be a broad question, remember the interviewer wants to hear about you as a data analyst. So consider your journey with data analysis, what got you interested in the first place, your previous experience, and why you are applying for this role in particular.&nbsp;</p><h4>3. Tell me how you coped with a challenging data analysis project&nbsp;</h4><p>Here, the interviewer is essentially asking how you overcome challenges, giving you a chance to highlight your strengths in action. Make sure to talk about some of your strengths and weaknesses that you're working to improve.&nbsp;</p><p>Be honest about what went wrong or what you found difficult, and try to highlight any skills listed in the job requirements of this role. Again, make sure you give an answer with a positive outcome, showing the lessons/skills you learned to cope with similar challenges in the future.&nbsp;</p><p>The interviewer may instead ask you to talk about a successful project, but your approach should be the same either way. Give a specific example, highlight what went well and what was challenging, and mention the lessons you learned.&nbsp;</p><h4>4. What type of data have you worked with?</h4><p>This question asks you to be as specific as possible. Focus on the size and type of data you have worked with, whether from previous work experience or your own projects and programs. Many hiring managers will be looking to see if you can handle large, complex data.&nbsp;</p><p>You can draw on all kinds of examples here, whether it&#8217;s career-related or something that&#8217;s part of a personal project or online course.&nbsp;</p><h3>Data analyst process interview questions</h3><p>In your day-to-day work as a data analyst, you&#8217;ll spend a lot of time working on various tasks and processes. During the hiring manager interview, you&#8217;ll likely encounter questions about processing, including:</p><h4>5. What is data cleaning, and how do you do it?&nbsp;</h4><p>Data cleaning (also known as data preparation or data cleansing) takes up a large part of your work hours as a data analyst. When you answer this question, you can show the interviewer how you handle the process. You&#8217;ll want to explain how you handle missing data, duplicates, outliers, and more. Be sure to explain why it is important and how you have dealt with it in past projects.&nbsp;</p><h4>6. How do you communicate technical concepts to a non-technical audience?</h4><p>Much of data analysis involves ordering your findings into a narrative and clearly explaining it to both technical and non-technical audiences. This is where your soft skills come in: communication and storytelling. Give examples of how you&#8217;ve drawn insights from data and communicated those to audiences. These might include presentations to shareholders or written communication within your portfolio.&nbsp;</p><h4>7. How would you go about measuring the performance of our company?</h4><p>When an interviewer offers up a question about the company, this is an opportunity to show your research into their work and how you align with them. Consider how your analysis skills can bring insights specific to this company in particular, with their problems and goals in mind.&nbsp;</p><h4>8. How would you estimate&#8230;?</h4><p>They may give you a situational question here, asking how you&#8217;d approach a task from start to finish. This question will test your analytical skills, as well as your ability to think on your feet. You should talk the interviewer through your approach and rely on your knowledge and skills to guide you.&nbsp;</p><h3>Data analyst technical interview questions</h3><h4>9. What data analytics software are you familiar with?</h4><p>This question helps the interviewer assess what level of experience you have and how much training you might need for the role in question.&nbsp;</p><p>You can prepare by including any software listed in the job description that you have worked with, mentioning software solutions and how you have used them for different stages across the data analysis process. Be sure to include relevant terminology to keep on track.&nbsp;</p><p>Software to mention for data analyst roles includes R, Python, Tableau, and Microsoft Excel. Be sure to try some extra <strong><a href="https://www.datacamp.com/tracks/data-analyst-with-r">data analyst training</a></strong> if you&#8217;re uncertain of these.&nbsp;</p><h4>10. What is your statistical knowledge for data analysis?&nbsp;</h4><p>This question is usually asking if you have a basic understanding of statistics and how you have used them in your previous data analysis work.&nbsp;</p><p>If you are entry-level and not familiar with statistical methods, make sure to research the following concepts:&nbsp;</p><ul><li><p>Standard deviation</p></li><li><p>Variance</p></li><li><p>Sample size</p></li><li><p>Mean</p></li></ul><p>If you do have some knowledge, be specific about how statistical analysis ties into business goals.</p><p>There are of course a lot of other questions you could be asked, but these are fantastic questions to study and know how to answer well. You most likely will be asked questions like these in your interviews!</p>

Typical Data Analyst Interview Process
The data analyst interview process typically involves the following steps: 

Recruiter Screening Call: Your first step will often involve a screening call with a recruiter to get a sense of your experience, interest, and salary expectations, as well as provide you with details about the position. 

Hiring Manager Interview: The next call is normally with the hiring manager. They might ask more about your direct experience, as well as why you are interested in the position. 

Technical Screen: This part is specific to data analyst roles. The technical interview can involve SQL and Python questions or a take-home test. 

On-site interview: The final step tends to focus on your business acumen.

Once you have passed through these core parts of the interview process, you may have to wait for an offer. Regardless of whether you get the job or not, you may get a chance for feedback, which can be helpful for your career.
General Data Analyst Interview Questions and Answers  
These are some of the questions you might encounter early on during the interview process, usually in the HR interview. They are often the data analyst behavioral interview questions used to determine what kind of professional you are and how much you understand about the role and the company: 
1. Tell me about yourself
Despite being a relatively simple question, this one can be hard for many people to answer. Essentially, the interviewer is looking for a relatively concise and focused answer about what’s brought you to the field of data analytics and what interests you about this role. 
You should focus on why data analytics is meaningful to you, what excites you about this specific role, and what you’re hoping to gain from it. 
2. What makes you the best candidate for the job? 
Although this can be a broad question, remember the interviewer wants to hear about you as a data analyst. So consider your journey with data analysis, what got you interested in the first place, your previous experience, and why you are applying for this role in particular. 
3. Tell me how you coped with a challenging data analysis project 
Here, the interviewer is essentially asking how you overcome challenges, giving you a chance to highlight your strengths in action. Make sure to talk about some of your strengths and weaknesses that you're working to improve. 
Be honest about what went wrong or what you found difficult, and try to highlight any skills listed in the job requirements of this role. Again, make sure you give an answer with a positive outcome, showing the lessons/skills you learned to cope with similar challenges in the future. 
The interviewer may instead ask you to talk about a successful project, but your approach should be the same either way. Give a specific example, highlight what went well and what was challenging, and mention the lessons you learned. 
4. What type of data have you worked with?
This question asks you to be as specific as possible. Focus on the size and type of data you have worked with, whether from previous work experience or your own projects and programs. Many hiring managers will be looking to see if you can handle large, complex data. 
You can draw on all kinds of examples here, whether it’s career-related or something that’s part of a personal project or online course. 
Data analyst process interview questions
In your day-to-day work as a data analyst, you’ll spend a lot of time working on various tasks and processes. During the hiring manager interview, you’ll likely encounter questions about processing, including:
5. What is data cleaning, and how do you do it? 
Data cleaning (also known as data preparation or data cleansing) takes up a large part of your work hours as a data analyst. When you answer this question, you can show the interviewer how you handle the process. You’ll want to explain how you handle missing data, duplicates, outliers, and more. Be sure to explain why it is important and how you have dealt with it in past projects. 
6. How do you communicate technical concepts to a non-technical audience?
Much of data analysis involves ordering your findings into a narrative and clearly explaining it to both technical and non-technical audiences. This is where your soft skills come in: communication and storytelling. Give examples of how you’ve drawn insights from data and communicated those to audiences. These might include presentations to shareholders or written communication within your portfolio. 
7. How would you go about measuring the performance of our company?
When an interviewer offers up a question about the company, this is an opportunity to show your research into their work and how you align with them. Consider how your analysis skills can bring insights specific to this company in particular, with their problems and goals in mind. 
8. How would you estimate…?
They may give you a situational question here, asking how you’d approach a task from start to finish. This question will test your analytical skills, as well as your ability to think on your feet. You should talk the interviewer through your approach and rely on your knowledge and skills to guide you. 
Data analyst technical interview questions
9. What data analytics software are you familiar with?
This question helps the interviewer assess what level of experience you have and how much training you might need for the role in question. 
You can prepare by including any software listed in the job description that you have worked with, mentioning software solutions and how you have used them for different stages across the data analysis process. Be sure to include relevant terminology to keep on track. 
Software to mention for data analyst roles includes R, Python, Tableau, and Microsoft Excel. Be sure to try some extra data analyst training if you’re uncertain of these. 
10. What is your statistical knowledge for data analysis? 
This question is usually asking if you have a basic understanding of statistics and how you have used them in your previous data analysis work. 
If you are entry-level and not familiar with statistical methods, make sure to research the following concepts: 

Standard deviation

Variance

Sample size

Mean

If you do have some knowledge, be specific about how statistical analysis ties into business goals.
There are of course a lot of other questions you could be asked, but these are fantastic questions to study and know how to answer well. You most likely will be asked questions like these in your interviews!

10 General Data Analyst Interview Questions You'll Be Asked

Recruiter: someone hired by a company to help them hire the best talent for open positions.

<div class="captioned-image-container"><figure><a class="image-link image2 is-viewable-img" target="_blank" href="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1d10df64-9974-4617-adfd-828ddfdee834_700x700.jpeg" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1d10df64-9974-4617-adfd-828ddfdee834_700x700.jpeg 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1d10df64-9974-4617-adfd-828ddfdee834_700x700.jpeg 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1d10df64-9974-4617-adfd-828ddfdee834_700x700.jpeg 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1d10df64-9974-4617-adfd-828ddfdee834_700x700.jpeg 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1d10df64-9974-4617-adfd-828ddfdee834_700x700.jpeg" width="376" height="376" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/1d10df64-9974-4617-adfd-828ddfdee834_700x700.jpeg&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:700,&quot;width&quot;:700,&quot;resizeWidth&quot;:376,&quot;bytes&quot;:null,&quot;alt&quot;:&quot;Working-recruiters&quot;,&quot;title&quot;:null,&quot;type&quot;:null,&quot;href&quot;:null,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:null,&quot;isProcessing&quot;:false}" class="sizing-normal" alt="Working-recruiters" title="Working-recruiters" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1d10df64-9974-4617-adfd-828ddfdee834_700x700.jpeg 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1d10df64-9974-4617-adfd-828ddfdee834_700x700.jpeg 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1d10df64-9974-4617-adfd-828ddfdee834_700x700.jpeg 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1d10df64-9974-4617-adfd-828ddfdee834_700x700.jpeg 1456w" sizes="100vw" fetchpriority="high"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><div class="pencraft pc-reset icon-container restack-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-refresh-cw"><path d="M3 12a9 9 0 0 1 9-9 9.75 9.75 0 0 1 6.74 2.74L21 8"></path><path d="M21 3v5h-5"></path><path d="M21 12a9 9 0 0 1-9 9 9.75 9.75 0 0 1-6.74-2.74L3 16"></path><path d="M8 16H3v5"></path></svg></div><div class="pencraft pc-reset icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></div></div></div></div></a></figure></div><blockquote><p><strong>Recruiter: someone hired by a company to help them hire the best talent for open positions. &nbsp;</strong></p></blockquote><p>Applying to 1,000s of jobs, trying to land referrals, and messaging recruiters on LinkedIn is exhausting. &nbsp;</p><p>But did you know that you can set up your LinkedIn where recruiters reach out to you?&nbsp;</p><p>Well, you can! &nbsp;</p><p>Here are 4 steps to make your LinkedIn recruiter friendly.&nbsp;</p><div><hr></div><p><strong>1. Turn on LinkedIn&#8217;s &#8220;Open to&#8221; feature on your profile&nbsp;</strong></p><p>The &#8220;Open to&#8221; feature is how you tell recruiters, &#8220;Hey! I need a job!&#8221;&nbsp;</p><p>To turn on &#8220;Open to&#8221; on your profile: &nbsp;</p><p>a) Locate and click on the "open to"&nbsp;button below your name on your profile</p><p>b) Select &#8220;Finding a new job&#8221;&nbsp;</p><p>c) Enter the job titles you&#8217;re looking for (e.g. data analyst, operations analyst, etc.)&nbsp;</p><p>d) Enter the location type (remote jobs are very competitive, so if you can work in-person/hybrid, I recommend selecting those as well)&nbsp;</p><p>e) Enter your on-site work locations as well as remote (For remote, enter large cities near you because some companies want you to be able to come in on a rare occasion)&nbsp;</p><p>f) Choose when you can start&nbsp;</p><p>g) Choose which employment types (full-time, contract, etc.) you&#8217;re open to&nbsp;</p><p>h) Choose visibility (If you currently have a job, make sure you select &#8220;Recruiters only&#8221; so your boss doesn&#8217;t see you want to leave!)&nbsp;</p><p>i) Hit save&nbsp;</p><p>Doing all of this will help you show up when a recruiter is searching LinkedIn for available candidates. &nbsp;</p><div><hr></div><p><strong>2. Include keywords throughout your LinkedIn page&nbsp;</strong></p><p>When recruiters are searching for candidates, they will often search for keywords such as SQL, Tableau, Python, data visualization, years of experience, and more.&nbsp;</p><p>So, including these in your profile will increase the chance you show up in their search. &nbsp;</p><p>Where can you add these keywords?&nbsp;</p><p>In your headline, &#8220;about&#8221; section, skills section, work history, education history, and even in posts. &nbsp;&nbsp;</p><p>Although, make sure you don&#8217;t use keywords in a deceptive or &#8220;scammy&#8221; way, because that will only hurt you.&nbsp;</p><div><hr></div><p><strong>3. Add your resume and portfolio to your &#8220;featured&#8221; section&nbsp;</strong></p><p>LinkedIn allows you to showcase whatever you want in the &#8220;featured&#8221; section on your LinkedIn.</p><p>When you&#8217;re a job seeker, you want to make sure it&#8217;s as simple as possible for recruiters to find what they need, so add your resume and portfolio to this section.</p><div><hr></div><p><strong>4. Optimize your LinkedIn</strong>&nbsp;</p><p>Attracting recruiters to your LinkedIn doesn&#8217;t help a lot if your page doesn&#8217;t &#8220;sell&#8221; them when they get there.&nbsp;</p><p>So it is CRUCIAL to optimize every section of your profile to show recruiters how talented you are.&nbsp;</p><p>This step is too large to include in this week&#8217;s email, so tune in next week for how to optimize your LinkedIn to impress recruiters. &nbsp;</p><p>Happy learning, &nbsp;</p><p>Alex from Analyst Builder &nbsp;</p>

Recruiter: someone hired by a company to help them hire the best talent for open positions.  

Applying to 1,000s of jobs, trying to land referrals, and messaging recruiters on LinkedIn is exhausting.  
But did you know that you can set up your LinkedIn where recruiters reach out to you? 
Well, you can!  
Here are 4 steps to make your LinkedIn recruiter friendly. 


1. Turn on LinkedIn’s “Open to” feature on your profile 
The “Open to” feature is how you tell recruiters, “Hey! I need a job!” 
To turn on “Open to” on your profile:  
a) Locate and click on the "open to" button below your name on your profile
b) Select “Finding a new job” 
c) Enter the job titles you’re looking for (e.g. data analyst, operations analyst, etc.) 
d) Enter the location type (remote jobs are very competitive, so if you can work in-person/hybrid, I recommend selecting those as well) 
e) Enter your on-site work locations as well as remote (For remote, enter large cities near you because some companies want you to be able to come in on a rare occasion) 
f) Choose when you can start 
g) Choose which employment types (full-time, contract, etc.) you’re open to 
h) Choose visibility (If you currently have a job, make sure you select “Recruiters only” so your boss doesn’t see you want to leave!) 
i) Hit save 
Doing all of this will help you show up when a recruiter is searching LinkedIn for available candidates.  


2. Include keywords throughout your LinkedIn page 
When recruiters are searching for candidates, they will often search for keywords such as SQL, Tableau, Python, data visualization, years of experience, and more. 
So, including these in your profile will increase the chance you show up in their search.  
Where can you add these keywords? 
In your headline, “about” section, skills section, work history, education history, and even in posts.   
Although, make sure you don’t use keywords in a deceptive or “scammy” way, because that will only hurt you. 


3. Add your resume and portfolio to your “featured” section 
LinkedIn allows you to showcase whatever you want in the “featured” section on your LinkedIn.
When you’re a job seeker, you want to make sure it’s as simple as possible for recruiters to find what they need, so add your resume and portfolio to this section.


4. Optimize your LinkedIn 
Attracting recruiters to your LinkedIn doesn’t help a lot if your page doesn’t “sell” them when they get there. 
So it is CRUCIAL to optimize every section of your profile to show recruiters how talented you are. 
This step is too large to include in this week’s email, so tune in next week for how to optimize your LinkedIn to impress recruiters.  
Happy learning,  
Alex from Analyst Builder

How to Get Recruiters to Reach out to YOU

Posting on LinkedIn is a great way to network, share your expertise, and show recruiters that you&#8217;re passionate and knowledgeable about data analytics.

<div class="captioned-image-container"><figure><a class="image-link image2 is-viewable-img" target="_blank" href="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fad821370-2b35-47b0-b4bc-9af741cd72a8_700x467.jpeg" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fad821370-2b35-47b0-b4bc-9af741cd72a8_700x467.jpeg 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fad821370-2b35-47b0-b4bc-9af741cd72a8_700x467.jpeg 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fad821370-2b35-47b0-b4bc-9af741cd72a8_700x467.jpeg 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fad821370-2b35-47b0-b4bc-9af741cd72a8_700x467.jpeg 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fad821370-2b35-47b0-b4bc-9af741cd72a8_700x467.jpeg" width="560" height="373.6" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/ad821370-2b35-47b0-b4bc-9af741cd72a8_700x467.jpeg&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:467,&quot;width&quot;:700,&quot;resizeWidth&quot;:560,&quot;bytes&quot;:null,&quot;alt&quot;:&quot;LinkedIn logo&quot;,&quot;title&quot;:null,&quot;type&quot;:null,&quot;href&quot;:null,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:null,&quot;isProcessing&quot;:false}" class="sizing-normal" alt="LinkedIn logo" title="LinkedIn logo" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fad821370-2b35-47b0-b4bc-9af741cd72a8_700x467.jpeg 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fad821370-2b35-47b0-b4bc-9af741cd72a8_700x467.jpeg 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fad821370-2b35-47b0-b4bc-9af741cd72a8_700x467.jpeg 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fad821370-2b35-47b0-b4bc-9af741cd72a8_700x467.jpeg 1456w" sizes="100vw" fetchpriority="high"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><div class="pencraft pc-reset icon-container restack-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-refresh-cw"><path d="M3 12a9 9 0 0 1 9-9 9.75 9.75 0 0 1 6.74 2.74L21 8"></path><path d="M21 3v5h-5"></path><path d="M21 12a9 9 0 0 1-9 9 9.75 9.75 0 0 1-6.74-2.74L3 16"></path><path d="M8 16H3v5"></path></svg></div><div class="pencraft pc-reset icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></div></div></div></div></a></figure></div><blockquote><p>Posting on LinkedIn is a great way to network, share your expertise, and show recruiters that you&#8217;re passionate and knowledgeable about data analytics.&nbsp;</p></blockquote><p>Further, recruiters want to know that you&#8217;re a real person (and not a spam account) and that you&#8217;re active enough on LinkedIn to see when they message you.&nbsp;</p><p>Have no idea what to post about?&nbsp;</p><p><strong>Here are 30 LinkedIn post ideas to get you started:</strong><br><br>1. Introduce yourself.<br>2. Share a project and ask for feedback.<br>3. Share your whole portfolio.<br>4. Share a resource that has helped you learn.<br>5. Share a YouTube video that has helped you learn.<br>6. Share a content creator that has helped you learn and what you learned from them.<br>7. Share a struggle and ask for help.<br>8. Share why you want to become a data analyst.<br>9. Share what inspires you during your journey.<br>10. Share something you&#8217;re currently learning for work.<br>11.&nbsp;Post an opinion. (examples: Tableau vs. Power BI, Leading vs. Trailing commas)<br>12. React to recent news in the world of data and tech.<br>13. Create a list. (top 5 resources, 10 favorite Excel formulas, etc.)<br>14. Help someone. (Post a job opening, post )<br>15. Quiz your network. (you can easily create a poll on LinkedIn)<br>16.&nbsp;Share an update on your journey.<br>17.&nbsp;Share a quick tip. (&#8220;This Excel trick will save you 10 minutes!&#8221;)<br>18.&nbsp;Ask an open-ended question to your network. (why did you go into data? What&#8217;s your favorite part about analytics?)<br>19.&nbsp;Be authentic. Not everything is sunshine and rainbows. (Remain professional though &#128522;)<br>20.&nbsp;Recap and summarize what you learned from a training you attended.<br>21.&nbsp;Share a milestone.<br>22.&nbsp;Share an accomplishment like completing a course or a project.<br>23.&nbsp;Share an obstacle in your journey and how you overcame it.<br>24.&nbsp;Share your routine and how you balance your journey with the rest of your life.<br>25.&nbsp;With the above, share a time-management/productivity hack.<br>26.&nbsp;Share a joke or meme. (LinkedIn gets too serious)<br>27.&nbsp;Post your honest opinion on a course you&#8217;ve taken.<br>28.&nbsp;Repost a LinkedIn post that has helped you and add your thoughts.<br>29.&nbsp;Share what tools you&#8217;re learning and why.<br>30.&nbsp;Anything else that&#8217;ll help you connect with your network and make you look good to employers!</p><p>Good Luck on LinkedIn!</p>

Posting on LinkedIn is a great way to network, share your expertise, and show recruiters that you’re passionate and knowledgeable about data analytics. 

Further, recruiters want to know that you’re a real person (and not a spam account) and that you’re active enough on LinkedIn to see when they message you. 
Have no idea what to post about? 
Here are 30 LinkedIn post ideas to get you started:
1. Introduce yourself.
2. Share a project and ask for feedback.
3. Share your whole portfolio.
4. Share a resource that has helped you learn.
5. Share a YouTube video that has helped you learn.
6. Share a content creator that has helped you learn and what you learned from them.
7. Share a struggle and ask for help.
8. Share why you want to become a data analyst.
9. Share what inspires you during your journey.
10. Share something you’re currently learning for work.
11. Post an opinion. (examples: Tableau vs. Power BI, Leading vs. Trailing commas)
12. React to recent news in the world of data and tech.
13. Create a list. (top 5 resources, 10 favorite Excel formulas, etc.)
14. Help someone. (Post a job opening, post )
15. Quiz your network. (you can easily create a poll on LinkedIn)
16. Share an update on your journey.
17. Share a quick tip. (“This Excel trick will save you 10 minutes!”)
18. Ask an open-ended question to your network. (why did you go into data? What’s your favorite part about analytics?)
19. Be authentic. Not everything is sunshine and rainbows. (Remain professional though 😊)
20. Recap and summarize what you learned from a training you attended.
21. Share a milestone.
22. Share an accomplishment like completing a course or a project.
23. Share an obstacle in your journey and how you overcame it.
24. Share your routine and how you balance your journey with the rest of your life.
25. With the above, share a time-management/productivity hack.
26. Share a joke or meme. (LinkedIn gets too serious)
27. Post your honest opinion on a course you’ve taken.
28. Repost a LinkedIn post that has helped you and add your thoughts.
29. Share what tools you’re learning and why.
30. Anything else that’ll help you connect with your network and make you look good to employers!
Good Luck on LinkedIn!

Posting on LinkedIn is a great way to network, share your expertise, and show recruiters that you’re passionate and knowledgeable about data analytics.

30+ LinkedIn Post Ideas to get You Started

Your LinkedIn Profile may land you a job faster than randomly applying! Let's look at the best ways to optimize your profile!

<div class="captioned-image-container"><figure><a class="image-link image2 is-viewable-img" target="_blank" href="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F34524b1b-dc58-457f-80c4-86808adb689a_1276x902.png" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F34524b1b-dc58-457f-80c4-86808adb689a_1276x902.png 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F34524b1b-dc58-457f-80c4-86808adb689a_1276x902.png 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F34524b1b-dc58-457f-80c4-86808adb689a_1276x902.png 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F34524b1b-dc58-457f-80c4-86808adb689a_1276x902.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F34524b1b-dc58-457f-80c4-86808adb689a_1276x902.png" width="616" height="435.44827586206895" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/34524b1b-dc58-457f-80c4-86808adb689a_1276x902.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:902,&quot;width&quot;:1276,&quot;resizeWidth&quot;:616,&quot;bytes&quot;:563985,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:null,&quot;isProcessing&quot;:false}" class="sizing-normal" alt="" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F34524b1b-dc58-457f-80c4-86808adb689a_1276x902.png 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F34524b1b-dc58-457f-80c4-86808adb689a_1276x902.png 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F34524b1b-dc58-457f-80c4-86808adb689a_1276x902.png 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F34524b1b-dc58-457f-80c4-86808adb689a_1276x902.png 1456w" sizes="100vw" fetchpriority="high"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><div class="pencraft pc-reset icon-container restack-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-refresh-cw"><path d="M3 12a9 9 0 0 1 9-9 9.75 9.75 0 0 1 6.74 2.74L21 8"></path><path d="M21 3v5h-5"></path><path d="M21 12a9 9 0 0 1-9 9 9.75 9.75 0 0 1-6.74-2.74L3 16"></path><path d="M8 16H3v5"></path></svg></div><div class="pencraft pc-reset icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></div></div></div></div></a></figure></div><blockquote><p><strong>Your LinkedIn Profile may land you a job faster than randomly applying</strong></p></blockquote><p>When you&#8217;re job searching, you want to give yourself the best possible chance to land a job!</p><p>To do that, you need to have a LinkedIn profile that makes a recruiter say, &#8220;This person is an awesome data analyst. I should hire them.&#8221;&nbsp;</p><p>(assuming you&#8217;re trying to become a data analyst)&nbsp;</p><p>If you want to have a great LinkedIn profile, follow these steps to optimize each section:&nbsp;</p><p>(To use my profile as a reference as you read each step, click on the LinkedIn icon at the bottom of the page.)</p><div><hr></div><p><strong>1. Your profile picture&nbsp;</strong></p><p>This is the FIRST THING someone sees when someone visits your profile, so make sure it is professional and inviting.&nbsp;</p><p>Use a high-quality photo from the chest up wearing professional clothing, and this is your first impression on someone, so I recommend smiling (or at least not looking angry). &nbsp;</p><div><hr></div><p><strong>2. Your headline&nbsp;</strong></p><p>This is the second thing someone sees when they come to your page and the first thing that gives actual information.&nbsp;</p><p>This section should start out with your current job title or the one you want (e.g. &#8220;data analyst&#8221;).</p><p>I don&#8217;t recommend putting &#8220;aspiring data analyst&#8221;.&nbsp;</p><p>You are already a data analyst even if you aren&#8217;t being paid to do it yet,</p><p>After your title, include information about your education (if you went to college), tech stack, and add a little personality if you&#8217;d like.&nbsp;</p><p>Many people separate this information using vertical bars. &nbsp;</p><div><hr></div><p><strong>3. Your header photo&nbsp;</strong></p><p>This isn&#8217;t the most crucial section for your LinkedIn, but everyone sees your header when they click on your profile.&nbsp;</p><p>One way you can create one is by making a free Canva account and using a LinkedIn header template to create yours, and you can include things like your tech stack, experience, or whatever you want people to see.&nbsp;</p><p>Just make sure it looks professional and isn&#8217;t overloaded with info. &nbsp;</p><div><hr></div><p><strong>4. Your &#8220;about&#8221; section&nbsp;</strong></p><p>Once you&#8217;ve wowed someone with the top of your profile, your &#8220;about&#8221; section is where they come to learn more about you.&nbsp;</p><p>In this section, write about your experience, projects, and impacts you&#8217;ve made.</p><p>Your goal is to convince the recruiter that you&#8217;re the best choice for the job. &nbsp;</p><div><hr></div><p><strong>5. Your skills section&nbsp;</strong></p><p>When recruiters search for candidates, they often search by skills that are crucial for job, so thoroughly filling out this section will boost your profile in their results.&nbsp;</p><p>Include all the technical tools you&#8217;ve used and &nbsp;other skills a data analyst recruiter would be interested in.</p><div><hr></div><p><strong>6. Your experience section&nbsp;</strong></p><p>This is where you will add all your previous work experience.</p><p>The key to this section is to highlight TRANSFERABLE skills from your previous jobs that would be helpful in a data analytics position.&nbsp;</p><p>This includes using any kind of analytics software (like Excel), any analysis of data, and soft skills like time management, critical thinking, project management, etc. &nbsp;</p><div><hr></div><p><strong>7. Your education section&nbsp;</strong></p><p>This is where you will add your educational history (especially if you attended college).&nbsp;</p><p>Similar to the experience section, you want to showcase transferrable skills, so highlight relevant coursework and projects. &nbsp;</p><div><hr></div><p><strong>8. Your featured section&nbsp;</strong></p><p>Your featured section at the top of your page is terrific to show recruiters what you actually want them to look at.&nbsp;</p><p>I recommend adding your resume and portfolio to this section. &nbsp;</p><p>If you have any specific projects or LinkedIn posts you want to highlight, feel free to add them too! &nbsp;&nbsp;</p><div><hr></div><p><strong>9. Your posts&nbsp;</strong></p><p>Last, you want to have at least a few posts on your LinkedIn to show recruiters you&#8217;re a real person who does data analytics. &nbsp;</p><p>You can post about your journey, projects you&#8217;ve done, strongest data opinions, etc.&nbsp;</p><p>Doing all these things will help your LinkedIn look fantastic and make you look like the great data analyst you are. &nbsp;</p><div><hr></div><p>You can check out my LinkedIn as inspiration here: </p><p class="button-wrapper" data-attrs="{&quot;url&quot;:&quot;https://www.linkedin.com/in/alex-freberg/&quot;,&quot;text&quot;:&quot;Alex LinkedIn&quot;,&quot;action&quot;:null,&quot;class&quot;:null}" data-component-name="ButtonCreateButton"><a class="button primary" href="https://www.linkedin.com/in/alex-freberg/"><span>Alex LinkedIn</span></a></p>

Your LinkedIn Profile may land you a job faster than randomly applying

When you’re job searching, you want to give yourself the best possible chance to land a job!
To do that, you need to have a LinkedIn profile that makes a recruiter say, “This person is an awesome data analyst. I should hire them.” 
(assuming you’re trying to become a data analyst) 
If you want to have a great LinkedIn profile, follow these steps to optimize each section: 
(To use my profile as a reference as you read each step, click on the LinkedIn icon at the bottom of the page.)


1. Your profile picture 
This is the FIRST THING someone sees when someone visits your profile, so make sure it is professional and inviting. 
Use a high-quality photo from the chest up wearing professional clothing, and this is your first impression on someone, so I recommend smiling (or at least not looking angry).  


2. Your headline 
This is the second thing someone sees when they come to your page and the first thing that gives actual information. 
This section should start out with your current job title or the one you want (e.g. “data analyst”).
I don’t recommend putting “aspiring data analyst”. 
You are already a data analyst even if you aren’t being paid to do it yet,
After your title, include information about your education (if you went to college), tech stack, and add a little personality if you’d like. 
Many people separate this information using vertical bars.  


3. Your header photo 
This isn’t the most crucial section for your LinkedIn, but everyone sees your header when they click on your profile. 
One way you can create one is by making a free Canva account and using a LinkedIn header template to create yours, and you can include things like your tech stack, experience, or whatever you want people to see. 
Just make sure it looks professional and isn’t overloaded with info.  


4. Your “about” section 
Once you’ve wowed someone with the top of your profile, your “about” section is where they come to learn more about you. 
In this section, write about your experience, projects, and impacts you’ve made.
Your goal is to convince the recruiter that you’re the best choice for the job.  


5. Your skills section 
When recruiters search for candidates, they often search by skills that are crucial for job, so thoroughly filling out this section will boost your profile in their results. 
Include all the technical tools you’ve used and  other skills a data analyst recruiter would be interested in.


6. Your experience section 
This is where you will add all your previous work experience.
The key to this section is to highlight TRANSFERABLE skills from your previous jobs that would be helpful in a data analytics position. 
This includes using any kind of analytics software (like Excel), any analysis of data, and soft skills like time management, critical thinking, project management, etc.  


7. Your education section 
This is where you will add your educational history (especially if you attended college). 
Similar to the experience section, you want to showcase transferrable skills, so highlight relevant coursework and projects.  


8. Your featured section 
Your featured section at the top of your page is terrific to show recruiters what you actually want them to look at. 
I recommend adding your resume and portfolio to this section.  
If you have any specific projects or LinkedIn posts you want to highlight, feel free to add them too!   


9. Your posts 
Last, you want to have at least a few posts on your LinkedIn to show recruiters you’re a real person who does data analytics.  
You can post about your journey, projects you’ve done, strongest data opinions, etc. 
Doing all these things will help your LinkedIn look fantastic and make you look like the great data analyst you are.  


You can check out my LinkedIn as inspiration here: 
Alex LinkedIn

How to Optimize your LinkedIn Profile

<div class="captioned-image-container"><figure><a class="image-link image2 is-viewable-img" target="_blank" href="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7efd525a-ff24-4b9a-8a94-a32544fe05a3_800x534.jpeg" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7efd525a-ff24-4b9a-8a94-a32544fe05a3_800x534.jpeg 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7efd525a-ff24-4b9a-8a94-a32544fe05a3_800x534.jpeg 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7efd525a-ff24-4b9a-8a94-a32544fe05a3_800x534.jpeg 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7efd525a-ff24-4b9a-8a94-a32544fe05a3_800x534.jpeg 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7efd525a-ff24-4b9a-8a94-a32544fe05a3_800x534.jpeg" width="478" height="319.065" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/7efd525a-ff24-4b9a-8a94-a32544fe05a3_800x534.jpeg&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:534,&quot;width&quot;:800,&quot;resizeWidth&quot;:478,&quot;bytes&quot;:32464,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/jpeg&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:null,&quot;isProcessing&quot;:false}" class="sizing-normal" alt="" srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7efd525a-ff24-4b9a-8a94-a32544fe05a3_800x534.jpeg 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7efd525a-ff24-4b9a-8a94-a32544fe05a3_800x534.jpeg 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7efd525a-ff24-4b9a-8a94-a32544fe05a3_800x534.jpeg 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7efd525a-ff24-4b9a-8a94-a32544fe05a3_800x534.jpeg 1456w" sizes="100vw" fetchpriority="high"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><div class="pencraft pc-reset icon-container restack-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-refresh-cw"><path d="M3 12a9 9 0 0 1 9-9 9.75 9.75 0 0 1 6.74 2.74L21 8"></path><path d="M21 3v5h-5"></path><path d="M21 12a9 9 0 0 1-9 9 9.75 9.75 0 0 1-6.74-2.74L3 16"></path><path d="M8 16H3v5"></path></svg></div><div class="pencraft pc-reset icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></div></div></div></div></a></figure></div><h1><strong>Recruiter: someone hired by a company to help&nbsp;them hire the best talent for open positions. &nbsp;</strong></h1><p>If you are a job seeker, it is in your best interest to catch the attention of recruiters.&nbsp;</p><p>Like most things, that is easier said than done, so here are 3 steps to help you find recruiters to work with. &nbsp;</p><p><strong>1.</strong> <strong>Make sure your skills, resume, LinkedIn, and portfolio make recruiters want to work with you (rather than scare them away).</strong>&nbsp;</p><p>When you work with a recruiter, they aren&#8217;t just going to take your word that you&#8217;re an expert in SQL, Tableau, Python, etc.&nbsp;</p><p>Instead, they will use your resume, LinkedIn, and portfolio to get a good idea what level of analyst you are.&nbsp;</p><p>So before you reach out to a recruiter, make sure all of these are top notch. &nbsp;</p><div><hr></div><p><strong>2.</strong> <strong>Get in touch with recruiters.</strong></p><p>Here are three ways you can find recruiters to reach out to:&nbsp;</p><p>a) When you get active on LinkedIn (and you better be if you want a job), you see what your connections post as well as the posts they interact with.&nbsp;</p><p>So, if you&#8217;re connected with other data analysts, you&#8217;ll see them interact with posts made by recruiters hiring for a data position, so that&#8217;s one easy way to find and add active recruiters.&nbsp;</p><p>b) When you find a job posting you&#8217;re interested in and meet the basic qualifications for on LinkedIn, click on the company page, then click on &#8220;people&#8221; near the top.&nbsp;</p><p>From here, you can filter the company&#8217;s current employees by position on recruiters.&nbsp;</p><p>Pick 2-3 of these recruiters and DM them using the practices described in step 3 below.&nbsp;</p><p>c) Connect with recruiters through local staffing firms.&nbsp;</p><p>Many companies outsource recruiting to staffing firms, and you can often find local staffing firms to help you out (especially if you live in/near a large city).&nbsp;</p><p>Google &#8220;Data Analyst technical recruiters {YOUR CITY}&#8221; and scroll down to the businesses.&nbsp;</p><p>If you find a local technical staffing firm that likely works with data analysts: sign up, fill out a form, email them, or do whatever their website says to connect with the firm&#8217;s recruiters. &nbsp;</p><div><hr></div><p><strong>3.</strong> <strong>DM recruiters about a specific position you&#8217;re qualified for.</strong>&nbsp;</p><p>Now that you&#8217;ve found recruiters, it&#8217;s time to reach out to them.&nbsp;</p><p>One note before we start messaging them:&nbsp;</p><p>Recruiters are very busy people who get a ton of messages every single day, so you should do your best to stand out, be polite, and understand that most of the recruiters you message won&#8217;t respond.&nbsp;</p><p>That&#8217;s okay. Once you get good at this and send enough messages, you will get responses.&nbsp;</p><p>Here is a short template of what your message should look like:&nbsp;</p><p><code>&#8220;Hi {recruiter name}. My name is {your name}.&nbsp;</code></p><p><code>I saw your company&#8217;s posting for {position w/ link to the posting}, and I believe my experience doing {brief experience description relevant to posting} would make me a great fit for the role. Would you be open to a quick chat to discuss my qualifications?&nbsp;</code></p><p><code>Thanks,&nbsp;</code></p><p><code>{your name}&#8221;&nbsp;</code></p><p>Make sure that you edit the template BEFORE you paste it into LinkedIn, so you don&#8217;t accidentally send the template, and try to add some personalization to the recruiter so it seems less like a template.&nbsp;</p><p>Follow these tips and you will be well on your way to working with recruiters! &nbsp;</p><div class="subscription-widget-wrap-editor" data-attrs="{&quot;url&quot;:&quot;https://analystbuilder.substack.com/subscribe?&quot;,&quot;text&quot;:&quot;Subscribe&quot;,&quot;language&quot;:&quot;en&quot;}" data-component-name="SubscribeWidgetToDOM"><div class="subscription-widget show-subscribe"><div class="preamble"><p class="cta-caption">Thanks for reading Analyst Builder&#8217;s Substack! Subscribe for free to receive new posts and support my work.</p></div><form class="subscription-widget-subscribe"><input type="email" class="email-input" name="email" placeholder="Type your email&#8230;" tabindex="-1"><input type="submit" class="button primary" value="Subscribe"><div class="fake-input-wrapper"><div class="fake-input"></div><div class="fake-button"></div></div></form></div></div>

Recruiter: someone hired by a company to help them hire the best talent for open positions.  
If you are a job seeker, it is in your best interest to catch the attention of recruiters. 
Like most things, that is easier said than done, so here are 3 steps to help you find recruiters to work with.  
1. Make sure your skills, resume, LinkedIn, and portfolio make recruiters want to work with you (rather than scare them away). 
When you work with a recruiter, they aren’t just going to take your word that you’re an expert in SQL, Tableau, Python, etc. 
Instead, they will use your resume, LinkedIn, and portfolio to get a good idea what level of analyst you are. 
So before you reach out to a recruiter, make sure all of these are top notch.  


2. Get in touch with recruiters.
Here are three ways you can find recruiters to reach out to: 
a) When you get active on LinkedIn (and you better be if you want a job), you see what your connections post as well as the posts they interact with. 
So, if you’re connected with other data analysts, you’ll see them interact with posts made by recruiters hiring for a data position, so that’s one easy way to find and add active recruiters. 
b) When you find a job posting you’re interested in and meet the basic qualifications for on LinkedIn, click on the company page, then click on “people” near the top. 
From here, you can filter the company’s current employees by position on recruiters. 
Pick 2-3 of these recruiters and DM them using the practices described in step 3 below. 
c) Connect with recruiters through local staffing firms. 
Many companies outsource recruiting to staffing firms, and you can often find local staffing firms to help you out (especially if you live in/near a large city). 
Google “Data Analyst technical recruiters {YOUR CITY}” and scroll down to the businesses. 
If you find a local technical staffing firm that likely works with data analysts: sign up, fill out a form, email them, or do whatever their website says to connect with the firm’s recruiters.  


3. DM recruiters about a specific position you’re qualified for. 
Now that you’ve found recruiters, it’s time to reach out to them. 
One note before we start messaging them: 
Recruiters are very busy people who get a ton of messages every single day, so you should do your best to stand out, be polite, and understand that most of the recruiters you message won’t respond. 
That’s okay. Once you get good at this and send enough messages, you will get responses. 
Here is a short template of what your message should look like: 
“Hi {recruiter name}. My name is {your name}. 
I saw your company’s posting for {position w/ link to the posting}, and I believe my experience doing {brief experience description relevant to posting} would make me a great fit for the role. Would you be open to a quick chat to discuss my qualifications? 
Thanks, 
{your name}” 
Make sure that you edit the template BEFORE you paste it into LinkedIn, so you don’t accidentally send the template, and try to add some personalization to the recruiter so it seems less like a template. 
Follow these tips and you will be well on your way to working with recruiters!  


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