Maya’s Data Roadmap

Start with Excel and a small project. You don’t have to finish all ten weeks before looking for a first opportunity. Take another week on a topic if you need the practice.

1. Week 1

   Set the foundation

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   What to learn

   Explore data job titles: business, financial, marketing, product, and healthcare analyst. Choose a direction connected to your background and interests.

   What to build

   Look at how a portfolio makes your work visible. Choose a question you would like to answer with data.

   Finding work

   Refresh your LinkedIn photo, banner, headline, About, experience, skills, and education. Make room in Featured for your projects. Explore foot-in-the-door roles and internal opportunities that let you use Excel and gain data experience.

2. Week 2

   Start showing your work

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   What to learn

   Sharpen Excel with filters, formulas, pivot tables, and VLOOKUP. Practice the skills on your checklist using real data.

   What to build

   Build a small sales-data project. Show the question, your analysis, and what you found. Publish a simple first version on LinkedIn.

   Finding work

   Use LinkedIn actively: tell people what you are learning, share your work, and start conversations instead of treating your profile as a static résumé.

3. Week 3

   Make data easy to understand

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   What to learn

   Learn charts, color, and layout in Power BI. Connect a dataset and build a dashboard that answers a question.

   What to build

   Create an education-data project in Power BI, or improve the visual explanation of the project you already started.

   Finding work

   Update your résumé for your target role: relevant keywords, a clear job focus, and quantitative achievements where you have them.

4. Week 4

   Put SQL to work

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   What to learn

   Practice SELECT, FROM, WHERE, GROUP BY, and aggregations. Use queries to answer questions rather than only completing exercises.

   What to build

   Work through a financial-analysis question using SQL. Save your queries and explain what the results mean.

   Finding work

   Give your work a simple home on Carrd or GitHub Pages. Include an introduction and readable project details; link it from LinkedIn and your résumé.

5. Week 5

   Go deeper and start reaching out

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   What to learn

   Practice joins, UNION, CASE WHEN, and CTEs. Use the SQL checklist to identify what still needs practice.

   What to build

   Apply those techniques to a healthcare dataset, or extend an existing project with a more useful question.

   Finding work

   Start cold messages to recruiters, alumni, and peers. Begin a repeatable application routine; Avery’s sample target is 20 relevant applications a week when your schedule allows.

6. Week 6

   Level up Power BI

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   What to learn

   Practice heatmaps, treemaps, and a clear narrative across report pages in Power BI.

   What to build

   Build or extend a sports-data project and publish a short explanation of your findings.

   Finding work

   Keep applying and networking. Include local and hybrid opportunities that fit your goals, and share your project with people who might find it useful.

7. Week 7

   Strengthen the skills you need

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   What to learn

   Review Excel, SQL, and your visualization tool. Work on a gap that is slowing down a real project before adding another tool.

   What to build

   Try a manufacturing-data question, or finish an unfinished project using the tools you already know.

   Finding work

   If you have an interview booked, practice behavioral and technical questions and explaining your projects. Otherwise, keep applications and conversations moving.

   Only if you need itIf the jobs you want ask for Python, try variables, loops, functions, pandas, and seaborn. Otherwise, leave it for later. You don’t need it just to start applying.

8. Week 8

   Connect your work to an industry

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   What to learn

   Practice explaining findings accurately. Explore correlation and regression when they are relevant to the question you are answering.

   What to build

   Use an HR dataset or a dataset from your target industry. Focus on a useful question and a clear recommendation.

   Finding work

   Keep engaging on LinkedIn with comments and posts. Talk about the kind of role you want and the work you can show.

   Only if you need itAvery’s broader curriculum includes R, p-values, and regression. Explore these only when they support your target role; keep using your core tools otherwise.

9. Week 9

   Polish your proof and widen the search

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   What to learn

   Review the requirements in your target job descriptions. Close a repeated gap instead of collecting tools just in case.

   What to build

   Make your best projects easy to find and read. Add clear screenshots, a short write-up or video, and working links.

   Finding work

   Re-share projects and follow up with contacts. Avery’s broader plan increases to 40 applications a week; increase only if you can keep them relevant and maintain your networking.

   Only if you need itExplore another BI tool or SAS only if the roles you want call for it. You do not need all of them to have a useful portfolio.

10. Week 10

    Finish your capstone and keep going

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    What to learn

    Bring your core tools together to answer a question in the industry you want to work in.

    What to build

    Finish your capstone, or improve your best existing project. Explain the question, your analysis, what you found, and what you’d recommend. Put it in your portfolio.

    Finding work

    Keep applying, posting, and networking. Review which efforts lead to conversations, improve your approach, and prepare for interviews as they arrive.

Keep applying and talking to people as you go. If you get an interview, make time to prepare for it right away. The plan gives you things to work on, but it can’t promise when you’ll get hired.