Finance → first data role · 7 hours/week

Created

Priya’s Data Roadmap

You’re the Contender.

You’re getting interviews, which is good. Something is working. Keep applying and talking to people, and use what happens in each interview to prepare for the next one.

Your report card

Your starting point, based on your answers.

SkillsStart here

63 / 100

Portfolio

85 / 100

Job readiness

90 / 100

Your scores come from your answers. They don’t compare you with other people. Job readiness covers your LinkedIn, résumé, and interviews.

What you’re working toward

Keep generating relevant interviews and learn from each conversation.

You have a record of the roles you applied for, the interview stages you reached, and any feedback. Use that evidence to decide what to improve next.

Your week-by-week roadmap

7 hours / week

Keep the applications and networking that are generating interviews. Use the learning weeks only for gaps you discover, and prepare for each interview as soon as it is booked.

Take the time you need. This is a ten-week outline, but a week’s work can take longer. Skip things you already know. Pick projects that interest you, and finish one before piling on more.Your chosen goal: December 15, 2026.
  1. Week 1

    Set the foundation

    What to learn
    Explore financial analyst roles alongside data analyst roles. Compare their requirements with your finance/accounting background and existing Excel, SQL, and Power BI experience.
    What to build
    Look at how a portfolio makes your work visible. Choose a question you would like to answer with data.
    Finding work
    Prepare for any booked interviews now: practice behavioral and technical questions and explaining your work. Track interview stages and feedback while keeping your job search active.
  2. Week 2

    Start showing your work

    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

    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

    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

    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

    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

    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

    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

    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

    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.

Your first three steps

  1. List roles that connect your current experience with analytics.
  2. Try another job website and find a relevant opening.
  3. Follow up with a contact and record what you learn from your next interview.

What to leave for later. Do not assume interviews mean nerves are the problem. Use the actual stage and feedback to decide what needs attention.

Your skills checklist

Avery’s 51 skills, with your focus first.

0 / 51 checked

Focus on Excel, SQL, and Power BI. Choose one visualization tool. Keep Python for later unless the roles you want specifically require it.

Check a skill when you can use it in your own work. These are your checkmarks, not an automatic assessment of mastery.

ExcelYour focus0/15
SQLYour focus0/16
Tableau / Power BIYour focus0/8

Practice these in Power BI. This is one shared set of eight skills; you do not need to learn both tools.

PythonFor later / when needed0/12

Checkmarks save in this browser for this roadmap. They do not change your report-card scores.

Help people find your work

Your portfolio
  • Create one link that introduces you and your projects.
  • Give each project its own detail page, with the question, approach, and findings.
  • Use a readable write-up or short video, with images and text.
  • Link to code where it helps; a recruiter should be able to understand your work without reading the repository.
Your résumé and LinkedIn
  • Use a single-column résumé in Word or PDF, without tables or images.
  • Use relevant keywords and describe achievements with numbers where you have them.
  • Add a clear LinkedIn profile photo, a relevant headline, a cover photo, and a complete About section.
  • Keep experience, skills, and education up to date, and make your portfolio easy to find.
Your network and applications
  • List family, friends, neighbors, people from the pickup line or dog park. Start with people you already see.
  • Tell them about your data journey and the work you want to do.
  • Reach out to hiring managers and recruiters. Send a thoughtful cold message or arrange a coffee chat.
  • Try different job websites and include conversations alongside applications.

People who made the switch

Loan officer → data analyst

Ryan Ponder

With no degree, he shadowed the data teams, networked internally, and moved into a data role at his own company.

Salesman → analytics at Dentsu

Tim Beecher

A former locksmith and cold-caller who landed a remote analytics job through a referral and a hackathon project.

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Data Career JumpstartBased on what you told us.

Fictional example for Priya. Your roadmap will be based on your answers.