Your Step-by-Step Guide to Becoming a Data Analyst in 60 Days

In 60 days of steady practice, one to two hours a day, you can learn enough Excel, SQL, Power BI and basic Python to apply for entry-level data analyst roles. You won't be an expert, but you can be job-ready if you spend most of the time working on real datasets and finish with two portfolio projects you can explain. The order that works best for most beginners is Excel first, then SQL, then Power BI, then Python, then portfolio and interview preparation.

"Pro in 60 days" is a big promise, and I'd rather be straight with you: it depends on your starting point and on whether you actually practise every day. This is the plan I give students in our Anand data analysis batches who want to self-study alongside class.

How should you study for 60 days?

• Work every day for one to two hours rather than one long session a week. Short daily practice sticks better.
• Spend at least half your time doing, not watching. Follow along with your own data.
• Keep a short learning log each evening: what you did, what confused you, one thing to try tomorrow.
• Use one sample business dataset throughout (sales, HR or inventory), so each tool builds on the last.

Days 1 to 12: Excel for data analysis

Excel is still the tool most analysts touch daily.

• Clean data structure: one header row, one record per row, Excel Tables.
• Formulas: SUMIFS, COUNTIFS, IF, XLOOKUP, TEXT and date functions.
• PivotTables and PivotCharts for summarising.
• Power Query for importing and cleaning data without manual copy-paste. This skill carries straight into Power BI.
• Simple charts that answer one question each.

Goal by day 12: build a one-page Excel summary of your dataset with a pivot table and two charts.

Days 13 to 25: SQL

SQL is how you get data out of databases, and it's tested in most analyst interviews.

• SELECT, WHERE, ORDER BY.
• GROUP BY with SUM, COUNT and AVG, and HAVING.
• JOINs (inner and left, and why rows go missing or get duplicated).
• CASE expressions, subqueries and common table expressions (CTEs).
• Window functions such as ROW_NUMBER and running totals, once the basics are solid.

Install SQL Server Express or PostgreSQL and load your dataset. Our post on why SQL matters has a starter query.

Goal by day 25: answer ten business questions about your dataset in SQL.

Days 26 to 40: Power BI

• Import data with Power Query and shape it.
• Build a star schema: fact table, dimension tables, a date table, and one-to-many relationships.
• Write DAX measures: totals, CALCULATE, percentage of total, year-to-date and growth versus last year. See why DAX matters.
• Design a report with a clear title per page, slicers and a few visuals that each answer a question.
• Publish to the Power BI service and learn how refresh and sharing work.

Goal by day 40: a three-page Power BI report on your dataset. If you want to formalise this, Microsoft's PL-300 exam (Power BI Data Analyst Associate) is the matching certification.

Days 41 to 50: Python basics for analysts

You don't need to become a software developer. Focus on:

• Python basics: variables, lists, dictionaries, loops and functions.
• pandas: reading CSV and Excel files, filtering, grouping, merging and handling missing values.
• Matplotlib or Seaborn for quick charts.
• Running everything in Jupyter notebooks.

Goal by day 50: repeat one of your Excel or SQL analyses in pandas and compare the results.

Days 51 to 60: portfolio and job preparation

• Days 51 to 55: Finish two portfolio projects. Each needs a short write-up covering the business question, the data, the method, three findings and one recommendation. Put them on GitHub or a simple portfolio page, and publish the Power BI report if the data can be public.
• Days 56 to 58: Interview practice. Write SQL queries against a clock, explain a DAX measure out loud, and practise walking through a project in two minutes.
• Days 59 to 60: Update your CV and LinkedIn to list projects and tools, not just course names. Apply for data analyst, MIS analyst and junior BI roles.

What if you fall behind?

Most people do, and that's fine. Stretch the plan to 90 days rather than skipping SQL or the portfolio, which are the parts employers check most. If you're stuck on the same thing for more than a day, ask someone. A mentor or class saves a lot of time here.

TechNet Consultancy runs this path as a guided data analysis course with business datasets and feedback, plus focused SQL, Power BI and Python training. For the bigger picture, read data analysis careers.

FAQ

Can I become a data analyst in 60 days?

You can become ready for entry-level roles in about 60 days if you practise one to two hours daily and finish real projects. Many people take closer to three to six months, depending on their background.

Should I learn Python or SQL first?

SQL first for most analyst roles. It's used daily and tested in interviews. Python is valuable but can come after Excel, SQL and Power BI.

Do I need a degree in statistics?

No. Basic statistics such as averages, distributions and percentages are enough for most entry-level analyst work. Deeper statistics matter more for data science roles.

What should a data analyst portfolio include?

Two or three projects, each with a clear business question, the data source, your method, key findings and a recommendation, plus a link to the report or code.

Is Power BI or Tableau better to learn?

Both are used. Power BI is very common in Indian companies that use Microsoft 365 and is a good first choice. The concepts carry over to Tableau.