Learn data analytics in this order: problem framing and spreadsheets, SQL, Python with pandas, statistics and A/B testing, one BI tool, then AI-assisted analysis. Employers screen for SQL first, so build your first project in SQL with a one-page dashboard.
- 1Spreadsheetsformulas, pivots
- 2SQLjoins, windows
- 3Pythonpandas, plots
- 4Statisticstests, experiments
- 5BI toolPower BI, Tableau
- 6AI-assistedverify everything
Learning path: free resources in order
| Stage | Free resource | Industry tool you will meet |
|---|---|---|
| 1. Problem framing and spreadsheets | Storytelling with Data (blog and resources) · Google Sheets or Excel help centres | Excel, Google Sheets |
| 2. SQL | SQLBolt · Mode SQL tutorial · PostgreSQL tutorial | PostgreSQL, MySQL, BigQuery, Snowflake |
| 3. Python for data | Python tutorial · pandas: 10 minutes | Python, pandas, Jupyter |
| 4. Statistics and A/B testing | Khan Academy statistics · Trustworthy Online Controlled Experiments · statsmodels | statsmodels, experimentation platforms |
| 5. BI tools | Power BI documentation (Microsoft Learn) · Tableau training | Power BI, Tableau, Looker |
| 6. Practice data | Kaggle datasets · UCI Machine Learning Repository · data.gov.in | Real datasets for portfolio work |
| 7. AI-assisted analysis | Hugging Face LLM course · LangChain docs | LLM APIs, text-to-SQL, RAG |
A realistic 12 to 24 week plan
| Weeks | Focus | Output you can show |
|---|---|---|
| Weeks 1 to 2 | Spreadsheets and framing | Five business questions answered in Sheets with pivots |
| Weeks 3 to 6 | SQL | Joins, window functions and cohorts on a real dataset |
| Weeks 7 to 9 | Python and statistics | One EDA notebook and one A/B test readout |
| Weeks 10 to 12 | BI and portfolio | A published dashboard with a one-page recommendation |
Roles in the industry
| Role | What the work looks like | Core tools |
|---|---|---|
| Data analyst | Answer business questions with queries and dashboards | SQL, Excel, Power BI/Tableau |
| Business analyst | Translate business needs into metrics and requirements | Excel, SQL, BI tools |
| Analytics engineer | Build clean, tested data models for analysts | SQL, dbt, a cloud warehouse |
| Product / growth analyst | Funnels, retention, experiments | SQL, Python, experimentation tools |
Mistakes to avoid
- Averaging averages, or comparing a partial month with a full month.
- Joining on a non-unique key and silently multiplying rows; always check row counts before and after a join.
- Presenting a correlation as the cause of a change.
- Pasting private data into a public AI tool.
Build as you learn
Data analytics projects
SQL, cohorts, A/B tests and dashboards on real datasets.
PM portfolio projects
Teardowns, funnel audits, AI feature specs and experiments.
Free tools for this field
Free data analytics tools
SQL engines, notebooks and dashboards you can use for free.
Free AI tools for builders
Run, build and test AI projects without paying.
Frequently asked questions
What should I learn first for data analytics?
Spreadsheets and SQL. Most analyst interviews test SQL before anything else.
Is Python required for a data analyst job?
Not always. SQL, a BI tool and statistics get many analyst roles; Python widens your options and is required for analytics engineering.
Power BI or Tableau?
Pick one and finish a dashboard end to end. Power BI is common in Indian enterprises; Tableau Public is free for a public portfolio.
How many projects do I need?
Three: a SQL business analysis, a cohort or segmentation project and an A/B test readout, each with a recommendation.
Learn this with mentors
Data and business analytics programmes with admissions open: live classes, mentors and project work.
