Free Learning Hub · Updated April 2026
Your Complete
Data Analyst
Learning Path
In brief: Follow a suggested learning sequence for SQL, Python, dashboards and case studies. Adapt the pace to your experience and use completed exercises to assess progress.
Explore SQL tutorials, Python guides, case-study frameworks, and an eight-week practice schedule. Adjust the pace to your starting point and available time.
Learning Tracks
Choose your learning track
Three focused tracks — each with structured content, interview-style practice questions and a clear path to preparing for interviews.
Most Important
SQL for Data Analysts
Practise querying relational data
SELECT, WHERE, ORDER BYEasy
GROUP BY & AggregationsEasy
All JOIN TypesMedium
Subqueries & CTEsMedium
Window FunctionsHard
Interview readiness8 topics covered
Start SQL Learning →
High Demand
Python for Analytics
Pandas, NumPy, Matplotlib & more
Python Basics & SetupEasy
Pandas — Complete GuideMedium
NumPy & StatisticsMedium
Matplotlib & SeabornEasy
End-to-End ProjectsHard
Interview readinessPython topics and exercises
Start Python Learning →
Interview Winning
Business Case Studies
Frameworks, metrics & A/B testing
Case Study FrameworksMedium
Metric Design & KPIsMedium
A/B Testing ConceptsHard
Root Cause AnalysisHard
Funnel & Cohort AnalysisMedium
Interview readiness6 modules covered
Start Case Studies →
Structured Roadmap
A suggested 8-week
learning roadmap
Use this suggested sequence to build skills step by step. Hiring outcomes depend on your experience, the role, and the employer.
Week 01 – 02
14 days
SQL Foundations
Master SELECT, WHERE, GROUP BY, HAVING and all JOIN types. Solve 20 practice problems daily.
Week 03 – 04
14 days
Advanced SQL
Window functions, CTEs, subqueries. The topics that separate junior from senior analysts in interviews.
Week 05 – 06
14 days
Python & Pandas
Python basics, Pandas DataFrames, groupby, merge. Complete one end-to-end EDA project using real data.
Week 07 – 08
14 days
Mock Interviews
3 full mock interview sessions, case study prep, resume polish, HR round practice. Interview ready.
Week by Week
Detailed week-by-week breakdown
Exactly what to study, in what order, and what outcome to expect by the end of each week.
📖
Week 1
SQL Basics & Fundamentals
Start with SELECT, FROM, WHERE, ORDER BY and LIMIT. Learn how databases work, what tables are, and how to read and write simple queries. Aim for 10 practice queries per day.
SELECT *WHERE conditionsORDER BYDISTINCTNULL handling
✓ By end of week: Write any basic SELECT query confidently. Filter data by any condition.
📊
Week 2
GROUP BY, Aggregations & JOINs
Master COUNT, SUM, AVG, MIN, MAX with GROUP BY and HAVING. Then learn all 4 JOIN types — INNER, LEFT, RIGHT, FULL OUTER. The anti-join pattern (LEFT JOIN + IS NULL) is essential.
COUNT/SUM/AVGGROUP BYHAVINGINNER JOINLEFT JOIN
✓ By end of week: Answer “revenue per region” and “customers with no orders” questions in SQL.
🧩
Week 3
Subqueries & CTEs
Learn to nest queries inside queries, and write clean multi-step logic using CTEs (WITH clauses). Practice rewriting messy nested subqueries into readable CTEs — interviewers notice this.
SubqueriesWITH clauseMulti-CTE chainsCorrelated subqueries
✓ By end of week: Write any multi-step SQL query cleanly using CTEs. No more deeply nested queries.
🏆
Week 4
Window Functions (Advanced SQL)
An advanced SQL topic. Master RANK, DENSE_RANK, ROW_NUMBER, LAG, LEAD, SUM OVER, and rolling averages. Solve 15+ window function interview questions for practice.
RANK()LAG/LEADPARTITION BYRunning totalsRolling avg
✓ By end of week: Explain and test window-function queries on small datasets.
🐍
Week 5
Python Basics & Pandas
Set up Python and Jupyter. Learn Pandas DataFrames — read_csv, head, info, describe, filtering, groupby, merge, pivot_table. Complete a sales analysis project using a real dataset.
DataFramesgroupbymergepivot_tableapply + lambda
✓ By end of week: Load, clean and analyse any CSV dataset using Pandas. Create grouped summaries.
📈
Week 6
Visualisation & EDA Project
Matplotlib and Seaborn for charts. Complete an end-to-end EDA project — load, clean, analyse, visualise and present insights. Upload to GitHub for your portfolio.
MatplotlibSeabornEDA workflowGitHub portfolio
✓ By end of week: Portfolio project live on GitHub with charts, insights and a business summary.
📋
Week 7
Case Studies & Business Metrics
Learn the 5 case study frameworks. Practice metric design, A/B test interpretation, funnel analysis and root cause diagnosis. Prepare answers for the 10 most common analyst case questions.
Metric designA/B testingRoot causeFunnel analysis
✓ By end of week: Confidently answer “DAU dropped 20%, why?” and “design metrics for X feature” questions.
🎯
Week 8
Mock Interviews + Final Polish
3 full mock interview sessions covering all rounds. Resume review, LinkedIn optimisation, behavioral prep using STAR framework. Fix any gaps from mock feedback. You’re interview ready.
Mock interviews x3Resume polishSTAR frameworkOffer ready
🎉 Interview ready — apply to target companies with full confidence.
Test Yourself
Quick knowledge check
Answer 5 real interview-style questions. See where you stand right now.
Free Resources
Everything included — 100% free
No paywalls. No email required. The published learning guides are free to read.
SQL
SQL Hub — Complete Guide
SELECT to window functions. Full theory, examples, live compiler.
8 topics · 50+ examples
Interview Qs
SQL Practice Questions
Google, Amazon, Flipkart, Swiggy. Easy to Hard with full answers.
Practice questions · Full solutions
Python
Python Learning Hub
20 pages from beginner to advanced. Pandas, NumPy, Seaborn.
20 pages · Beginner to advanced
Case Studies
Business Case Study Guide
Frameworks, metrics, A/B tests and 20+ worked examples.
6 frameworks · 20+ examples
Dashboards
Dashboards & KPI Design
How to design metrics, KPIs, funnels and A/B test frameworks.
KPIs · Funnels · A/B Testing
Resume
Resume & HR Round Prep
STAR framework, project storytelling, salary negotiation tips.
HR prep · Resume review guide
Projects
10 Portfolio Projects
Real datasets, GitHub-ready projects to impress every recruiter.
10 projects · Real datasets
Career
Salary Offer Comparison in USD
Hypothetical offers and a checklist for comparing compensation.
Compare offer components
Common Questions
Questions before you start?
I have zero coding experience. Can I still learn data analytics?
You can start with no coding experience. Begin with small examples, check each result, and adjust the pace to your starting point.
How long will it realistically take to become interview-ready?
Preparation time varies. Use completed exercises, explanations you can give without notes, and feedback from practice sessions to assess progress.
Is all this content really free? What’s the catch?
All the written content, SQL guides, Python tutorials, practice questions and the SQL compiler are completely free — no account, no email, no credit card. We make money through paid 1-on-1 mentor sessions for candidates who want live mock interviews and personalised feedback. The free content alone is enough to prepare — the paid sessions just accelerate your progress.
Should I learn SQL or Python first?
Start with the tools listed in your target job descriptions. SQL supports database work; Python supports data manipulation and automation. Add a visualisation tool when the role calls for it.
What salary can I expect as a data analyst in India in 2026?
Compensation varies by role, employer, location, and experience. Compare current advertised ranges and distinguish fixed pay from bonuses and equity. No particular salary or premium is guaranteed.
Do I need a computer science degree to become a data analyst?
No. Many top companies — including startups and unicorns — hire analysts based on demonstrated skills, not degrees. What matters is your ability to write SQL, analyse data in Python, communicate insights clearly, and tackle business problems. A strong portfolio (2–3 solid projects on GitHub), good SQL and Python scores, and interview preparation matter far more than your degree.
Ready to start your
data analyst journey?
Start with the topic that fits your current skills, then use the exercises to identify what to practise next.
Documentation and next steps
Check the relevant documentation for your software version and database dialect. These are practice resources, not verified employer question banks.
Find a related learning guide · About the author · Suggest a correction