100 interactive topics covering SQL, Excel, Python, Power BI, and everything you need to land your first data analyst role.
Learn what data analytics is, the 4 types of analytics, the full data pipeline, career paths, and tools — with interactive content. Beginner-friendly guide to start your data analytics journey.
Learn about qualitative vs quantitative data, structured vs unstructured data, and common data types used in analytics. Complete guide with examples.
Master essential Excel functions for data analysis: VLOOKUP, SUMIF, Pivot Tables, data cleaning, and more. Complete hands-on guide for analysts.
Master SQL fundamentals for data analysis: SELECT, WHERE, ORDER BY, LIMIT, and database basics. Hands-on tutorial with real examples.
Master SQL JOINs (INNER, LEFT, RIGHT), GROUP BY, HAVING, aggregate functions, and subqueries. Advanced SQL tutorial with real examples.
Learn Python fundamentals for data analysis: variables, data types, lists, dictionaries, loops, and functions. Hands-on tutorial for analysts.
Master Pandas for data analysis: DataFrames, filtering, groupby, merging, data cleaning. Complete hands-on guide with real examples for analysts.
Master data visualization principles: choose right charts, design dashboards, color theory, and visualization best practices for data analysts.
Learn to build interactive dashboards with Power BI and Tableau. DAX formulas, calculated fields, filters, and dashboard best practices.
Learn essential statistics for data analysis: mean, median, standard deviation, correlation, hypothesis testing, and statistical significance.
Master A/B testing for data-driven decisions: experiment design, sample size, statistical significance, and common pitfalls. Complete practical guide.
Complete data analytics capstone project: data collection, cleaning, analysis, visualization, and insights. Real-world e-commerce analysis walkthrough.
Master advanced Excel functions like VLOOKUP, INDEX-MATCH, SUMIFS, and array formulas. Complete guide with real examples and quizzes for data analysts.
Master SQL window functions like ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, and SUM OVER. Complete guide with e-commerce and analytics examples.
Master Pandas groupby() for data aggregation and merge() for joining DataFrames. Complete guide with real examples from Indian e-commerce companies.
Learn SQL from scratch. Understand databases, tables, and write your first queries. The single most important skill for every data analyst.
Master SQL SELECT, WHERE, and ORDER BY with real examples. Learn filtering, comparison operators, logical operators, and sorting — with an interactive quiz.
Master SQL JOINs with visual diagrams and real examples. Learn INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN and when to use each.
Master SQL GROUP BY with COUNT, SUM, AVG, MIN, MAX. Learn HAVING vs WHERE and how to summarize millions of rows into meaningful business insights.
Learn SQL subqueries and Common Table Expressions (CTEs). Understand inline subqueries, correlated subqueries, and when to use CTEs for cleaner queries.
Master SQL window functions including ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, and running totals. The most powerful tool in the intermediate SQL analyst's toolkit.
The 50 most common SQL interview questions for data analyst roles with detailed answers. From basic SELECT to window functions — exactly what Indian tech companies test.
Complete SQL reference guide with all essential commands. SELECT, JOIN, GROUP BY, window functions, CTEs, and more. The only SQL cheat sheet data analysts actually use.
MySQL vs PostgreSQL vs BigQuery compared for data analysts. Learn which SQL dialect companies use in India, performance differences, and which to learn first.
Learn how to write faster SQL queries. Indexing, query optimization, avoiding SELECT *, JOIN order, and EXPLAIN plans explained for data analysts.
Learn Python for data analysis from scratch. Master Pandas, NumPy, data cleaning, and visualization. Real examples with Indian datasets — perfect for analysts.
Master Pandas DataFrames from scratch. Learn indexing, filtering, selecting columns, adding rows, and essential operations with real Indian business examples.
Master data cleaning in Pandas. Handle missing values, remove duplicates, fix data types, detect outliers, and transform messy data into analysis-ready datasets.
Master Pandas groupby for aggregations, merge/join for combining datasets, and pivot tables for reshaping data. Essential techniques for every data analyst.
Learn NumPy for data analysis. Master arrays, vectorized operations, statistical functions, and numerical computations essential for Python analysts.
Matplotlib vs Seaborn comparison for data analysts. Learn when to use each library, their strengths, and how to create charts with real examples.
Complete Python data analysis project using real Zomato restaurant data. Load, clean, analyze, and visualize restaurant ratings and trends with Pandas.
Master exploratory data analysis with Python. Learn the systematic approach to understanding data, finding patterns, and generating insights before modeling.
Master the fundamental principles of effective data visualization. Learn when to use each chart type, avoid common mistakes, and create visuals that communicate insights clearly.
Interactive decision tree to help you choose the perfect chart type for your data. Answer simple questions about your data story and get instant chart recommendations.
Complete Power BI tutorial for beginners. Learn to connect data, create visuals, build interactive dashboards, and share reports. Step-by-step with examples.
Power BI, Tableau, and Looker compared for data analysts. Which BI tool do Indian companies use? Pricing, learning curve, job demand, and feature comparison.
Build a complete Power BI sales dashboard from scratch. Load data, create DAX measures (YoY growth, MTD, YTD), build interactive visuals, and deploy. Full code included.
Complete Tableau Public tutorial for beginners. Learn to connect data, create stunning visualizations, build interactive dashboards, and publish your portfolio.
Complete Looker Studio (formerly Google Data Studio) tutorial. Connect Google Analytics, Sheets, and BigQuery. Build interactive dashboards for free.
Master statistics for data analysis. Learn descriptive vs inferential statistics, sampling, distributions, hypothesis testing, and how analysts use statistics in real projects.
Master mean, median, and mode with real examples. Learn when to use each measure of central tendency, how outliers affect them, and avoid common mistakes in data analysis.
Master standard deviation and variance with real examples. Learn how to measure data spread, interpret SD, and use it for quality control, risk analysis, and anomaly detection.
Master correlation vs causation with real examples. Learn why correlation doesn't imply causation, confounding variables, and how to prove causal relationships with experiments.
Master p-values with real examples. Learn what p < 0.05 means, how to interpret p-values in A/B tests, common mistakes, and when results are statistically significant.
Master the normal distribution (Gaussian bell curve) with real examples. Learn the 68-95-99.7 rule, Z-scores, standard normal distribution, and when data is normally distributed.
Master A/B testing from planning to analysis. Learn hypothesis setup, sample size calculation, statistical significance, common pitfalls, and how to run experiments like Flipkart, Swiggy, Google.
Free A/B test sample size calculator. Calculate required users for statistically significant experiments. Input baseline rate, minimum detectable effect, significance level, and power.
Master confidence intervals with real examples. Learn what 95% CI means, how to calculate and interpret confidence intervals, margin of error, and use in A/B tests.
Free statistical significance calculator for A/B tests. Calculate p-value, confidence intervals, and determine if your results are statistically significant. Two-proportion z-test and chi-square test.
Understand data warehouses, how they differ from databases, and why they're essential for analytics. Learn with real-world examples from Flipkart, Swiggy, and BigQuery.
Understand the difference between ETL and ELT data pipelines. Learn when to transform data before loading and when to load raw data first with real-world examples.
Learn dbt (data build tool) — the SQL transformation framework that turns analysts into data engineers. Understand models, tests, and documentation with real examples.
Learn Google BigQuery from scratch. Understand serverless architecture, how to query public datasets, optimize costs, and use BigQuery for analytics at scale.
Learn Apache Spark for big data processing. Understand RDDs, DataFrames, PySpark, and when analysts need Spark vs SQL warehouses like BigQuery.
Master dimensional modeling for data warehouses. Learn star schema, snowflake schema, fact tables, dimension tables with real examples from Flipkart and Swiggy.
Understand the critical differences between data lakes and data warehouses. Learn when to use each with real examples from Flipkart, Netflix, and Indian startups.
Master cohort analysis to track user retention, lifetime value, and behavior patterns over time. Learn SQL queries, Python implementation, and real examples from Flipkart, PhonePe.
Master funnel analysis to identify drop-off points and optimize conversion. Learn SQL queries, Python visualization, and real examples from Swiggy, Flipkart, PhonePe checkout funnels.
Master RFM analysis to segment customers by recency, frequency, and monetary value. Learn SQL queries, scoring, and targeting strategies with real examples from Zomato, Myntra.
Master customer lifetime value (LTV) calculation with SQL, Python, and cohort-based methods. Learn LTV/CAC ratio, retention curves, and real examples from Swiggy, Netflix, PhonePe.
Free LTV calculator to estimate customer lifetime value. Input average order value, purchase frequency, lifespan, and margin to calculate LTV and LTV/CAC ratio with real-time results.
Free churn rate calculator to measure customer retention. Calculate monthly and annual churn rate, retention rate, customer lifespan, and churn impact on revenue with real-time results.
Free CAC calculator to measure customer acquisition cost. Input marketing spend, sales costs, and new customers to calculate CAC, LTV/CAC ratio, and payback period with benchmarks.
Master North Star Metric framework to align teams around one growth metric. Learn to choose, measure, and optimize NSM with real examples from Swiggy, Spotify, Airbnb, and Netflix.
Master KPI dashboard design with best practices, metrics categorization, and real examples. Learn what KPIs to track for product, marketing, sales, and operations dashboards.
Learn how Flipkart uses cohort analysis, recommendation systems, A/B testing, and predictive analytics to optimize customer experience, inventory, and logistics at scale.
Learn how Swiggy uses geospatial analytics, ML forecasting, dynamic pricing, and real-time dashboards to optimize delivery times, reduce costs, and predict demand across 600+ cities.
Learn how Netflix uses collaborative filtering, content-based filtering, and A/B testing to generate personalized recommendations that drive 80% of viewing. Includes Python examples.
Learn how Amazon uses recommendation engines, demand forecasting, dynamic pricing, and supply chain analytics to optimize customer experience and operational efficiency at massive scale.
Learn how Zomato uses analytics for restaurant recommendations, delivery optimization, customer retention, and fraud detection across food delivery and dining operations.
Learn how D2C brands use analytics to reduce return rates, optimize CAC/LTV, improve size recommendations, and achieve profitability in competitive e-commerce markets.
Learn how real estate platforms use analytics for price prediction, market trends analysis, lead scoring, and investment recommendations using regression and time-series models.
Craft a resume that gets past ATS and impresses hiring managers. Includes templates, keyword optimization, project descriptions, and real examples from analysts at Google, Amazon, and Flipkart.
Build portfolio projects that demonstrate SQL, Python, Tableau skills and business thinking. Includes step-by-step guides, datasets, GitHub templates, and real examples from hired analysts.
Comprehensive guide to data analyst interview questions covering SQL, Python, statistics, case studies, and behavioral questions. Includes answers, examples, and tips from analysts at Google, Amazon, Flipkart.
Write a cover letter that gets noticed. Includes template, real examples for fresher and experienced analysts, and tips to customize for each company (Flipkart, Google, startups).
Optimize your LinkedIn profile to attract recruiters. Includes headline templates, summary examples, skills to add, and tips to appear in recruiter searches (SQL, Python, Tableau).
Complete list of 50+ companies hiring data analysts in India across FAANG, startups, e-commerce, fintech, and consulting. Includes salary ranges, required skills, and application tips.
Complete guide for freshers to land first data analyst job. Includes portfolio projects, resume template, skills to learn, companies that hire freshers, and interview preparation strategy.
Complete glossary of data analytics terms with plain-English definitions. Covers business metrics, analytics techniques, tools, and statistical concepts. Searchable reference guide.
Complete SQL glossary with plain-English definitions. Covers SELECT, JOIN, WHERE, GROUP BY, window functions, aggregations, and database concepts. Quick reference for data analysts.
Complete statistics glossary covering mean, median, standard deviation, p-value, confidence intervals, hypothesis testing, and probability concepts. Plain-English definitions with examples.
Essential ML glossary covering supervised learning, unsupervised learning, regression, classification, clustering, and evaluation metrics. Plain-English definitions for analysts.
50+ free dataset sources for data analysis practice. Includes Kaggle, UCI, data.gov.in, Google Dataset Search, industry-specific datasets, and portfolio project ideas.
Complete toolkit for data analysts in 2026. Covers SQL databases, Python libraries, BI tools, Excel, collaboration platforms, and learning resources. All tools categorized by function.
Complete e-commerce analytics project using Python and SQL. Includes dataset, RFM analysis, cohort retention, funnel analysis, and Power BI dashboard. Perfect for portfolio.
Complete IPL cricket analytics project using Python pandas. Analyze player performance, team statistics, match outcomes. Includes dataset and code for portfolio.
Build interactive IPL cricket dashboard in Power BI. Includes team performance, player stats, match trends, filters. Perfect for portfolio with India context.
Analyze data analyst job market using Naukri/LinkedIn data. Skills demand, salary trends, top companies. Python project for portfolio.
Analyze social media performance with Python. Track engagement metrics, growth trends, content performance. Project includes dataset and visualization code.
Calculate ROI of analytics tools and training investments. Interactive calculator shows payback period, net benefit, ROI percentage for data analyst courses and software.
Calculate fair salary for data analysts in India. Compare by city, experience, skills. Interactive calculator shows market rate, negotiation range, and tips.
Complete data cleaning checklist for analysts. Handle missing values, duplicates, outliers, data types, validation. Python and SQL examples included.
Master data storytelling to present insights that drive action. Framework for structuring presentations, visualizations, and recommendations. For data analysts.
Master proven storytelling frameworks for data presentations. Pyramid Principle, Situation-Complication-Resolution, SCQA. Structure insights that drive action.
Practice SQL with 25 real interview questions from Amazon, Flipkart, Google. Covers SELECT, JOINs, GROUP BY, window functions, CTEs. Solutions included.
Practice Python for data analysis with 20 hands-on problems. Pandas, NumPy, data cleaning, aggregation, visualization. Solutions included.
Test your data analyst knowledge with 50-question quiz covering SQL, Python, statistics, Excel, Power BI. Get instant feedback and explanations.
Complete roadmap for data analysts looking to advance. Explore 5 career paths (Senior Analyst, Data Scientist, Analytics Engineer, Product Manager, Consultant) with skills needed for each.