SkillNavoraData Science & AI Handbook
AI-First Data Engineer · Data Science & AI

Data Science & AI Handbook

Every class, explained simply. 124 lessons across Excel, SQL, Power BI, Python, statistics, machine learning, generative AI and agentic AI, with real outputs, diagrams, quizzes, practice files and a downloadable PDF for each lesson.

Start learning →⬇ Complete handbook PDFLive class tracker
124lessons
11tracks
316+diagrams and charts
759quiz questions
275kwords
Created by Varun Kumar · Version 2026.10 · Updated October 2026
1Pick your track or follow the live class tracker.
2Read the lesson, run the examples and look at every chart.
3Take the quiz, solve the exercises, then practise online.
4Download the PDF to revise offline and tick the lesson complete.
Track 1 · 12 lessons

Advanced Excel

From the Excel window to dashboards: formatting, 100+ functions, lookups, pivot tables, advanced charts, Power Query, Power Pivot and AI in Excel.

Start lesson 1 →⬇ Track PDF
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  1. 01Getting Started with Excel for Data Analysis● Live classes 1–2PDF
  2. 02Data Entry, Data Validation & Data Cleaning● Live class 2PDF
  3. 03Number, Custom & Conditional Formatting● Live class 2PDF
  4. 04Formulas, Cell References & Aggregate Functions● Live class 3PDF
  5. 05Text Functions, Logical Functions & IF● Live classes 3–4PDF
  6. 06Sorting, Filtering & Dynamic Arrays● Live class 4PDF
  7. 07Lookup Functions: VLOOKUP, XLOOKUP and INDEX-MATCH● Live classes 4–5PDF
  8. 08Pivot Tables from Basic to Advanced● Live classes 5–7PDF
  9. 09Date, Time & Number Functions and Named Ranges● Live classes 7–8PDF
  10. 10Charts and Advanced Charts● Live classes 7–8PDF
  11. 11Interactive Dashboard: Superstore Case Study● Live classes 8–9PDF
  12. 12Power Query, Power Pivot, Analysis ToolPak & What-If Analysis● Live class 9PDF
Track 2 · 15 lessons

SQL for Data Analysis

Relational databases, ER diagrams and keys, DDL/DML, filtering, aggregation, joins, set operators, subqueries, window functions, CTEs, views, procedures and performance.

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  1. 01Databases, RDBMS & SQL Basics● SQL class 1PDF
  2. 02ER Diagrams, Relationships, Keys & Normalisation● SQL class 1PDF
  3. 03Data Types, Operators, NULLs, Variables & Comments● SQL class 2PDF
  4. 04DDL, DML & Constraints: CREATE, ALTER, INSERT, UPDATE, DELETE● SQL classes 2–3PDF
  5. 05SELECT, WHERE, ORDER BY & Filtering Operators● SQL class 3PDF
  6. 06SQL Functions: Aggregate, Text, Date & Number● SQL class 4PDF
  7. 07GROUP BY, HAVING, CASE WHEN, ROLLUP, CUBE & COALESCE● SQL class 4 · SQL–Power BI bridgePDF
  8. 08Joins and a Retail Case Study● SQL class 4PDF
  9. 09Set Operators: UNION, UNION ALL, INTERSECT & EXCEPT● SQL class 4PDF
  10. 10Subqueries, Derived Tables & EXISTS● SQL classes 4–5PDF
  11. 11OFFSET-FETCH and Window Functions I: OVER, PARTITION BY & Ranking● SQL class 5PDF
  12. 12Window Functions II: Running Totals, LEAD, LAG & Frames● SQL class 5–6PDF
  13. 13CTEs, Views, Temporary Tables, Stored Procedures & Functions● SQL class 6PDF
  14. 14Transactions, Indexes, Performance & Security● SQL class 6 (extended)PDF
  15. 15End-to-End SQL Case Study & Interview Practice● SQL class 5 (end-to-end case study)PDF
Track 3 · 6 lessons

Power BI

ETL with Power Query, star-schema modelling, DAX measures and time intelligence, interactive reports, case studies, publishing, security and Copilot.

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  1. 01Getting Started with Power BI & Your First HR Report● Power BI class 1PDF
  2. 02Power Query, Star Schema & Data Modelling (Adventure Cycles)● Power BI class 3PDF
  3. 03DAX Fundamentals: Measures, Filter Context & CALCULATE● Power BI classes 2–3PDF
  4. 04DAX Time Intelligence, Ranking, Parameters & Dynamic Measures● Power BI classes 3–4PDF
  5. 05Visuals, Filters, Drill-through, Tooltips, Buttons & Report Design● Power BI classes 2–4PDF
  6. 06Power BI Service, Connectivity Modes, Security & Capstone● Power BI class 4PDF
Track 4 · 6 lessons

Tableau

Tableau Public and Desktop: connecting data, dimensions and measures, every core chart, calculated fields, table calculations and LOD expressions, filters, parameters and sets, data modelling, dashboards, stories and publishing.

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  1. 01Getting Started with Tableau: Interface, Data Connections and Your First ViewsPDF
  2. 02Charts in Tableau: From Bar and Line to Maps, Dual Axis, Histograms and ParetoPDF
  3. 03Calculations in Tableau: Calculated Fields, Table Calculations and LOD ExpressionsPDF
  4. 04Filters, Parameters, Sets, Groups and Hierarchies in TableauPDF
  5. 05Data Modelling in Tableau: Relationships, Joins, Unions, Blending, Extracts and Tableau PrepPDF
  6. 06Dashboards, Actions, Stories and Publishing in Tableau (Capstone)PDF
Track 5 · 4 lessons

AI for Data Analysis

Generative AI for analysts: prompting, AI vs GenAI vs agentic AI, AI over SQL (Metabase, MinusX), Copilot and Gemini in Excel, and office agents.

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  1. 01Generative AI for Analysts: How It Works, Prompting & AI vs GenAI vs Agentic AI● DVA with AI · class 1PDF
  2. 02AI with SQL: Text-to-SQL, Docker, Metabase & MinusX● DVA with AI · class 2PDF
  3. 03AI in Excel & Office: Copilot, Gemini, Claude, Python in Excel & Agents● DVA with AI · class 3PDF
  4. 04The AI-Assisted Analyst Workflow, Agents & Governance (Capstone)● DVA with AI · wrap-upPDF
Track 6 · 29 lessons

Python for Data Science

Python from zero: data types, control flow, data structures, functions, OOP, files, NumPy, pandas, visualisation, dates, SQL connections, EDA and DSA for data roles.

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  1. 01Introduction to Python: Why, Install & Jupyter Notebooks● Python class 1PDF
  2. 02Variables, Data Types, Type Conversion, print(), f-strings & input()● Python class 2PDF
  3. 03Operators, Strings, Indexing, Slicing & String Methods● Python classes 3–4PDF
  4. 04Control Flow: if, elif, else, Nested Conditions & match● Python class 3PDF
  5. 05Loops: while, for, range, break, continue & Patterns● Python classes 3–4PDF
  6. 06Lists: Creating, Slicing, Methods, Sorting & Comprehensions● Python class 5PDF
  7. 07Tuples & Sets: Immutable Records, Unpacking and Set Algebra● Python class 5PDF
  8. 08Dictionaries: Key-Value Data, Counting and Grouping● Python class 6PDF
  9. 09Functions: Reusable Code, Arguments, Scope and Docstrings● Python class 7PDF
  10. 10Comprehensions, Lambda, map/filter and Generators● Python class 8PDF
  11. 11Object-Oriented Python: Classes, Objects, Inheritance and Dataclasses● Python class 9PDF
  12. 12Files, Exceptions, JSON and CSV● Python class 10PDF
  13. 13Modules, Packages, Virtual Environments and Git● Python class 10PDF
  14. 14Debugging, Testing, Regular Expressions and Code Quality● Python class 10PDF
  15. 15NumPy: Fast Arrays, Vectorisation and Broadcasting● Python class 11PDF
  16. 16pandas I: Series, DataFrames, Reading Data, Selecting and Filtering● Python class 12PDF
  17. 17pandas II: Data Cleaning: Missing Values, Types, Duplicates, Text and Outliers● Python class 13PDF
  18. 18pandas III: GroupBy, Pivot Tables, Merge/Join and Reshape (melt/unpivot)● Python class 14PDF
  19. 19pandas IV: Dates, Time Series, Resampling and Rolling Windows● Python class 15PDF
  20. 20Data Visualisation I: Matplotlib and Choosing the Right Chart● Python class 16PDF
  21. 21Data Visualisation II: Seaborn Statistical Charts and Interactive Plotly● Python class 16PDF
  22. 22Exploratory Data Analysis (EDA) Case Study: Telecom Customer Churn● Python class 17PDF
  23. 23Python and SQL: sqlite3, DuckDB, SQLAlchemy and pandas.read_sql● Python class 18PDF
  24. 24APIs, JSON and Web Scraping with requests and BeautifulSoup● Python class 18PDF
  25. 25Performance and Bigger Data: Vectorisation, Memory, Parquet, Polars and DuckDB● Python class 19PDF
  26. 26DSA I: Big-O, Arrays, Strings, Hashing, Two Pointers and Sliding Window● DSA for data roles (added)PDF
  27. 27DSA II: Stacks, Queues, Linked Lists, Recursion, Sorting and Binary Search● DSA for data roles (added)PDF
  28. 28DSA III: Trees, Heaps, Graphs (BFS/DFS) and Dynamic Programming● DSA for data roles (added)PDF
  29. 29Python Capstone: End-to-End Retail Sales Analytics Project● Python capstonePDF
Track 7 · 28 lessons

Statistics & Machine Learning

Probability, CLT, hypothesis tests, the ML lifecycle, regression, classification, trees, ensembles, clustering, PCA, time series, NLP, deep learning basics, evaluation, explainability and deployment.

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  1. 01Descriptive Statistics: Centre, Spread, Shape and Relationships● Python class 14PDF
  2. 02Probability and Distributions: Rules, Bayes, Binomial, Poisson and Normal● Python class 14PDF
  3. 03Sampling, the Central Limit Theorem and Confidence Intervals● Python classes 14–15 · ML class 1PDF
  4. 04Hypothesis Testing I: The Framework, p-values, z-tests and t-tests● ML classes 1–2PDF
  5. 05Hypothesis Testing II: Chi-Square, ANOVA, Non-Parametric Tests and Correlation Tests● ML classes 1 and 3 · Python class 15PDF
  6. 06A/B Testing and Experimentation: Design, Power, Analysis and Pitfalls● ML class 2 (case study)PDF
  7. 07Introduction to Machine Learning: Types, Business Framing and the ML Lifecycle● ML classes 3–4PDF
  8. 08The scikit-learn Workflow: Train/Test Split, Baselines, Pipelines and Saving Models● ML class 4PDF
  9. 09Data Preprocessing and Feature Engineering for ML● ML class 6PDF
  10. 10Linear Regression: Fitting, Interpreting, Assumptions and Diagnostics● Python class 16 · ML class 4PDF
  11. 11Gradient Descent: How Models Learn (Built from Scratch in NumPy)● ML class 5PDF
  12. 12Overfitting, Polynomial Regression and Regularisation (Ridge, Lasso, Elastic Net)● ML class 6PDF
  13. 13Logistic Regression: Probabilities, Odds Ratios and Credit-Risk Scoring● Python class 16 · ML class 4PDF
  14. 14Classification Metrics: Confusion Matrix, Precision, Recall, ROC, PR and Business Value● ML class 7PDF
  15. 15Cross-Validation and Hyperparameter Tuning: K-Fold, GridSearchCV, RandomizedSearchCV and Optuna● ML classes 6–7PDF
  16. 16K-Nearest Neighbours: Classifier and Regressor (Bank Loans Case Study)● ML class 7PDF
  17. 17Decision Trees: Splits, Impurity, Pruning and Interpretable Rules● ML track (added)PDF
  18. 18Ensembles I: Bagging and Random Forests● ML track (added)PDF
  19. 19Ensembles II: Gradient Boosting, XGBoost, LightGBM and CatBoost● ML track (added)PDF
  20. 20Support Vector Machines and Naive Bayes● ML track (added)PDF
  21. 21Imbalanced Classification: Fraud Detection with Class Weights, Resampling and Thresholds● ML track (added)PDF
  22. 22Clustering I: K-Means and RFM Customer Segmentation● ML class 6PDF
  23. 23Clustering II and Dimensionality Reduction: Hierarchical, DBSCAN, Gaussian Mixtures, PCA, t-SNE and UMAP● ML track (added)PDF
  24. 24Time-Series Forecasting: Decomposition, Baselines, ETS, ARIMA and ML with Lag Features● ML track (added) · Power BI forecastingPDF
  25. 25NLP and Text Analytics: Cleaning, TF-IDF, Sentiment Classification, Topics and Embeddings● ML track (added) · GenAI bridgePDF
  26. 26Neural Networks and Deep Learning: From a Neuron to Transformers● ML track (added) · GenAI bridgePDF
  27. 27Explainable AI and MLOps: SHAP, Fairness, Deployment, Monitoring and Responsible ML● ML track (added)PDF
  28. 28ML Capstone: End-to-End Churn Prediction and Retention Strategy● ML capstonePDF
Track 8 · 12 lessons

Generative AI & Agentic AI

How LLMs work, prompting, LLM APIs in Python, embeddings and vector search, RAG, tool use, agents, MCP, evaluation, guardrails, fine-tuning and responsible AI.

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  1. 01How Large Language Models Work: Tokens, Embeddings, Transformers and Training● DVA + AI class 1 (deep dive)PDF
  2. 02Prompt Engineering in Depth: Patterns, Templates, Few-Shot, Reasoning and Structured Output● DVA + AI class 1 (deep dive)PDF
  3. 03Calling LLM APIs from Python: Messages, Streaming, Structured Output, Costs and Reliability● GenAI LaunchpadPDF
  4. 04Embeddings and Vector Search: Meaning as Numbers, Similarity and Vector Databases● GenAI LaunchpadPDF
  5. 05Retrieval-Augmented Generation (RAG): Build, Evaluate and Improve a Q&A Assistant● GenAI LaunchpadPDF
  6. 06Tool Use and Function Calling: Letting LLMs Query Data, Call APIs and Take Actions● GenAI LaunchpadPDF
  7. 07AI Agents and Agentic AI: Loops, Planning, Memory, Multi-Agent Systems and When to Use Them● DVA + AI class 1 (AI vs GenAI vs Agentic AI) · GenAI LaunchpadPDF
  8. 08Model Context Protocol (MCP): Connecting AI Assistants to Your Data and Tools● DVA + AI class 2 (MinusX, Metabase) · GenAI LaunchpadPDF
  9. 09Evaluating LLM Applications and Building Guardrails: Test Sets, LLM-as-Judge, Injection, PII and Safety● DVA + AI class 3 (governance) · GenAI LaunchpadPDF
  10. 10Customising Models: Prompting vs RAG vs Fine-Tuning, LoRA, Distillation and Open-Weight Models● GenAI LaunchpadPDF
  11. 11GenAI for Data Work: Text-to-SQL, AI Coding Assistants, Data Cleaning and Automated Insights● DVA + AI classes 2–3 · GenAI LaunchpadPDF
  12. 12Building and Shipping a GenAI App: Architecture, Streamlit/FastAPI, Cost, Observability and Responsible AI (Capstone)● GenAI Launchpad capstonePDF
Track 9 · 6 lessons

AI Agents, MCP & APIs for PMs and BAs

No-code track for PMs, BAs and senior PMs: APIs, MCP with Claude, how AI agents work and how to build them, connecting apps with n8n, Make, Zapier and Replit, a complete Claude Code tutorial, and voice agents and voice cloning.

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  1. 01APIs Without Code: How Apps Talk, REST, JSON, Authentication, Webhooks and Postman for PMs and BAsPDF
  2. 02MCP (Model Context Protocol) Without Code: Connect Claude to Your Apps and DataPDF
  3. 03How AI Agents Work and How to Build One Without CodePDF
  4. 04Connect All Your Apps with Claude, n8n, Make, Zapier and ReplitPDF
  5. 05Claude Code Complete Tutorial: From First Prompt to Agents, MCP, Hooks and AutomationPDF
  6. 06Voice Agents and Voice Cloning: Tools, Architecture, Build Steps and Responsible UsePDF
Track 10 · 2 lessons

AI & No-Code Tools Lab

Complete tutorials for the design and automation tools used across the programs: Figma (design systems, prototypes, Dev Mode, Figma Make) and n8n (workflows, APIs, AI agents, MCP).

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  1. 01Figma Complete Tutorial: Interface, Auto Layout, Components, Variables, Prototypes, Dev Mode and Figma AIPDF
  2. 02n8n Complete Tutorial: Workflow Automation, Data Pipelines and AI AgentsPDF
Track 11 · 4 lessons

Capstone, Portfolio & Interviews

End-to-end capstone, portfolio and GitHub, analyst and data scientist interview preparation with model answers, and a 90-day plan.

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  1. 01Portfolio, GitHub and Resume for Data Roles● Career trackPDF
  2. 02Data Analyst Interview Preparation: SQL, Excel, Power BI, Statistics and Case Questions● Career trackPDF
  3. 03Data Scientist, ML and GenAI Interview Preparation: Questions and Model Answers● Career trackPDF
  4. 04Your 90-Day Plan: Learning Roadmap, Job Search Strategy and Growing in Your First Role● Career trackPDF

Live class tracker

What each live class covered, and the handbook lessons that explain it in detail. Lessons go further than the class, with the latest 2026 features.

Data Visualization & Analytics 2026 (Excel)
Class 1Programme introduction and orientationGetting Started with Excel for Data Analysis
Class 2Basics of Excel, terminology, shortcuts, properties, data types, data validation, formatting, number and custom number formatting, conditional and custom conditional formatting, first worksheetGetting Started with Excel for Data Analysis Data Entry, Data Validation & Data Cleaning Number, Custom & Conditional Formatting
Class 3Formulas vs functions; aggregate (SUM, COUNT, COUNTA, COUNTBLANK, AVERAGE, MIN, MAX, SMALL, LARGE); text (LEFT, RIGHT, MID, CONCAT, TEXTJOIN, LOWER, UPPER, PROPER, LEN, TRIM, FIND, SEARCH, TEXT); logical (AND, OR, NOT); Text to Columns; IFFormulas, Cell References & Aggregate Functions Text Functions, Logical Functions & IF
Class 4Doubts; IF, IFS, nested IF; array functions; sorting and level-wise sorting; filtering; remove duplicates; UNIQUE(); SUMPRODUCT; VLOOKUP, HLOOKUP, XLOOKUPText Functions, Logical Functions & IF Sorting, Filtering & Dynamic Arrays
Class 5Reverse lookup, INDEX, MATCH, INDEX + MATCH, CHOOSE, VLOOKUP with CHOOSE; pivot tables, pivot cache, pivot areas, filtering, grand totalSorting, Filtering & Dynamic Arrays Lookup Functions: VLOOKUP, XLOOKUP and INDEX-MATCH
Class 6Pivot refresh vs Refresh All, groups and bins, grand and sub totals, expand/collapse, MoM and YoY, calculated fields; Video Game Sales case studyLookup Functions: VLOOKUP, XLOOKUP and INDEX-MATCH
Class 7Running totals, slicers, value field settings; date and time functions; number functionsLookup Functions: VLOOKUP, XLOOKUP and INDEX-MATCH Pivot Tables from Basic to Advanced
Class 8Named ranges (static and dynamic); thermometer, Pareto, population pyramid and waterfall charts; Superstore case study beginsPivot Tables from Basic to Advanced Date, Time & Number Functions and Named Ranges
Class 9Superstore dashboard; protected sheet and workbook; Power Pivot; Power Query Editor; comments; descriptive statistics with Analysis ToolPak; Evaluate FormulaDate, Time & Number Functions and Named Ranges Charts and Advanced Charts
SQL
SQL 1SQL basics, terminology, ER diagrams, relationship types, keys (primary, foreign, super, candidate, alternate, composite), DDL/DML/DCL/TCL, installing SSMSDatabases, RDBMS & SQL Basics ER Diagrams, Relationships, Keys & Normalisation
SQL 2Properties of SQL, data types, variables and naming rules, operators, comments, Excel vs SQL; CREATE, ALTER, DROP, TRUNCATE; INSERT, DELETE, SELECT; DELETE vs DROP vs TRUNCATEData Types, Operators, NULLs, Variables & Comments DDL, DML & Constraints: CREATE, ALTER, INSERT, UPDATE, DELETE
SQL 3Constraints; aliases with AS; SELECT, WHERE, ORDER BY; IN, NOT IN, LIKE, NOT LIKE, IS NULL, IS NOT NULLDDL, DML & Constraints: CREATE, ALTER, INSERT, UPDATE, DELETE SELECT, WHERE, ORDER BY & Filtering Operators
SQL 4GROUP BY and HAVING; aggregate, text, date and number functions; DATEPART(); CASE WHEN; case study with joins; UNION ALL, UNION, INTERSECT, EXCEPT; subqueriesSQL Functions: Aggregate, Text, Date & Number GROUP BY, HAVING, CASE WHEN, ROLLUP, CUBE & COALESCE Joins and a Retail Case Study Set Operators: UNION, UNION ALL, INTERSECT & EXCEPT Subqueries, Derived Tables & EXISTS
SQL 5End-to-end case study; OFFSET FETCH; window functions; OVER, PARTITION BY; RANK, DENSE_RANK, ROW_NUMBER; aggregate windows; LEAD and LAG; CTEs, views, stored proceduresOFFSET-FETCH and Window Functions I: OVER, PARTITION BY & Ranking Window Functions II: Running Totals, LEAD, LAG & Frames CTEs, Views, Temporary Tables, Stored Procedures & Functions End-to-End SQL Case Study & Interview Practice
SQL + Power BI bridgeROLLUP, CUBE and COALESCE()GROUP BY, HAVING, CASE WHEN, ROLLUP, CUBE & COALESCE
Power BI
Power BI 1Components and views, installing Power BI Desktop, ETL, analytics stages; HR data analysis; visuals and formatting; YEAR() DAX column; data groupsGetting Started with Power BI & Your First HR Report Power Query, Star Schema & Data Modelling (Adventure Cycles)
Power BI 2Small multiples; DAX measures and columns; cards and matrix; filters on cards; chart formatting; imagesDAX Fundamentals: Measures, Filter Context & CALCULATE DAX Time Intelligence, Ranking, Parameters & Dynamic Measures
Power BI 3Adventure Works case study; hierarchies; custom tooltips; KPIs, cards and gauges; visual, page and report filters; edit interactions; custom themes; conditional formatting; marking a date tableVisuals, Filters, Drill-through, Tooltips, Buttons & Report Design Power BI Service, Connectivity Modes, Security & Capstone
Power BI 4Parameters and dynamic measures; Q&A; forecasting; buttons and bookmarks; drill-through, include/exclude; import vs DirectQuery; display foldersPower BI Service, Connectivity Modes, Security & Capstone
Data Visualization & Analytics with AI
DVA + AI 1Unpivot columns in Power Query; GenAI basics; prompt engineering; AI vs GenAI vs Agentic AIGenerative AI for Analysts: How It Works, Prompting & AI vs GenAI vs Agentic AI AI with SQL: Text-to-SQL, Docker, Metabase & MinusX
DVA + AI 2AI in SQL; installing Docker; Metabase and MinusXAI in Excel & Office: Copilot, Gemini, Claude, Python in Excel & Agents
DVA + AI 3Gemini in Excel, Copilot for Excel, office agentsThe AI-Assisted Analyst Workflow, Agents & Governance (Capstone)
Python
Python 1Course intro, properties of Python, installations, Jupyter notebooks, intro to programmingIntroduction to Python: Why, Install & Jupyter Notebooks
Python 2Type conversion, print(), f-strings, .format(), input()Variables, Data Types, Type Conversion, print(), f-strings & input() Operators, Strings, Indexing, Slicing & String Methods
Python 3Control flow: if/elif/else, while loopsControl Flow: if, elif, else, Nested Conditions & match Loops: while, for, range, break, continue & Patterns
Python 4for loops, intro to OOP, string methods, indexing and slicingLoops: while, for, range, break, continue & Patterns Operators, Strings, Indexing, Slicing & String Methods Object-Oriented Python: Classes, Objects, Inheritance and Dataclasses
Python 5Data structures: lists, dictionaries, tuples, setsLists: Creating, Slicing, Methods, Sorting & Comprehensions Tuples & Sets: Immutable Records, Unpacking and Set Algebra Dictionaries: Key-Value Data, Counting and Grouping
Python 6User-defined functions, lambda, map() and filter()Functions: Reusable Code, Arguments, Scope and Docstrings Comprehensions, Lambda, map/filter and Generators
Python 7File handling, modules and packagesFiles, Exceptions, JSON and CSV Modules, Packages, Virtual Environments and Git
Python 8NumPy arraysNumPy: Fast Arrays, Vectorisation and Broadcasting
Python 9pandas DataFrames: subsetting, reordering, renaming, dropping, sorting, filteringpandas I: Series, DataFrames, Reading Data, Selecting and Filtering
Python 10Type conversion, univariate analysis, aggregate functions, missing valuespandas II: Data Cleaning: Missing Values, Types, Duplicates, Text and Outliers pandas III: GroupBy, Pivot Tables, Merge/Join and Reshape (melt/unpivot)
Python 11Joining datasets, groupby and pivot_tablepandas III: GroupBy, Pivot Tables, Merge/Join and Reshape (melt/unpivot)
Python 12Visualisation in PythonData Visualisation I: Matplotlib and Choosing the Right Chart Data Visualisation II: Seaborn Statistical Charts and Interactive Plotly Exploratory Data Analysis (EDA) Case Study: Telecom Customer Churn
Python 13Dates in Python, outlier treatment, connecting to SQL databasespandas IV: Dates, Time Series, Resampling and Rolling Windows pandas II: Data Cleaning: Missing Values, Types, Duplicates, Text and Outliers Python and SQL: sqlite3, DuckDB, SQLAlchemy and pandas.read_sql
Python 14Matplotlib subplots, probability distributions, normal distribution, inferential statistics, CLTData Visualisation I: Matplotlib and Choosing the Right Chart Descriptive Statistics: Centre, Spread, Shape and Relationships Probability and Distributions: Rules, Bayes, Binomial, Poisson and Normal Sampling, the Central Limit Theorem and Confidence Intervals
Python 15Sampling, CLT, hypothesis testing; z, t, chi-square and ANOVASampling, the Central Limit Theorem and Confidence Intervals Hypothesis Testing I: The Framework, p-values, z-tests and t-tests Hypothesis Testing II: Chi-Square, ANOVA, Non-Parametric Tests and Correlation Tests
Python 16Intro to ML and statistical modelling; linear and logistic regressionIntroduction to Machine Learning: Types, Business Framing and the ML Lifecycle Linear Regression: Fitting, Interpreting, Assumptions and Diagnostics Logistic Regression: Probabilities, Odds Ratios and Credit-Risk Scoring
Machine Learning
ML 1Hypothesis testing, CLT, parametric vs non-parametric tests, hypothesis-testing case studySampling, the Central Limit Theorem and Confidence Intervals Hypothesis Testing I: The Framework, p-values, z-tests and t-tests Hypothesis Testing II: Chi-Square, ANOVA, Non-Parametric Tests and Correlation Tests
ML 2Tests in the case study, p-value, alpha, critical value, one-tailed vs two-tailedHypothesis Testing I: The Framework, p-values, z-tests and t-tests A/B Testing and Experimentation: Design, Power, Analysis and Pitfalls
ML 3Conclusion of hypothesis testing; intro to ML; business problem types; business to statistics problem; types of ML systemsHypothesis Testing II: Chi-Square, ANOVA, Non-Parametric Tests and Correlation Tests Introduction to Machine Learning: Types, Business Framing and the ML Lifecycle
ML 4Life cycle of analytics and ML projects; linear vs non-linear models; linear equations and link functionsIntroduction to Machine Learning: Types, Business Framing and the ML Lifecycle The scikit-learn Workflow: Train/Test Split, Baselines, Pipelines and Saving Models Logistic Regression: Probabilities, Odds Ratios and Credit-Risk Scoring
ML 5Gradient descent for linear regression in PythonGradient Descent: How Models Learn (Built from Scratch in NumPy)
ML 6Pre-processing: missing values, outliers, categorical encoding; overfitting and underfitting; heuristic segmentation (RFM)Data Preprocessing and Feature Engineering for ML Cross-Validation and Hyperparameter Tuning: K-Fold, GridSearchCV, RandomizedSearchCV and Optuna Clustering I: K-Means and RFM Customer Segmentation
ML 7KNN classifier and regressor, GridSearchCV, Bank Loans case study, classification metricsK-Nearest Neighbours: Classifier and Regressor (Bank Loans Case Study) Cross-Validation and Hyperparameter Tuning: K-Fold, GridSearchCV, RandomizedSearchCV and Optuna Classification Metrics: Confusion Matrix, Precision, Recall, ROC, PR and Business Value

Practice hub

Every lesson ends with a quiz and exercises. For more practice, these free sites have exercises, quizzes and coding challenges. Links open the original site.

Excel
SQL
Power BI
Tableau
AI Analytics
Python
Stats & ML
GenAI & Agents
Agents
Tools
Career
SFMC
Marketing
Content
FDE

Practice datasets

All datasets are fictional and generated for this handbook, so you can share and reuse them freely. Download a CSV and follow along in Excel, SQL, Power BI or Python.

FileRowsWhat it contains
superstore_sales.csv6,323Retail orders 2023–2025 across Indian cities: dates, ship mode, customer, segment, region, product, category, sales, quantity, discount, profit
customers.csv800Customer master: id, name, segment, city, state, region
products.csv78Product master: id, category, sub-category, name, list price
video_game_sales.csv2,400Fictional game titles with platform, year, genre, publisher and regional sales (millions)
hr_employees.csv1,5001,500 employees: department, role, education, hire date, income, satisfaction, overtime, rating, attrition
employees_small.csv2020-row employee table used in the Excel and SQL basics lessons
lookup_products.csv66-row product table used in the Excel lookup lesson
reps_small.csv88-row sales reps table used in the Excel sorting, filtering and dynamic array lesson
bank_loans.csv850Loan applicants: age, education, employment and address years, income, debt ratios, default flag (last 150 rows are new applicants)
house_prices.csv1,200Homes in four cities: area, rooms, age, distance to centre, parking, price in lakh (with some missing values and outliers)
retail_transactions.csv4,548Invoices per customer for RFM segmentation
telecom_churn.csv3,000Subscribers with tenure, contract, service, charges, support calls and churn
daily_sales.csv1,461Daily units sold 2022–2025 with trend, seasonality and weekend effect
ab_test.csv4,000A/B test of a new checkout: group, converted, order value
delivery_times.csv115Delivery minutes in two cities (two-sample t-test)
channel_survey.csv600Age group vs preferred shopping channel (chi-square test)
diet_study.csv120Weight loss under three diets (ANOVA)
product_reviews.csv1,200Short product reviews with rating and sentiment label (NLP)
customer_reviews.csv3,0003,000 realistic e-commerce reviews with stars, sentiment, negation and mixed opinions (text classification)
adventure_cycles/fact_sales.csv9,000Power BI case study fact table: order date, product, customer, territory, quantity, sales, cost
adventure_cycles/dim_product.csv45Power BI case study: product dimension
adventure_cycles/dim_customer.csv1,200Power BI case study: customer dimension
adventure_cycles/dim_territory.csv6Power BI case study: territory dimension
adventure_cycles/dim_date.csv1,096Power BI case study: date table

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Complete handbook

All 128 lessons (1,211 pages) in four PDF parts, one per group of tracks.

⬇ Part 1: Excel, SQL, Power BI, AI (497 pages)⬇ Part 2: Python (300 pages)⬇ Part 3: Statistics & ML (277 pages)⬇ Part 4: GenAI & Career (149 pages)

Track PDFs

Lesson PDFs and workbooks

Every lesson has its own PDF (the “PDF” link next to it). Excel lessons also include a practice workbook with live formulas.

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