Data Science, GenAI & Product Handbooks

1. M01Fundamentals of Product ManagementPDF
2. M02Product Strategy and Strategic ThinkingPDF
3. M03Problem Discovery and Market ResearchPDF
4. M04Problem Framing and PrioritizationPDF
5. M05UX and Design CollaborationPDF
6. M06Effective Communication and Stakeholder ManagementPDF
7. M07Product Analytics and MetricsPDF
8. M08Product Launch, Growth, and MonetizationPDF
9. M09Tech 101 and System Design for PMsPDF
10. M10LLMs, RAGs, and AI FoundationsPDF
11. M11Vibe Coding for Product ManagersPDF
12. M12Agentic AI and AI SystemsPDF

1. M13AI Tools Landscape and Your Starter StackPDF
2. M14Claude for Daily PM Work: Basic to AdvancedPDF
3. M15Claude Code: Basic to AdvancedPDF
4. M16Lovable: Basic to AdvancedPDF
5. M17Replit and Other AI App BuildersPDF
6. M18Figma and AI Design ToolsPDF
7. M19Notion, PRD Tools and AI DocumentationPDF
8. M20Automation with Zapier, Make and n8nPDF
9. M21AI Chatbots for Business: WhatsApp, Slack and WebPDF
10. M22Voice Agents for BusinessesPDF
11. M23Learning Paths, Courses and CommunitiesPDF

1. ·Start Here
2. ·Framework LibraryPDF
3. ·Case Study LibraryPDF
4. ·Practical Projects & PortfolioPDF
5. ·Interview HandbookPDF
6. ·Templates & datasets
7. ·Glossary
8. ·References & Resources
9. ·Appendix: Source Coverage & Gap Analysis

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

1. 01Getting Started with Excel for Data AnalysisLive classes 1–2PDF
2. 02Data Entry, Data Validation & Data CleaningLive class 2PDF
3. 03Number, Custom & Conditional FormattingLive class 2PDF
4. 04Formulas, Cell References & Aggregate FunctionsLive class 3PDF
5. 05Text Functions, Logical Functions & IFLive classes 3–4PDF
6. 06Sorting, Filtering & Dynamic ArraysLive class 4PDF
7. 07Lookup Functions: VLOOKUP, XLOOKUP and INDEX-MATCHLive classes 4–5PDF
8. 08Pivot Tables from Basic to AdvancedLive classes 5–7PDF
9. 09Date, Time & Number Functions and Named RangesLive classes 7–8PDF
10. 10Charts and Advanced ChartsLive classes 7–8PDF
11. 11Interactive Dashboard: Superstore Case StudyLive classes 8–9PDF
12. 12Power Query, Power Pivot, Analysis ToolPak & What-If AnalysisLive class 9PDF

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

1. 01Databases, RDBMS & SQL BasicsLive class: SQL class 1PDF
2. 02ER Diagrams, Relationships, Keys & NormalisationLive class: SQL class 1PDF
3. 03Data Types, Operators, NULLs, Variables & CommentsLive class: SQL class 2PDF
4. 04DDL, DML & Constraints: CREATE, ALTER, INSERT, UPDATE, DELETELive class: SQL classes 2–3PDF
5. 05SELECT, WHERE, ORDER BY & Filtering OperatorsLive class: SQL class 3PDF
6. 06SQL Functions: Aggregate, Text, Date & NumberLive class: SQL class 4PDF
7. 07GROUP BY, HAVING, CASE WHEN, ROLLUP, CUBE & COALESCELive class: SQL class 4 · SQL–Power BI bridgePDF
8. 08Joins and a Retail Case StudyLive class: SQL class 4PDF
9. 09Set Operators: UNION, UNION ALL, INTERSECT & EXCEPTLive class: SQL class 4PDF
10. 10Subqueries, Derived Tables & EXISTSLive class: SQL classes 4–5PDF
11. 11OFFSET-FETCH and Window Functions I: OVER, PARTITION BY & RankingLive class: SQL class 5PDF
12. 12Window Functions II: Running Totals, LEAD, LAG & FramesLive class: SQL class 5–6PDF
13. 13CTEs, Views, Temporary Tables, Stored Procedures & FunctionsLive class: SQL class 6PDF
14. 14Transactions, Indexes, Performance & SecurityLive class: SQL class 6 (extended)PDF
15. 15End-to-End SQL Case Study & Interview PracticeLive class: SQL class 5 (end-to-end case study)PDF

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

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

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.

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

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

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

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

1. 01Introduction to Python: Why, Install & Jupyter NotebooksLive class: Python class 1PDF
2. 02Variables, Data Types, Type Conversion, print(), f-strings & input()Live class: Python class 2PDF
3. 03Operators, Strings, Indexing, Slicing & String MethodsLive class: Python classes 3–4PDF
4. 04Control Flow: if, elif, else, Nested Conditions & matchLive class: Python class 3PDF
5. 05Loops: while, for, range, break, continue & PatternsLive class: Python classes 3–4PDF
6. 06Lists: Creating, Slicing, Methods, Sorting & ComprehensionsLive class: Python class 5PDF
7. 07Tuples & Sets: Immutable Records, Unpacking and Set AlgebraLive class: Python class 5PDF
8. 08Dictionaries: Key-Value Data, Counting and GroupingLive class: Python class 6PDF
9. 09Functions: Reusable Code, Arguments, Scope and DocstringsLive class: Python class 7PDF
10. 10Comprehensions, Lambda, map/filter and GeneratorsLive class: Python class 8PDF
11. 11Object-Oriented Python: Classes, Objects, Inheritance and DataclassesLive class: Python class 9PDF
12. 12Files, Exceptions, JSON and CSVLive class: Python class 10PDF
13. 13Modules, Packages, Virtual Environments and GitLive class: Python class 10PDF
14. 14Debugging, Testing, Regular Expressions and Code QualityLive class: Python class 10PDF
15. 15NumPy: Fast Arrays, Vectorisation and BroadcastingLive class: Python class 11PDF
16. 16pandas I: Series, DataFrames, Reading Data, Selecting and FilteringLive class: Python class 12PDF
17. 17pandas II: Data Cleaning: Missing Values, Types, Duplicates, Text and OutliersLive class: Python class 13PDF
18. 18pandas III: GroupBy, Pivot Tables, Merge/Join and Reshape (melt/unpivot)Live class: Python class 14PDF
19. 19pandas IV: Dates, Time Series, Resampling and Rolling WindowsLive class: Python class 15PDF
20. 20Data Visualisation I: Matplotlib and Choosing the Right ChartLive class: Python class 16PDF
21. 21Data Visualisation II: Seaborn Statistical Charts and Interactive PlotlyLive class: Python class 16PDF
22. 22Exploratory Data Analysis (EDA) Case Study: Telecom Customer ChurnLive class: Python class 17PDF
23. 23Python and SQL: sqlite3, DuckDB, SQLAlchemy and pandas.read_sqlLive class: Python class 18PDF
24. 24APIs, JSON and Web Scraping with requests and BeautifulSoupLive class: Python class 18PDF
25. 25Performance and Bigger Data: Vectorisation, Memory, Parquet, Polars and DuckDBLive class: Python class 19PDF
26. 26DSA I: Big-O, Arrays, Strings, Hashing, Two Pointers and Sliding WindowLive class: DSA for data roles (added)PDF
27. 27DSA II: Stacks, Queues, Linked Lists, Recursion, Sorting and Binary SearchLive class: DSA for data roles (added)PDF
28. 28DSA III: Trees, Heaps, Graphs (BFS/DFS) and Dynamic ProgrammingLive class: DSA for data roles (added)PDF
29. 29Python Capstone: End-to-End Retail Sales Analytics ProjectLive class: Python capstonePDF

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

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

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

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

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.

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

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).

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

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

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

1. CSVab_test.csv94 KB
2. CSVbank_loans.csv33 KB
3. CSVchannel_survey.csv7 KB
4. CSVcustomer_reviews.csv364 KB
5. CSVcustomers.csv41 KB
6. CSVdaily_sales.csv22 KB
7. CSVdelivery_times.csv1 KB
8. CSVdiet_study.csv2 KB
9. CSVemployees_small.csv1 KB
10. CSVhouse_prices.csv45 KB
11. CSVhr_employees.csv130 KB
12. CSVlookup_products.csv0 KB
13. CSVproduct_reviews.csv86 KB
14. CSVproducts.csv4 KB
15. CSVreps_small.csv0 KB
16. CSVretail_transactions.csv143 KB
17. CSVsuperstore_sales.csv1.1 MB
18. CSVtelecom_churn.csv172 KB
19. CSVvideo_game_sales.csv189 KB
20. CSVadventure_cycles/dim_customer.csv31 KB
21. CSVadventure_cycles/dim_date.csv32 KB
22. CSVadventure_cycles/dim_product.csv2 KB
23. CSVadventure_cycles/dim_territory.csv0 KB
24. CSVadventure_cycles/fact_sales.csv475 KB
25. XLSXexcel/excel-01.xlsx11 KB
26. XLSXexcel/excel-02.xlsx11 KB
27. XLSXexcel/excel-03.xlsx9 KB
28. XLSXexcel/excel-04.xlsx13 KB
29. XLSXexcel/excel-05.xlsx14 KB
30. XLSXexcel/excel-06.xlsx12 KB
31. XLSXexcel/excel-07.xlsx15 KB
32. XLSXexcel/excel-09.xlsx13 KB
33. XLSXexcel/excel-10.xlsx6 KB
34. XLSXexcel/excel-11.xlsx6 KB
35. XLSXexcel/excel-12.xlsx7 KB

1. 26DSA I: Big-O, Arrays, Strings, Hashing, Two Pointers and Sliding WindowLive class: DSA for data roles (added)PDF
2. 27DSA II: Stacks, Queues, Linked Lists, Recursion, Sorting and Binary SearchLive class: DSA for data roles (added)PDF
3. 28DSA III: Trees, Heaps, Graphs (BFS/DFS) and Dynamic ProgrammingLive class: DSA for data roles (added)PDF

1. 05Loops: while, for, range, break, continue & PatternsLive class: Python classes 3–4PDF
2. 06Lists: Creating, Slicing, Methods, Sorting & ComprehensionsLive class: Python class 5PDF
3. 07Tuples & Sets: Immutable Records, Unpacking and Set AlgebraLive class: Python class 5PDF
4. 08Dictionaries: Key-Value Data, Counting and GroupingLive class: Python class 6PDF
5. 09Functions: Reusable Code, Arguments, Scope and DocstringsLive class: Python class 7PDF
6. 10Comprehensions, Lambda, map/filter and GeneratorsLive class: Python class 8PDF

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

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

1. M10LLMs, RAGs, and AI FoundationsPDF
2. M11Vibe Coding for Product ManagersPDF
3. M12Agentic AI and AI SystemsPDF

1. M13AI Tools Landscape and Your Starter StackPDF
2. M14Claude for Daily PM Work: Basic to AdvancedPDF
3. M15Claude Code: Basic to AdvancedPDF
4. M16Lovable: Basic to AdvancedPDF
5. M17Replit and Other AI App BuildersPDF
6. M18Figma and AI Design ToolsPDF
7. M19Notion, PRD Tools and AI DocumentationPDF
8. M20Automation with Zapier, Make and n8nPDF
9. M21AI Chatbots for Business: WhatsApp, Slack and WebPDF
10. M22Voice Agents for BusinessesPDF
11. M23Learning Paths, Courses and CommunitiesPDF

APIs, MCP with Claude, how agents work and how to build one, connecting apps with n8n, Make, Zapier and Replit, Claude Code, and voice agents.

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

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

Complete SFMC guide: data extensions, SQL, AMPscript, Email Studio, Automation Studio, Journey Builder, mobile channels, deliverability, Einstein and certification.

1. 01Salesforce Marketing Cloud (SFMC) Complete Guide: Email Studio, Data Extensions, SQL, AMPscript, Journey Builder and CertificationPDF

Complete digital marketing guide: strategy, SEO and AI search, Google and Meta Ads, social and influencers, email and WhatsApp, GA4 and attribution, CRO, budgets and AI.

1. 01Digital Marketing Complete Guide: Strategy, SEO, Google and Meta Ads, Social, Email, WhatsApp, Analytics and AIPDF

Complete content creation guide: niche and pillars, hooks and writing, short and long video, design, AI tools, publishing system, growth, analytics and monetisation.

1. 01Content Creation Complete Guide: Strategy, Writing, Short and Long Video, Design, AI Tools, Distribution and MonetisationPDF

Complete FDE guide: the role, skills stack, deployment lifecycle, LLM solutions for customers, integration patterns, a capstone and interview preparation.

1. 01Forward Deployed Engineer (FDE) Complete Guide: The Role, Skills, AI Deployments, Integrations and InterviewsPDF