Data Analytics Gen AI Final Detailed Syllabus Table (5 Months / 20 Weeks)

Month 1 – Foundations of Data & Excel
Module 1: Statistical Analysis (Weeks 1–2)

•  Introduction to Analytics | DA vs DS vs AI | Industry Use Cases
  Descriptive Stats: Mean, Median, Variance, Skewness, Correlation
  Probability: Normal, Binomial, Poisson, Bayes’ Theorem (critical for risk modeling)
  Inferential Stats: Sampling, Confidence Intervals, Hypothesis Testing
  Central Limit Theorem | P-values | Type I & II Errors
  Experimental Design & A/B Testing (added – must-have for digital/marketing analysts)
  New Critical Topic: Causal Inference & Business Experiments (used in finance & growth analytics)
  Data Cleaning & Preparation Best Practices
  GenAI: GPT for Stats Explanation, Auto-Simulations, AI-Generated Test Scenarios

Module 2: Advanced Excel & Google Sheets (Weeks 3–4)

• Functions: IF, AND, OR, Lookup (VLOOKUP, HLOOKUP, INDEX-MATCH, XLOOKUP)
  Advanced Formulas: Array, Dynamic Arrays, TEXT, DATE, LOGICAL (Excel 365 trending)
  Pivot Tables, Pivot Charts, Power Query (critical enterprise BI skill)
  Dashboards: KPI Cards, Drill-Through Reports, Interactive Filters
  VBA Macros for Automation (added – demanded in banking & consulting roles)
  Excel Copilot / Google Duet AI for Auto Insights & Narratives
  New Critical Topic: Spreadsheet Modeling for Finance & Operations
  Project: AI-Powered Sales Dashboard

Month 2 – Databases & SQL
Module 3: SQL for Analytics (Weeks 5–6)

• ER Models, Normalization | Relational Database Design
  SQL Queries: SELECT, WHERE, ORDER BY, DISTINCT
   Aggregations: SUM, AVG, COUNT, MIN, MAX
   Joins: INNER, LEFT, RIGHT, FULL OUTER, CROSS
   Subqueries | CTEs | Nested Queries
   Window Functions: RANK, ROW_NUMBER, LAG, LEAD (for retention & churn analysis)
   Transactions, Views, Stored Procedures (real-world enterprise workflows)
  Query Optimization: Indexing, Execution Plans
  New Critical Topic: NoSQL Basics (MongoDB/DocumentDB) (added – big data exposure)
  New Critical Topic: Data Security & Governance in SQL (critical in BFSI & healthcare)
  GenAI: GPT for Query Debugging, EverSQL AI for Optimization
  Project: Marketing Campaign Effectiveness with SQL

Month 3 – Programming & Visualization
Module 4: Python for Analytics (Weeks 7–8)

• Python Basics: Data Types, Loops, Functions
  OOP Concepts | Error Handling (often tested in interviews)
  NumPy (Math, Random, Broadcasting)
  Pandas (Wrangling, GroupBy, Merge, Pivot, Time Series)
  Data Cleaning: Missing Values, Outliers, Normalization
  Exploratory Data Analysis (EDA) | Business Simulations
  Visualization: Matplotlib, Seaborn, Plotly (interactive plots – Tableau/Power BI integration)
  New Critical Topic: APIs in Python for Data Ingestion (REST, JSON, CSV feeds)
  New Critical Topic: Web Scraping & Real-Time Data Pipelines (Requests, BeautifulSoup)
  GenAI: PandasAI for Auto-EDA, GPT for Code Debugging
  Project: Customer Segmentation

Module 5: Data Visualization & Storytelling (Weeks 9–10)
Power BI

•  Architecture | Data Import | Power Query
   Relationships (Star, Snowflake) | DAX: CALCULATE, FILTER, SWITCH
   Charts: Bar, Line, TreeMap, Decomposition, Gauges
   Advanced Analytics: Smart Narrative, Key Influencers (AI-driven visuals)
   Power BI Service | DirectQuery | Live Connection
   New Critical Topic: Row-Level Security (RLS) & Governance in BI

Tableau

•  Connecting Data (Excel, SQL, Cloud)
   Extracts vs Live | Joins, Blends, Unions
   Calculated Fields, Parameters, LOD Expressions
   Forecasting, Trend Lines, Reference Lines
   Dashboards | Drill-Down Reports | Storyboards
   New Critical Topic: Embedded Analytics (Tableau in Enterprise Apps)
   GenAI: Tableau GPT for Auto-Summaries & Storytelling
   Project: Interactive AI-Driven Dashboard

Month 4 – Generative AI & Machine Learning
Module 6: Generative AI for Analysts (Weeks 11–12)

•  LLMs: GPT, Claude, Gemini | Enterprise Use Cases
   Prompt Engineering: Zero-shot, Few-shot, Chain-of-Thought
   AI for Data Cleaning & Anomaly Detection
   AI-Powered EDA Automation | Auto-Generated Reports
   LangChain Basics | Retrieval-Augmented Generation (RAG)
   New Critical Topic: AI Agents for Analytics Automation (AutoGPT, CrewAI)
   New Critical Topic: Ethics, Bias & Responsible AI in Analytics (companies demand this awareness)
   Project: AI-Powered Exploratory Analysis

Module 7: Machine Learning Basics (Weeks 13–14)

•  Regression: Linear, Multiple, Lasso, Ridge
   Classification: Logistic, Decision Tree, Random Forest
   Clustering: K-means, Hierarchical, DBSCAN
   Time Series: ARIMA, Prophet (essential for forecasting in retail/finance)
   Model Evaluation: Accuracy, Precision, Recall, F1, ROC, AUC
   Explainable AI (SHAP, LIME) (critical for BFSI & healthcare compliance)
   New Critical Topic: Recommendation Systems (E-commerce/Media)
   New Critical Topic: Feature Engineering Best Practices
   AutoML: Google AutoML, Azure ML Studio
   GenAI: GPT for ML Pipeline Automation
   Project: Churn Prediction / Sales Forecasting

Month 5 – Cloud, Collaboration & Capstone
Module 8: Cloud & Collaboration (Weeks 15–16)

•  BigQuery: Large-Scale Querying
   Snowflake: Cloud Data Warehousing | Schemas
   AWS S3: Data Lake Basics
   GitHub: Version Control, Branching, Pull Requests
   New Critical Topic: CI/CD for Analytics Pipelines (GitHub Actions, Jenkins basics)
   New Critical Topic: DataOps & MLOps Fundamentals (added – modern analytics workflows)
   Collaboration: ChatGPT Teams, GitHub Copilot
   Cloud Deployment of Dashboards
   GenAI: AI-Assisted Cloud Querying & Data Governance
   Project: Cloud-Hosted Analytics

Module 9: Capstone Project (Weeks 17–20) End-to-End GenAI-Powered Analytics Project:

•  E-commerce: Sales Forecasting & Recommendation Engine
   Finance: Fraud Detection & Risk Analytics with Explainable AI
   Healthcare: Patient Outcome Prediction + AI Storytelling Dashboard
   Marketing: Campaign ROI & Attribution with GenAI Narratives
   Banking: Loan Default Prediction with Responsible AI Insights Final Capstone Showcase & Industry Pitch Presentation

Value Added Coures ( Extra)

Module 8: R & RStudio for Analytics

•  Introduction to R & RStudio – Setup, Packages, IDE Overview
   R Basics – Data Types, Vectors, Lists, Factors
   R Programming – Loops, Functions, Apply Family
   Data Wrangling with dplyr & tidyr
   Data Import/Export – CSV, Excel, Databases
   Data Visualization with ggplot2
   Advanced Visualization – Facets, Themes, Interactive Plots
   Statistical Analysis in R – Hypothesis Testing, Correlation, Regression
   R for Business Analytics – Forecasting, Time Series
   Project: Business Insights Dashboard in R (ggplot2 + Shiny)