MODULE 1: Statistics & Probability

Descriptive statistics, distributions
Probability theory, Bayes' Theorem
Hypothesis testing
Sampling methods

MODULE 2: Mathematics for Machine Learning

Linear algebra: Matrix operations, eigenvalues
Calculus: Derivatives, gradients, optimization
Vector calculus for backpropagation

MODULE 3: Economics & Business Analytics

Microeconomics: Supply, demand, market equilibrium
Macroeconomics: Inflation, GDP, employment
Time series economics
Business KPIs and cost-benefit analytics

MODULE 4: Foundations of Data Science

What is Data Science?

Data Science lifecycle
Ethics in AI and Data Privacy (GDPR, Bias, Fairness

MODULE 5: Data Visualization

EXCEL:

1. Arithmetic Operators, Sort & Filter, Statistical and Mathematical Functions, Conditional Formatting
2. Lookup, Index & Match, Logical, Text Functions, Pivot Tables
3. Data Cleaning, What if analysis, Scenario Management.
4. Charts, Dashboards, Regression, and Forecasting

Power BI:

 Introduction to Power BI Ecosystem:

• Power BI Desktop vs. Power BI Service
• Importing data from Excel, SQL, Web

Data Modelling:

• Relationships, normalization, star schema
• Calculated columns and measures

DAX (Data Analysis Expressions):

• Aggregation functions: SUM, AVERAGE, CALCULATE, FILTER
• Time intelligence functions: TOTALYTD, DATESINPERIOD

Visualization & Interactivity:

• Cards, maps, tree maps, KPI indicators
• Drill-throughs, tooltips, bookmarks

Publishing & Sharing:

• Reports to Power BI Cloud
• Workspace collaboration and refresh schedules

Tableau:

1. Tableau Installation + Introduction to Tableau, Charts and Maps, Fundamentals of Data Visualization and Reporting.
2. Introduction to Calculated Fields, Table Calculations, Aggregations, Granularity and LOD Expressions
3. Introduction to Data Extracts, Filters, Tableau Dashboards, Tableau Storyboards, and Formatting.

Module 6: SQL

Introduction to Databases, ER Models, Schema Design
SQL Basics (SELECT, WHERE, GROUP BY,HAVING, Joins)
Advanced SQL (Window Functions, Indexing, Query Optimization
NoSQL Databases (MongoDB) & SQL in Python (SQLite, PostgreSQL)

Module 7: Python for data science and data handling

Python Basics (Variables, Data Types, Loops, Conditional Statements)
Functions, OOP in Python, Exception Handling
Numpy & Pandas (DataFrames, Data Manipulation, Handling Missing Values)
Data Visualization (Matplotlib, Seaborn, Plotly)

Module 8: Machine Learning and Predictive Modelling

Supervised Learning Regression:

Introduction to Machine Learning, ML Workflow, Bias-Variance Tradeoff
Linear Regression, Polynomial Regression, Feature Engineering
Regularization (Ridge, Lasso, Elastic Net), Model Evaluation Metrics

Supervised Learning Classification:

Logistic Regression, Decision Trees (Gini, Entropy, Pruning)
Random Forest & Ensemble Learning (Bagging, Boosting - XGBoost, LightGBM)
Support Vector Machines (SVM) & Hyperparameter Tuning
Model Optimization & Deployment (Cross-Validation, Grid Search)

Unsupervised Learning and Dimensionality Reduction:

Clustering (K-Means, Hierarchical Clustering
Principal Component Analysis (PCA) & t-SNE
Feature Engineering for Clustering Models
CASE STUDY:

Ensemble Learning and Time Series Analysis:

Bagging (Random Forest, Extra Trees)
Boosting (Gradient Boosting, XGBoost, CatBoost)
Time Series Forecasting (ARIMA, SARIMA, Prophet)

Natural Language Processing:

Text Preprocessing (Tokenization, Lemmatization, Stopwords Removal)
Word Embeddings (TF-IDF, Word2Vec, GloVe)
Named Entity Recognition (NER), Topic Modelling (LDA)
Sentiment Analysis & Chatbot Development

Deep Learning Foundation:

Neural Networks Basics (Perceptron, Activation Functions)
Backpropagation & Optimization Techniques (SGD, Adam)
Implementing Neural Networks with TensorFlow & Keras

MODULE 9: Advanced AI, Cloud Computing And Deployment

Convolutional Neural Networks (CNN):

CNN Architecture (Filters, Pooling, Padding)
Transfer Learning (VGG, ResNet, Inception
Object Detection (YOLO, SSD)
Image Segmentation (U-Net, Mask R-CNN) 

Recurrent Neural Networks (RNN) and LSTMs:

Introduction to Sequence Models & RNNs
LSTMs & GRUs (Gated Recurrent Units
Attention Mechanism in NLP
Case Study:

Reinforcement Learning:

Introduction to Reinforcement Learning (Markov Decision Process)
Q-Learning & Deep Q Networks (DQN)
Policy Gradient Methods & Actor-Critic Models
Case Study:

Model Deployment and Cloud Computing:

Introduction to Cloud Platforms (AWS, GCP, Azure)
Deploying Models using Flask & FastAPI
Serverless Model Deployment (AWS Lambda, Google Cloud Functions)
CI/CD for Machine Learning Models

 Data Engineering and Big Data:

Apache Spark & Hadoop Basics
Data Pipelines & Workflow Orchestration (Airflow, Prefect)
Distributed Computing & Parallel Processing

Advanced AI and Transformers:

Transformers & Self-Attention Mechanisms
BERT, GPT, T5 (Transfer Learning in NLP)
Fine-Tuning Pre-trained Language Models

MODULE 10: Time Series Analysis

Time series decomposition
ARIMA, SARIMA, Holt-Winters
Prophet and LSTM for forecasting

MODULE 11: Business Analytics

 1. Marketing & Retail Analytics

Goal: Use data to improve customer targeting, retention, and sales performance.

Subtopics:

Customer Segmentation (RFM analysis, clustering for customer groups)

Market Basket Analysis (association rule mining, cross-sell/up-sell insights)

Customer Lifetime Value (CLV) prediction

Churn analysis and retention modelling

Campaign performance analysis (A/B testing, uplift modelling)

Pricing and promotion analytics (elasticity, discount strategy, dynamic pricing)

Location and store performance analytics (geospatial analytics for retail

2. Financial & Risk Analytics

Goal: Analyze financial data to monitor performance, manage risk, and detect anomalies.

Subtopics:

Financial statement analysis (ratio analysis, trend analysis)

Credit scoring models (logistic regression, decision trees for credit risk)

Fraud detection techniques (outlier detection, supervised/unsupervised models)

Portfolio risk and return analytics (VaR, Sharpe ratio, Monte Carlo simulation)

Forecasting for financial KPIs (ARIMA, Prophet, LSTM for stock price, revenue forecasts)

Stress testing and scenario analysis

Operational risk analytics (loss event data analysis)

3. Web & Social Media Analytics

Goal: Leverage online data to understand audience behavior, sentiment, and digital engagement.

Subtopics:

Web traffic analysis (user journey, funnel conversion rates)

Clickstream data analytics (session patterns, abandonment analysis)

Social media sentiment analysis (NLP for positive/negative/neutral sentiment)

Topic modelling and trend detection (LDA, NMF on social data)

Engagement metrics and influencer analysis

Campaign analytics (tracking hashtag performance, engagement lift)

Social network analysis (community detection, influencer mapping)

4. Supply Chain & Logistics Analytics

Goal: Optimize supply chain operations and logistics through data insights.

Subtopics:

Demand forecasting (time series models: ARIMA, Prophet)

Inventory optimization (EOQ models, safety stock calculation, ABC analysis)

Supplier performance analytics (scorecards, risk assessment)

Logistics and route optimization (linear programming, heuristics for route planning)

Warehouse analytics (space utilization, picking/packing optimization)

Lead time and delivery performance monitoring

Cost-to-serve and profit margin analytics across channels

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