PHASE 1: FOUNDATION
Module 1: Programming Fundamentals (Python)
Variables, data types, control flow, functions, lambda expressions, object-oriented programming basics, file handling, exception handling

Module 2: Git & Version Control
Repositories, commits, branching, and collaborative workflow used by real-world data teams

Module 3: Statistics & Probability
Descriptive statistics, measures of dispersion, probability distributions, hypothesis testing, correlation and causation

PHASE 2: DATA HANDLING
Module 4: SQL for Data Analysis
Joins, subqueries, CTEs, window functions, aggregations, query optimization

Module 5: NumPy & Pandas (Advanced Python)
Array operations, DataFrames, merge/join/groupby, missing value treatment, datetime handling

Module 6: Data Visualization
Matplotlib, Seaborn, Plotly, Power BI, Tableau

Module 7: Exploratory Data Analysis (EDA)
Univariate, bivariate, and multivariate analysis, outlier detection, data storytelling
Project: End-to-end EDA on a real-world dataset

PHASE 3: MACHINE LEARNING CORE

Module 8: Supervised Learning — Regression
Linear Regression, Polynomial Regression, Cost Function, Gradient Descent, overfitting and underfitting
Project: Sales Prediction

Module 9: Supervised Learning — Classification
Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, evaluation metrics (accuracy, precision, recall, F1-score, confusion matrix)
Projects: Retail Classification, Healthcare Classification

Module 10: Unsupervised Learning
K-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA)
Project: Customer Segmentation

Module 11: Model Evaluation & Feature Engineering
Cross-validation, feature scaling, encoding, feature selection, hyperparameter tuning

PHASE 4: ADVANCED AI

Module 12: Deep Learning Foundations
Perceptron, Neural Networks, Activation Functions, Keras, Gradient Descent variants

Module 13: Specialization Track (Computer Vision / NLP / Time Series)
Computer Vision: OpenCV, Convolutional Neural Networks, image classification
Natural Language Processing: Tokenization, word embeddings, TF-IDF, sentiment analysis
Time Series Analysis: Trend and seasonality, forecasting with Facebook Prophet

Module 14: Generative AI & LLMs
How Large Language Models work, prompt engineering, applying GenAI tools in real data workflows

PHASE 5: CAREER READINESS

Module 15: Capstone Project
One end-to-end project mapped to industry domain (Fintech, Healthcare, or Product Analytics)

Module 16: Resume, LinkedIn & Interview Preparation
ATS-friendly resume building, SQL and case-study interview questions, ML theory preparation, mock interviews, HR round preparation