MONTH 1 — INTRODUCTION TO DATA
SCIENCE + FOUNDATIONS
WEEK 1: Introduction to Data Science
● What is Data Science
● Data Science vs AI vs ML
● Real-world applications
● Data-driven thinking
Activity: “Data in Daily Life Mapping”

WEEK 2: Data & Lifecycle
● Types of data
● Data Science lifecycle
● Problem → Data → Insight flow
Activity: “Social Media Data Journey”

WEEK 3: Statistics Basics
● Mean, median, mode
● Variance, standard deviation
● Data interpretation
Activity: “Class Survey Analysis”

WEEK 4: Math + Algorithms Thinking
● Basics of Mathematics in DS
● Differentiation (intuition)
● Integration (intuition)
● What is Algorithms
● Logical thinking
Activity: “Human Algorithm Simulation”

MONTH 2–3 — PYTHON FOR DATA
SCIENCE (CORE SKILL BUILDING)
WEEK 5: Python Basics
● Python
● Variables, loops, conditions
Activity: Convert real-life actions into code
WEEK 6: Functions + Logic Building
● Functions
● Problem solving
Activity: Mini coding challenges

WEEK 7: Data Structures
● Lists, tuples, dictionaries
Activity: Student record system project
WEEK 8: NumPy
● Arrays
● Operations
● NumPy
Activity: Fast math array challenge

WEEK 9: Pandas Basics
● DataFrames
● Data loading
● Pandas
Activity: Load and explore dataset

WEEK 10: Data Cleaning
● Missing values
● Filtering
● Transformation
Activity: “Dirty Data Fix Challenge”

WEEK 11: EXPLORATORY DATA
ANALYSIS (EDA) — CORE MODULE
Topics:
● What is EDA
● Summary statistics in data
● Patterns and trends
● Outliers detection
● Correlation (basic idea)
● Basic visualizations
Activity:
● “Data Detective Challenge”
○ Find insights from real dataset
○ Identify patterns & anomalies

WEEK 12: EDA MINI PROJECT
Topics:
● Full EDA workflow
● Insight extraction
● Reporting findings
Activity:
● “EDA Presentation Day”

MONTH 4–MID MONTH 5 — MACHINE
LEARNING CORE
WEEK 13: Introduction to ML
● What is Machine Learning
● Types of ML
Activity: Pattern guessing game

WEEK 14: ML Pipeline
● Data → training → testing → prediction
● Train/test split
Activity: Human ML simulation

WEEK 15: Regression
● Linear regression
● Prediction systems
Activity: Price prediction game

WEEK 16: Classification
● KNN
● Decision trees
Activity: Spam detection game

WEEK 17: Model Evaluation
● Accuracy
● Precision/Recall
● Overfitting
Activity: Model judge competition
WEEK 18 (Half): ML Mini Project
● End-to-end ML project
Activity: First ML submission

MONTH 5 (SECOND HALF) + MONTH 6
(FIRST HALF) — DEEP LEARNING +
SPECIALIZATION
WEEK 19: Deep Learning Basics
● What is Deep Learning
● Neural networks
Activity: Human neural network simulation

WEEK 20: Frameworks
● TensorFlow
● PyTorch
Activity: Simple model demo

WEEK 21: NLP & Computer Vision Intro
● Text analysis basics
● Image recognition basics
Activity: AI in real life showcase

WEEK 22: Applied AI Concepts
● Real-world AI systems
● Industry use cases
Activity: Case study discussion

MONTH 6 (FINAL HALF) — CAPSTONE
PROJECT

WEEK 23: Project Planning
● Dataset selection
● Problem definition
● Workflow design
Activity: Project pitch

WEEK 24: FINAL PROJECT EXECUTION
● Full pipeline:
○ Data collection
○ Cleaning
○ EDA
○ ML model
○ Evaluation
○ Presentation
Activity: Data Science Hackathon + Final Demo