Module 1: Introduction to Data Science & AI

Duration: 1 Week

• What is Data Science?
• Real-life applications (Netflix, Amazon, Healthcare, etc.)
• Difference between Data Science, AI & Machine Learning
• Role of a Data Scientist
• Career scope & roadmap
• Setting up the right mindset and tools

Module 2: Tools of the Trade

Duration: 2 Weeks

• Setting up Python (Anaconda, Jupyter Notebook)
• Introduction to R (optional but beneficial)
• Version Control with Git & GitHub
• Using Google Colab & VS Code
• Introduction to APIs, Web scraping tools
• Basic CLI (Command Line Interface)

Module 3: Python Programming for Data Science

Duration: 3 Weeks

• Python Basics (Variables, Data Types, Loops, Functions)
• List, Dictionary, Tuple, Set – Real-world use cases
• Pandas for data handling
• NumPy for numerical computing
• Working with Dates and Times
• Creating simple projects using Python

Module 4: Data Handling & Preprocessing

Duration: 2 Weeks

• Importing & exporting data (CSV, Excel, JSON)
• Cleaning messy data
• Handling missing values
• Data normalization and standardization
• Feature engineering basics
• Exploratory Data Analysis (EDA) with Pandas & Matplotlib

Module 5: Data Visualization

Duration: 2 Weeks

• Why visualization matters in decision-making
• Matplotlib & Seaborn: Static plots
• Plotly & Dash: Interactive dashboards
• Heatmaps, histograms, scatter plots, pie charts
• Creating your own mini dashboard project

Module 6: Statistics & Probability

Duration: 2 Weeks

• Descriptive vs Inferential Statistics
• Mean, Median, Mode, Variance, Standard Deviation
• Probability basics (coin toss, dice, cards, etc.)
• Distributions: Normal, Binomial, Poisson
• Hypothesis Testing & Confidence Intervals
• Real-life application-based examples

Module 7: Introduction to Machine Learning (ML)

Duration: 3 Weeks

• What is ML? Supervised vs Unsupervised Learning
• ML Workflow: From data to model
• Linear Regression, Logistic Regression
• KNN, Decision Trees, Random Forest
• Naïve Bayes, SVM (Support Vector Machine)
• Hands-on with Scikit-Learn

Module 8: Deep Dive into Artificial Intelligence

Duration: 3 Weeks

• What is AI? How it works with Data Science
• Types of AI: ANI, AGI, ASI
• Neural Networks basics
• Perceptron, activation functions
• Building a simple AI model with TensorFlow or Keras
• Ethics of AI & bias in algorithms

Module 9: Deep Learning Essentials

Duration: 3 Weeks

• Deep Neural Networks
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs) & LSTM
• Transfer Learning
• Building AI models for image, audio, and text
• Real-world mini projects (e.g., face detection, sentiment analysis)

Module 10: Natural Language Processing (NLP)

Duration: 2 Weeks

• Introduction to NLP and real-life applications
• Text pre-processing (tokenization, stemming, lemmatization)
• Sentiment analysis
• Chatbot basics
• Using Hugging Face & BERT (intro level)

Module 11: Big Data & Cloud Integration

Duration: 2 Weeks

• What is Big Data? How Data Science handles it
• Intro to Hadoop, Spark (basics)
• Working with AWS / Google Cloud / Azure (basic level)
• Deploying ML models on cloud

Module 12: SQL for Data Science

Duration: 1 Week

• Why SQL still matters
• SELECT, JOIN, GROUP BY, Subqueries
• Real-world queries on sample databases
• Integrating SQL with Python

Module 13: Projects, Capstone & Portfolio Building

Duration: 3 Weeks

• Real-world projects:
  • Sales prediction model
  • Customer churn analysis
  • Stock price prediction using ML
  • AI-based recommendation engine
• Building a GitHub portfolio
• Resume building for Data Scientist roles
• Interview questions + mock interview practice