Module 1: Introduction to Data Science

• Overview of Data Science
  • Importance and Applications
  • Data Science Process
• Tools and Technologies
  • Setting Up Development Environment
  • Version Control with Git and GitHub

Module 2: Python Programming for Data Science

• Introduction to Python
  • Basic Syntax and Data Types
  • Control Structures (if, for, while)
  • Functions and Modules
• Data Handling with Python
  • NumPy for Numerical Computations
  • Pandas for Data Manipulation
  • Matplotlib and Seaborn for Data Visualization

Module 3: Advanced Python

• Object-Oriented Programming (OOP)
  • Classes and Objects
  • Inheritance and Polymorphism
• Advanced Data Handling
  • Working with APIs
  • Data Processing with Itertools and Collections
• Python for Data Science Libraries
  • Scikit-Learn for Machine Learning
  • Statsmodels for Statistical Analysis

Module 4: SQL for Data Science

• Introduction to SQL
  • Basic Queries (SELECT, INSERT, UPDATE, DELETE)
  • Joins, Subqueries, and Aggregations
  • Database Design and Normalization
• Advanced SQL
  • Window Functions
  • Common Table Expressions (CTEs)
  • Performance Optimization

Module 5: Excel for Data Analysis

• Excel Basics
  • Data Entry and Formatting
  • Formulas and Functions
• Advanced Excel
  • Pivot Tables and Charts
  • Data Analysis Toolpak
  • VBA Macros for Automation

Module 6: Power BI for Data Visualization

• Introduction to Power BI
  • Setting Up Power BI
  • Connecting to Data Sources
• Building Reports and Dashboards
  • Creating Visualizations
  • DAX (Data Analysis Expressions) Basics
• Advanced Power BI
  • Advanced DAX Functions
  • Power BI Service for Collaboration
  • Custom Visuals

Module 7: Machine Learning (ML)

• Introduction to Machine Learning
  • Supervised vs. Unsupervised Learning
  • Model Evaluation and Validation
• Supervised Learning Algorithms
  • Regression (Linear, Polynomial)
  • Classification (Logistic Regression, Decision Trees, SVM)
• Unsupervised Learning Algorithms
  • Clustering (K-Means, Hierarchical)
  • Dimensionality Reduction (PCA, LDA)
• Model Deployment
  • Saving and Loading Models
  • Deploying Models with Flask/Django

Module 8: Natural Language Processing (NLP)

• Introduction to NLP
  • Text Preprocessing (Tokenization, Lemmatization)
  • Feature Extraction (TF-IDF, Word Embeddings)
• NLP Techniques
  • Sentiment Analysis
  • Named Entity Recognition (NER)
  • Topic Modeling (LDA)
• Advanced NLP
  • Transformers and BERT
  • Building Chatbots

Module 9: Deep Learning (DL)

• Introduction to Deep Learning
  • Neural Networks Basics
  • Activation Functions and Optimizers
• Deep Learning Frameworks
  • TensorFlow Basics
  • Keras for Quick Prototyping
• Advanced Deep Learning
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Generative Adversarial Networks (GANs)

Module 10: Full-Stack Development

• Front-End Development
  • HTML and CSS Basics
  • JavaScript and Front-End Frameworks (React/Vue)
• Back-End Development
  • Building RESTful APIs with Django/Flask
  • Database Integration (SQL/MongoDB)
• Deployment and Cloud
  • Docker and Kubernetes Basics
  • AWS Cloud Services for Deployment
  • CI/CD Pipelines

Module 11: Integrative Project

• Project Planning
  • Defining Requirements and Scope
  • Designing the Architecture
• Implementation
  • Data Collection and Preparation
  • Model Training and Evaluation
• Deployment and Presentation
  • Building a Web Interface
  • Final Deployment and Reporting