Module 1: Introduction to Data Science and Python Basics - Overview of Data Science and its applications - Introduction to Python programming language - Python data types, variables, and basic operations - Control structures: loops and conditionals - Functions and modules in Python Module 2: Data Manipulation and Analysis with Pandas - Introduction to Pandas library for data manipulation - Reading and writing data with Pandas - Data cleaning and preprocessing techniques - Data exploration and descriptive statistics - Data aggregation and grouping Module 3: Data Visualization with Matplotlib and Seaborn - Introduction to data visualization - Basic plotting with Matplotlib - Advanced visualization techniques with Seaborn - Customizing plots and adding annotations - Interactive visualization with Plotly Module 4: Machine Learning Fundamentals - Introduction to machine learning concepts - Supervised learning: regression and classification - Unsupervised learning: clustering and dimensional reduction - Model evaluation and validation techniques - Introduction to model selection and hyper-parameter tuning Module 5: Advanced Topics in Data Science - Time series analysis and forecasting - Natural Language Processing (NLP) with NLTK and SpaCy - Deep learning fundamentals with TensorFlow or PyTorch - Big Data processing with Apache Spark - Deployment of machine learning models Module 6: Capstone Project - Applying data science techniques to solve real-world problems - Formulating a project proposal and defining objectives - Data collection, cleaning, and preprocessing - Model development, evaluation, and refinement - Presentation of project findings and conclusions Module 7: API Through Python (Application Programming Interface) - Introduction to APIs and their role in data communication - Types of APIs: RESTful, SOAP, GraphQL - Making HTTP requests and handling responses in Python - Building API endpoints with Flask or Django - Deploying APIs to cloud platforms like Heroku or AWS