Module 1: Introduction to Data Science
This module provides an overview of data science, introducing key concepts, tools, and techniques. Students will learn about the data science lifecycle, including data collection, cleaning, and exploration. The module also covers the ethical considerations in data science and the role of a data scientist in various industries.

Module 2: Statistical Analysis and Probability
In this module, students will delve into statistical methods and probability theory, which are foundational to data science. Topics include descriptive statistics, inferential statistics, hypothesis testing, and probability distributions. Students will learn to apply these concepts to real-world data sets and interpret statistical results.

Module 3: Data Wrangling and Preparation
This module focuses on the practical skills needed to clean, transform, and prepare data for analysis. Students will work with various data formats, handle missing data, and perform data normalization and transformation. The module emphasizes the importance of data quality and prepares students to work with large and complex data sets.

Module 4: Data Visualization and Communication
In this module, students will learn how to effectively visualize data using tools like Tableau, Power BI, and Python libraries such as Matplotlib and Seaborn. The module covers best practices in data visualization and how to communicate findings clearly and effectively to non-technical audiences.

Module 5: Machine Learning Fundamentals
This module introduces students to the basics of machine learning, including supervised and unsupervised learning algorithms. Topics include linear regression, classification, clustering, and decision trees. Students will gain hands-on experience by building and evaluating machine learning models using Python and other relevant tools.

Module 6: Big Data and Cloud Computing
This module explores the concepts of big data and the use of cloud computing in data science. Students will learn about distributed computing, Hadoop, Spark, and cloud platforms like AWS and Google Cloud. The module covers how to handle and analyze large-scale data sets using these technologies.

Module 7: Advanced Topics in Data Science
This module covers advanced topics such as deep learning, natural language processing, and reinforcement learning. Students will explore cutting-edge techniques and tools in the field of data science. The module also includes case studies and projects that allow students to apply advanced methods to real-world problems.

Module 8: Capstone Project
In the capstone project, students will apply all the knowledge and skills they have acquired throughout the program. They will work on a comprehensive data science project, from data collection and preparation to analysis and presentation. The project will be an opportunity to showcase their ability to solve complex problems using data science techniques.