Module 1: Introduction to Data Science

• Overview of Data Science and its Applications
• Data Science Lifecycle
• Key Concepts: Data, Information, Knowledge

Module 2: Data Collection and Preparation

• Data Sources: Structured vs. Unstructured Data
• Web Scraping Techniques
• Data Cleaning and Preprocessing
• Data Wrangling with Pandas

Module 3: Exploratory Data Analysis (EDA)

• Descriptive Statistics and Data Visualization
• Using Matplotlib and Seaborn
• Identifying Patterns and Trends
• Hypothesis Testing

Module 4: Statistical Analysis

• Probability Distributions
• Inferential Statistics
• A/B Testing and Experimental Design
• Regression Analysis

Module 5: Machine Learning Fundamentals

• Introduction to Machine Learning Concepts
• Supervised vs. Unsupervised Learning
• Key Algorithms: Linear Regression, Decision Trees, Clustering

Module 6: Advanced Machine Learning Techniques

• Ensemble Methods: Random Forests, Boosting
• Neural Networks and Deep Learning Basics
• Model Evaluation and Validation Techniques
• Hyperparameter Tuning

Module 7: Data Visualization and Communication

• Principles of Effective Data Visualization
• Tools: Tableau, Power BI, or Advanced Matplotlib
• Creating Dashboards and Reports
• Communicating Insights to Stakeholders

Module 8: Big Data Technologies

• Introduction to Big Data: Hadoop and Spark
• Working with Large Datasets
• NoSQL Databases: MongoDB, Cassandra
• Data Streaming Concepts

Module 9: Deployment and Productionization

• Model Deployment Strategies
• APIs and Web Services
• Monitoring and Maintenance of Models
• Ethical Considerations in Data Science

Module 10: Capstone Project

• Real-world Data Science Project
• Applying Skills from Previous Modules
• Presentation of Findings and Solutions

Additional Resources

• Access to Online Forums and Communities
• Recommended Reading and Tools
• Guest Lectures from Industry Experts