Training on Big Data Analytics with Python

Module 1: Introduction to Big Data Analytics

• Overview of big data analytics
• Importance of Python in big data
• Key concepts and definitions
• Case Study: Analyzing Customer Behavior Using Big Data

Module 2: Python Basics for Data Analytics

• Python programming fundamentals
• Data structures and libraries
• Setting up the Python environment
• Practical Component: Developing a Simple Data Analytics Application Using Python

Module 3: Data Manipulation with pandas

• Introduction to pandas
• Data cleaning and preparation
• Advanced data manipulation techniques
• Practical : Cleaning and Analyzing a Real-World Dataset Using pandas

Module 4: Data Visualization with matplotlib and seaborn

• Basics of data visualization
• Creating plots with matplotlib
• Enhancing visualizations with seaborn
• Practical : Visualizing Sales Data to Identify Trends and Patterns

Module 5: Introduction to PySpark

• Overview of PySpark
• Setting up PySpark environment
• Working with RDDs and DataFrames
• Case Study: Using PySpark for Large-Scale Data Processing in a Retail Environment

Module 6: Big Data Processing with PySpark

• Data processing workflows in PySpark
• Transformations and actions
• Performance optimization
• Practical : Optimizing Data Processing Workflows for a Social Media Platform Using PySpark

Module 7: Machine Learning with Python

• Introduction to machine learning
• Using scikit-learn for machine learning
• Implementing machine learning models
• Practical: Building a Predictive Model for Customer Churn Using Python

Module 8: Handling Unstructured Data

• Understanding unstructured data
• Techniques for processing unstructured data
• Case Study: Text Mining for Sentiment Analysis in Customer Reviews

Module 9: Practical Projects in Big Data Analytics

• Real-world big data projects
• Applying learned skills to projects
• Project presentations and feedback
• Practical : Capstone Project - Building a Big Data Solution for a Specific Industry Problem

Module 10: Communicating Data Insights

• Best practices for data storytelling
• Creating impactful data reports
• Effective communication of data insights
• Practical : Presenting Data-Driven Insights to Stakeholders in a Business Scenario