Training on Big Data Analytics with Python

This course provides participants with the skills to handle, analyze, and visualize large datasets using Python. Through practical sessions, participants will learn how to use Python’s big data libraries and tools to process massive datasets, perform analytics, and extract actionable insights for decision-making.

Duration

10 Days

Who Should Attend

• Data analysts and scientists

• IT and business intelligence professionals

• Monitoring and evaluation specialists

• Anyone working with large-scale data and analytics

Personal Impact

• Enhance proficiency in Python for big data analytics.
• Develop skills for data manipulation, analysis, and visualization.
• Gain hands-on experience with real-world big data projects.
• Improve ability to derive insights from large datasets.

Organizational Impact

• Promote advanced data analytics practices within the organization.
• Enhance decision-making through data-driven insights.
• Ensure effective utilization of big data technologies.
• Improve overall data management and analytics capabilities.

By the end of the course, participants will be able to:

• Understand the fundamentals of big data analytics with Python.
• Learn data manipulation techniques using pandas.
• Develop skills for data visualization with matplotlib and seaborn.
• Gain proficiency in big data processing with PySpark.
• Explore machine learning applications with Python.
• Understand best practices for handling large datasets.
• Learn to implement scalable data analytics solutions.
• Gain insights into practical applications of big data analytics.
• Develop skills for working with unstructured data.
• Enhance ability to communicate data insights effectively.

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