Training on Big Data Analytics Using Python

Python has been one of the most adaptable, and robust open-source languages that are easy to learn and uses powerful libraries for data manipulation and analysis. For many years now, Python has been used in scientific computing and mathematical domains such as physics, finance, oil and gas, and signal processing. This Big Data Analytics with Python course provides a complete overview of data analysis techniques using Python. The Big Data Analytics with Python course teaches you to master the concepts of Python programming. Through this training, you will gain knowledge of the essential tools of Data Analytics with Python.

Day 1: Introduction to Big Data and Python

• Morning:
  • Welcome and Introduction to the Course
  • Overview of Big Data and its Challenges
  • Introduction to Python for Data Analytics
  • Setting up Python Environment (Anaconda, Jupyter Notebook)
• Afternoon:
  • Basic Python Programming Concepts (Variables, Data Types, Loops, Functions)
  • Introduction to Pandas (DataFrames and Series)

Day 2: Data Manipulation and Preprocessing

• Morning:
  • Data Cleaning and Handling Missing Values
  • Data Transformation (e.g., filtering, sorting, merging)
  • Data Visualization with Matplotlib and Seaborn
• Afternoon:
  • Introduction to NumPy for Numerical Operations
  • Exploratory Data Analysis (EDA)
  • Case Study: Exploring a Real-world Dataset

Day 3: Big Data Tools and Distributed Computing

• Morning:
  • Introduction to Big Data Technologies (Hadoop, Spark)
  • Overview of HDFS (Hadoop Distributed File System)
  • Setting up a Hadoop/Spark Cluster (Local or Cloud)
• Afternoon:
  • Introduction to PySpark
  • Working with RDDs (Resilient Distributed Datasets)
  • Basic Data Processing with PySpark

Day 4: Advanced Data Analytics with Python

• Morning:
  • Machine Learning with Scikit-Learn
  • Model Training and Evaluation
  • Feature Engineering
• Afternoon:
  • Introduction to Deep Learning with TensorFlow/Keras
  • Neural Networks and Deep Learning Concepts
  • Hands-on Deep Learning Exercise

Day 5: Big Data Analytics and Conclusion

• Morning:
  • Large-scale Data Processing with PySpark
  • Building Data Pipelines
  • Real-time Data Processing (Optional)
• Afternoon:
  • Final Project: Applying Big Data Analytics on a Real Dataset
  • Presentation of Projects
  • Q&A Session
  • Course Conclusion and Certification

Methodology

The instructor led trainings are delivered using a blended learning approach and comprises of presentations, guided sessions of practical exercise, web-based tutorials and group work. Our facilitators are seasoned industry experts with years of experience, working as professional and trainers in these fields.