Module 2: Python for Data Analysis

Topics Covered

• Introduction to Python

  Python is a versatile and beginner-friendly programming language widely used in Data Science. Key features include:

  • Simple syntax
  • Extensive libraries like NumPy, Pandas, and Matplotlib
  • Large community support
• Pandas: DataFrames and Series

  Pandas is a powerful library for data manipulation. Key concepts include:

  • DataFrames: Tabular data structures
  • Series: One-dimensional labeled arrays
  • Operations like merging, grouping, and filtering
• NumPy for Numerical Computations

  NumPy is essential for numerical computations in Python. Key features include:

  • Efficient array operations
  • Mathematical functions like trigonometry and linear algebra
  • Random number generation
• Data Visualization with Matplotlib and Seaborn

  Data visualization libraries like Matplotlib and Seaborn help in creating beautiful plots:

  • Matplotlib: Low-level plotting library
  • Seaborn: High-level interface for statistical graphics
  • Common charts: Line plots, bar charts, and histograms