Python for Data Analysis

Detailed syllabus for the Python for Data Analysis course.

• Introduction to Python for Data Analysis
  • Setting up Python environment and libraries
  • Data types, data structures, and variables
• Data Manipulation with Pandas
  • DataFrames and Series: Creating, merging, and joining
  • Cleaning data, handling missing values
  • Groupby operations and aggregations
• Data Visualization with Matplotlib and Seaborn
  • Creating bar charts, line charts, scatter plots
  • Histograms and box plots for distribution analysis
  • Customization of visual elements
• Data Cleaning and Preprocessing
  • Handling duplicates, outliers, and transformations
  • Data normalization and standardization
  • Data encoding for machine learning models
• Exploratory Data Analysis (EDA)
  • Understanding data distributions
  • Feature engineering and feature selection
  • Using visualizations for insights