Session 1: Data Collection and Preparation

• Importing and Exporting Data
• Reading data from CSV, Excel, SQL databases, and APIs
• Mathematical & statistical operations
• Reshaping and stacking arrays
• Hands-on Exercise: Solving mathematical problems using NumPy

Session 2: Data Manipulation and Transformation

• Filtering and selecting data from DataFrames
• Sorting and ranking data
• Grouping data and applying aggregate functions (groupby(), agg())
• Merging, joining, and concatenating datasets
• Hands-on Exercise: Checking for missing values, modifying data types, and sorting DataFrames

Session 3: Data Aggregation & Multi-Indexing

• Aggregating data using groupby()
• Multi-indexing for hierarchical data
• Hands-on Exercise:
  • Combining two datasets using joins
  • Grouping data by categorical variables and computing statistics

Session 4: Exploratory Data Analysis (EDA)

• Understanding the structure of datasets
• Displaying and summarizing data using Pandas (head(), describe(), info())
• Handling missing data (imputation, dropping rows/columns)
• Data Cleaning: Removing duplicates, correcting data types
• Feature Engineering: Creating new features, scaling, encoding categorical variables
• Hands-on Exercise: Univariate, Bivariate, and Multivariate Data Analysis Case Studies