Section outline

• Select section Day 1: Introduction to Python Programming

  Day 1: Introduction to Python Programming

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  • Introduction to Python (1 Hour)
    • Overview of Python and its applications.
    • Setting up the Python environment.
  • Control Structures (2 Hours)
    • Conditional statements and loops.
  • Functions and Modules (2 Hours)
    • Defining and calling functions, using modules.
  • Data Structures (2 Hours)
    • Lists, tuples, dictionaries, and sets

  Day 1 Assignment: Write a Python script using loops and functions.
• Select section Day 2: Data Manipulation and Visualization

  Day 2: Data Manipulation and Visualization

  • Introduction to NumPy (1.5 Hours)
    • Array operations and indexing.
  • Data Manipulation with Pandas (2.5 Hours)
    • DataFrames, data cleaning, and preprocessing.
  • Data Visualization with Matplotlib (2 Hours)
    • Basic plots and customization.
  • Introduction to Seaborn (1 Hour)
    • Advanced visualizations.

  Day 2 Project: Load, clean, and visualize a dataset using Pandas, Matplotlib, and Seaborn.

  • Day 2 Project: Load, clean, and visualize a dataset using Pandas, Matplotlib, and Seaborn.
• Select section Day 3: Basic Statistics with Python

  Day 3: Basic Statistics with Python

  • Descriptive Statistics (2 Hours)
    • Measures of central tendency and dispersion.
  • Probability and Distributions (2 Hours)
    • Probability concepts and distributions.
  • Hypothesis Testing (2 Hours)
    • Formulating hypotheses and conducting tests.
  • Correlation and Regression (1 Hour)
    • Analyzing and visualizing relationships.

  Day 3 Assignment: Conduct statistical analysis, including hypothesis testing and regression.