Data Analysis Using Python

Course Description
Python is a high-level, general-purpose programming language created by Guido van Rossum
and first released in 1991. It was designed with an emphasis on code readability, simplicity,
and versatility, making it one of the most widely used languages in data science, web
development, automation, artificial intelligence, and scientific computing.
This training equips learners with practical Python skills for data analysis, from basic
programming to advanced data manipulation and visualization. The course is structured into
logical modules delivered over multiple sessions, with each session building on the previous
one.

Course Objectives

By the end of the training participants will be able to:

• Write and execute Python code confidently
• Use Python data structures to store, retrieve, and manipulate data
• Work with NumPy for numerical operations
• Use Pandas for data cleaning, manipulation, and analysis
• Visualize data using Matplotlib and Seaborn
• Perform basic regression analysis
• Conduct an end-to-end Exploratory Data Analysis (EDA) project and generate
  insights

Module 1: Introduction & Setup

1. Why Python for Data Analysis:

• Popularity and applications
• Comparison with R, Excel, SPSS

2. Installing Anaconda and setting up the enviroment

• Navigator walkthrough
• Opening Jupiter Notebook/ Vscode

3. Python Basics

• Syntax variables
• Data types overview
• print(), comments
• Basic input/ output

Learning Activities

• Hands-on installation
• Running your first Python script: print ("Hello, Data Analysis!")

Module 2: Python Basics

• Data Types: string, int, float, bool
•  Operators: arithmetic, comparison, logical
•  Conditional statements: if, elif, else
•  Loops: for, while
•  Functions: definition, parameters, return values

Module 3: Data Structures

• Lists: creation, indexing, slicing, methods
• Tuples: immutability & use cases
• Sets: uniqueness, set operations
• Dictionaries: key-value storage
• Nested structures
• List comprehensions

Module 4: NumPy for Numerical Computing

• Introduction to NumPy arrays
• Creating arrays (arange, linspace, zeros, ones)
• Indexing and slicing
• Reshaping arrays
• Basic mathematical operations
• Statistical operations: mean, sum, std
• Vectorized operations
• Broadcasting
• Common NumPy functions
• Converting lists to NumPy arrays
• Manipulating matrices
• Applying descriptive statistics

Module 5: Pandas for Data Handling

• Introduction to Series and DataFrames
• Importing data (CSV, Excel)
• Selecting rows/columns
• Filtering and sorting
• GroupBy operations
• Aggregations (mean, sum, count)
• Handling missing values (dropna, fillna)

Module 6: Data Cleaning & Transformation

• Renaming columns
•  Data type conversion
•  Handling duplicates
•  Missing value imputation
• Apply(), map(), lambda functions
• Merging DataFrames
• Concatenating datasets

Module 7: Data Visualization

• Matplotlib: line, bar, scatter, histograms
•  Seaborn plots:
   Boxplot
   Heatmap
   Distribution plots
•  Plot labels, titles, legends
•  Customizing chart aesthetics

Module 8: Regression Analysis (Binomial & Linear)

• What is regression analysis?
• Simple linear regression using scikit-learn
•  Interpreting slope, intercept, R²
•  Logistic (binomial) regression for binary outcomes
• Evaluating model accuracy (confusion matrix, accuracy score)