Data Analysis Using Python

Course Description

The Data Analysis Using Python course equips learners with practical skills to analyze and visualize data using Python, one of the most versatile programming languages. Designed for students, researchers, and professionals, this course covers everything from basic Python programming to advanced data manipulation and visualization techniques. Structured into logical, progressive modules, each session builds on the previous one to ensure learners gain confidence and hands-on experience applying Python to real-world data analysis tasks in fields like data science, research, and business analytics.

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