Python for Data Science Course Content

Module 1: Introduction to Python & Environment Setup

Topics

• Introduction to Python

• Why Python for Data Science?

• Installing Python and Jupyter Notebook

• Using VS Code and Jupyter

• Variables and Data Types

• Input and Output Operations

Hands-on

• Calculator Program

• Temperature Converter

• Unit Conversion Program

Module 2: Operators, Conditional Statements & Loops

Topics

• Arithmetic Operators

• Comparison Operators

• Logical Operators

• Conditional Statements (if, elif, else)

• For Loops

• While Loops

• Break, Continue, Pass

Hands-on

• Prime Number Checker

• Multiplication Table Generator

• Pattern Printing Programs

Module 3: Python Data Structures

Topics

• Strings

• Lists

• Tuples

• Sets

• Dictionaries

• Common Built-in Functions

Hands-on

• Student Marks Management System

• Word Frequency Counter

• Duplicate Removal Program

Module 4: Functions & Exception Handling

Topics

• User-Defined Functions

• Function Parameters

• Return Statements

• Lambda Functions

• Map, Filter

• Exception Handling (try, except, finally)

Hands-on

• Password Validator

• Age Calculator

• Data Validation Functions

Module 5: NumPy for Data Science

Topics

• Introduction to NumPy

• Arrays and Array Creation

• Indexing and Slicing

• Array Operations

• Broadcasting

• Aggregations and Statistics

• Reshaping Arrays

Hands-on

• Matrix Operations

• Statistical Analysis

• Data Normalization

Module 6: Pandas Fundamentals

Topics

• Introduction to Pandas

• Series and DataFrames

• Reading CSV and Excel Files

• Selecting Rows and Columns

• Sorting and Filtering Data

• Basic Data Exploration

Hands-on

• Employee Dataset Analysis

• Customer Dataset Exploration

Module 7: Data Cleaning & Preprocessing

Topics

• Handling Missing Values

• Removing Duplicates

• Data Type Conversion

• String Operations

• Datetime Operations

• Feature Creation

Hands-on

• Cleaning Sales Data

• Preparing Data for Analysis

Module 8: Data Analysis with Pandas

Topics

• GroupBy Operations

• Aggregations

• Pivot Tables

• Merge, Join, Concatenate

• Exploratory Data Analysis (EDA)

Hands-on

• Customer Segmentation Analysis

• Sales Performance Analysis

Module 9: Data Visualization

Topics

• Introduction to Matplotlib

• Introduction to Seaborn

• Line Charts

• Bar Charts

• Histograms

• Box Plots

• Scatter Plots

• Heatmaps

Hands-on

• Visualizing Business Data

• EDA using Visualization Techniques

Module 10: Statistics for Data Science

Topics

• Mean, Median, Mode

• Variance and Standard Deviation

• Percentiles and Quartiles

• Correlation

• Probability Basics

• Normal Distribution

Hands-on

• Statistical Analysis of Real-world Data

• Correlation Analysis

Tools & Libraries Covered

• Python

• Jupyter Notebook

• NumPy

• Pandas

• Matplotlib

• Seaborn

Learning Outcomes

By the end of the course, learners will be able to:

• Write Python programs confidently

• Work with Python data structures and functions

• Perform numerical computing using NumPy

• Clean and preprocess data using Pandas

• Analyze datasets using Pandas

• Create insightful visualizations using Matplotlib and Seaborn

• Apply statistical concepts to real-world datasets

• Perform exploratory data analysis (EDA) independently

• Build a strong foundation for further learning in Machine Learning and Data Science