Unlock the power of Python with confidence. Whether you're stepping into programming for the first time or returning for a refresher, Intro to Python Programming offers a practical, student-friendly guide to learning one of today’s most popular and versatile languages. Designed for college learners, this textbook blends clear explanations with hands-on practice using tools like PyCharm and Visual Studio Code. You’ll start with the basics—writing your first “Hello, World!”—and build up to real-world scripting, data processing, and automation skills through engaging labs, walkthroughs, and interactive examples.
Each of the eight modules is designed to guide you from beginner to confident Python programmer, with a focus on writing clean, logical code and understanding how Python solves real problems. You'll explore topics like input and output, control structures, functions, data handling, and scripting—supported by detailed walkthroughs, practice labs, and hands-on demos. Whether you're learning in class or at your own pace, this book provides the structure and support you need to build a strong foundation in Python and apply it to real-world tasks.
Example Curriculum
Course Overview and Welcome
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Module 1 - Section 1 - Getting Started
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Module 1 - Section 2 - The Basics
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Module 1 - Section 3 - Variables and Data Types
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- Variables in Python: Storing and Using Data
- Variable Naming Conventions in Python
- Data Types: The Foundation of Every Value
- Video: Variables, Data Types, and Naming Conventions (3:11)
- Data Type Conversion in Python
- Strings in Python: Working with Text
- String Formatting in Python: Inserting Data into Text
- Video - Strings and String Formatting (4:05)
- Escape Characters in Python: Inserting Special Symbols in Strings
- Demo – Variables and Data Types (4:23)
Module 1 - Section 4 - Operators
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Module 1 - Section 5 - Math Modules
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Module 1 - Section 6 - Input
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Module 1 Labs
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Module 2 - Section 1 - Conditionals
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Module 2 - Section 2 - Loops
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Module 2 - Section 3 - Exception Handling
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Module 2 - Section 4 - Functions
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- Introduction to Functions
- Defining and Calling a Function
- Parameters and Arguments
- Return Values
- Returning Multiple Values from a Function in Python
- Local Variables and Scope
- Combining Functions with Loops and Conditionals
- Best Practices for Writing Functions
- Using Built-in Python Functions
- Demo - RPG Battle Simulator (5:31)
Module 2 - Section 5 - Packages and Modules
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Module 2 Labs
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Module 3 - Section 1 - Introduction to Data Structures
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- What Are Data Structures?
- Lists in Python
- Demo - Lists - Course Registration (3:11)
- Tuples in Python
- Demo - Tuples - Campus Location Lookup (3:39)
- Indexing and Slicing
- Dictionaries in Python
- Demo - Dictionaries - Inventory System (4:11)
- Sets in Python
- Demo - Sets - Class Enrollment (4:12)
- Choosing the Right Data Structure
Module 3 - Section 2 - File Handling
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Module 3 - Section 3 - Working with Data Files
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Section 3 Labs
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- Lab - Course Planner Using Lists
- Lab - Travel Itinerary Tracker Using Tuples
- Lab - Campus Bookstore Inventory Using Dictionaries
- Lab - Club Membership Analyzer Using Sets
- Lab - Student Notes Manager (Text File I/O)
- Lab - Game Inventory Reader (CSV File)
- Lab - Character Profiles Loader (JSON File)
- Lab - User Config Settings Viewer (XML File)
Module 4 - Section 1 - Object-Oriented Programming (OOP)
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Module 4 - Section 2 - Advanced Data Extraction and Transformation
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Module 4 - Section 3 - Python Libraries (Scientific and Data-Focused)
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Module 4 Labs
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- Lab - Character Tracker - OOP
- Lab - Employee Manager – Working with a List of Objects
- Lab - NumPy Power-Ups – Working with Game Score Arrays
- Lab - Fitness Tracker Analytics – Combining NumPy and Pandas
- Lab - Streaming Showdown – Visualizing Viewer Data with Matplotlib
- Lab - Department Dashboard – Visualizing CSV Data with Pandas and Matplotlib
Module 5 - Section 1 - Introduction to Automation with Python
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Module 5 - Section 2 - Tools and Libraries for Automation
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- Introduction to Python Tools and Libraries for System Tasks
- Real-World Automation Using os and sys Modules
- File System Scripting and Command Execution with shutil and subprocess
- Using psutil for Process and System Monitoring
- Demo - System Health Check (4:50)
- Task Scheduling with time, datetime, and schedule
- Automating User and Process Management
- Demo - Scheduled Process Watchdog (macOS & Windows) (4:52)
Module 5 - Section 3 - Logging and System Monitoring
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Module 5 - Section 4 - Automating Network Tasks
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Module 5 - Section 5 - Scheduling and Automating Tasks
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Module 5 - Section 6 - Expanded Network Analysis
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Module 5 - Section 7 - Introduction to Network Subnetting
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Module 5 Labs
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Module 6 - Section 1 - Introduction to Web Interaction in Python
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Module 6 - Section 2 - HTTP Basics
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Module 6 - Section 3 - Making HTTP Requests
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Module 6 - Section 4 - Consuming Public APIs
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Module 6 - Section 5 - Web Scraping with BeautifulSoup
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Module 6 - Section 6 - Challenges, Ethics, and Best Practices
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Module 6 Labs
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Module 7 - Section 1 - Handling Exceptions in Python
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Module 7 - Section 2 - Customizing Error Responses
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Module 7 - Section 3 - Validating Assumptions with assert
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Module 7 - Section 4 - Writing Defensive Code
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Module 7 - Section 5 - Logging and Debugging
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Module 7 Labs
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- Lab – Exception Handling Calculator
- Lab – Exception Handling and Assertions Calculator
- Lab - Creating and Using Custom Exceptions: Character Level Validator
- Lab – Logging in Python: Login Event Tracker
- Lab - Logging File Operations - Error Tracking with Context
- Lab - Challenge – Secure Student Enrollment System
Module 8 - Section 1 - Introduction to Data Processing
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Module 8 - Section 2 - Data Cleaning with Pandas
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Module 8 - Section 3 - Exploratory Data Analysis (EDA)
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Module 8 - Section 4 - Visualizing Data with Matplotlib and Seaborn
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Module 8 - Section 5 - Introduction to Machine Learning
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- What is Machine Learning?
- Supervised vs. Unsupervised Learning
- Introduction to scikit-learn
- Splitting Data into Training and Testing Sets
- Linear Regression and K-Nearest Neighbors (KNN)
- Demo - Predicting Annual Income with Linear Regression (5:41)
- Demo - Predicting Newsletter Signup with K-Nearest Neighbors (KNN) (5:47)
Module 8 - Section 6 - Ethics and Responsible Data Use
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Module 8 Labs
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