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Programming
in Python
The most versatile programming language in the world right now. From data science to automation, Python opens doors in almost every field.
What You'll Learn
A full semester of programming in python β completed at your own pace, with no deadlines or fixed class times.
What You'll Be Able to Do
- Apply Python programming concepts to real-world scenarios β including data types, constructs, I/O operations, object-oriented programming, graphical visualization, functions, and standard modules β to solve complex programming challenges
- Implement decision structures and loop structures proficiently, ensuring effective programming logic and control flow in Python code
- Master algorithm design and data analysis techniques, including fundamental search algorithms, recursion, sorting techniques, and practical applications like the Towers of Hanoi problem, to analyze and solve real-world problems effectively
Major Course Topics
Course chapters and lessons
Programming in Python has 17 chapters across 73 instructional hours, as listed in the course syllabus. Expand any chapter to see the lessons inside it.
- Chapters
- 17
- Lessons
- 82
- Instructional hours
- 73 hrs
- Semester hours
- 3
Chapter 1
Introduction to Programming
- 1.1 Systems Development Life Cycle
- 1.2 Program Design
- 1.3 Program Quality
- 1.4 Pseudocode
- 1.5 Flowcharts
- 1.6 Software Testing
- 1.7 Integrated Development Environment (IDE)
- 1.8 Key Concepts in Software Development: Version Control, Git, and I/O Operations
- 1.9 Introduction to "Hello, World!" Program and Python IDEs
Chapter 2
Introduction to Python: A Versatile Scripting and Modular Language
- 2.1 The Python language
- 2.2 Input/Output
Chapter 3
Introduction to Basic Data Types and Operations in Python
- 3.1 Constants, Variables, and Identifiers
- 3.2 Data types in Python
- 3.3 Strings
- 3.4 Type Conversion in Python: Implicit and Explicit Conversions, Combining Data Types, and Best Practices
Chapter 4
Understanding Python Errors, Comments, and Code Quality
- 4.1 Understanding and Managing Python Errors: A Comprehensive Guide
- 4.2 Comments and Code Quality in Python
- 4.3 Understanding the Python Interpreter and Shell
Chapter 5
Understanding Programming Fundamentals: Assignment, Arithmetic, Relational, and Logical Operations
- 5.1 Fundamental Programming Concepts and Operators
- 5.2 Relational Operators
- 5.3 Logical Operators
- 5.4 Order of Operations
Chapter 6
Decision-Making in Python: If Statements, Elif, Nested Statements, and Conditional Expressions
- 6.1 Control Structures and Structured Programming
- 6.2 If, Elif, and Else Statements
- 6.3 Chained Decisions
- 6.4 Nested Decision Statements in Programming
- 6.5 Conditional Expressions in Python
Chapter 7
Comprehensive Guide to Python Loops and Control Statements
- 7.1 While loop
- 7.2 For loop
- 7.3 Nested loops
- 7.4 Break and Continue
- 7.5 Loop else
Chapter 8
Functions and Control Flow in Python
- 8.1 Understanding Functions in Programming
- 8.2 Understanding Control Flow and Functions in Programming
- 8.3 Understanding Global and Local Scope in Python Functions
- 8.4 Understanding Arguments, Parameters, and Mutability in Python Functions
- 8.5 Understanding Function Return Values and Usage in Python
Chapter 9
Introduction to Python Data Structures: Lists, Tuples, and Dictionaries
- 9.1 List
- 9.2 Tuples
- 9.3 Dictionaries
Chapter 10
Mastering Python Modules: Techniques, Best Practices, and Advanced Concepts
- 10.1 Understanding and Utilizing Python Modules: Defining and Importing Functions for Geometric Calculations
- 10.2 Effective Usage of Python Modules and Best Practices
- 10.3 Managing Module Imports and Name Collisions in Python
- 10.4 Understanding Side Effects and __name__ in Python Modules
- 10.5 Understanding and Utilizing Colors in HTML and Python Modules
- 10.6 Python Modules: Built-in and Third-Party Options
- 10.7 Managing Python Environments and Seeking Help
Chapter 11
Mastering List Operations, Iteration, and Comprehensions
- 11.1 Comprehensive Guide to List Operations and Iteration in Python
- 11.2 Sorting and reversing lists
- 11.3 List Operations and Copying in Python
- 11.4 Working with List-of-Lists in Python
- 11.5 Understanding and Utilizing List Comprehensions in Python
Chapter 12
Mastering Python Dictionaries
- 12.1 Understanding Python Dictionaries: Concepts and Practice
- 12.2 Python Dictionary Creation and Usage
- 12.3 Python Dictionary Operations: Accessing, Modifying, and Managing Items
- 12.4 Conditionals and Loops in Python Dictionaries
- 12.5 Advanced Dictionary Concepts in Python
Chapter 13
Understanding Key Concepts in Object-Oriented Programming (OOP) and Their Implementation
- 13.1 Core Concepts of Object-Oriented Programming (OOP)
- 13.2 Understanding Classes, Instances, and Attributes in Object-Oriented Programming
- 13.3 Understanding __init__() and Instance Methods in Python
- 13.4 Customizing Python with Magic Methods and Operator Overloading
- 13.5 Importing and Using Classes in Python Modules
Chapter 14
Exploring Recursion: Key Concepts and Practical Applications
- 14.1 Understanding Recursion: The Three Towers Problem
- 14.2 Understanding Recursion and Factorial Calculation
- 14.3 Palindromes and List Permutations
- 14.4 Understanding Fibonacci Sequence and Greatest Common Divisor (GCD)
- 14.5 Exploring Recursive Algorithms: Binary Search and the Three Towers Problem
Chapter 15
Understanding and Implementing Inheritance in Python: Concepts and Practices
- 15.1 Understanding Class Relationships and Inheritance in Python
- 15.2 Understanding Subclassing and Inheritance in Python
- 15.3 Advanced Concepts in Object-Oriented Programming: Overriding, Inheritance, and Polymorphism
- 15.4 Hierarchical and Multilevel Inheritance
- 15.5 Understanding Multiple Inheritance and Mixin Classes in Python
Chapter 16
Working with Files in Python: Opening, Reading, Writing, and Handling Errors
- 16.1 File Operations in Python
- 16.2 File Modes for Reading and Writing
- 16.3 Opening Files from Different Locations
- 16.4 Handling Runtime Errors When Reading Files
- 16.5 Raising and Handling Exceptions in Python
Chapter 17
Key Concepts in Data Analysis: Indexing, Slicing, Missing Data, and Visualization
- 17.1 Introduction to Data Science Life Cycle and Tools
- 17.2 Introduction to NumPy Library and Its Operations
- 17.3 Introduction to the Pandas Library for Data Analysis
- 17.4 Exploratory Data Analysis (EDA): Techniques for Data Understanding, Indexing, Slicing, and Handling Missing Data
- 17.5 Essential Concepts and Tools for Data Visualization in Data Science
These chapters come straight from the official course syllabus, which also sets out the graded assignment and the proctored final exam.
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The most versatile programming language in the world right now. From data science to automation, Python opens doors in almost every field.
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Video-Based Lessons
HD video lectures broken into short, focused modules. Watch at your own pace β pause, rewind, replay any time.
Quizzes & Final Exam
Evaluate your understanding after every section with quizzes and a comprehensive final exam that mirrors real college assessment.
Structured College-Level Course
Structured lessons covering every topic β designed as a standard Programming in Python college-level course.
Official Transcript
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