Course Overview
This foundational MITx course introduces learners to computer science principles and computational thinking using Python.
The program is designed for students with little to no prior programming experience and focuses on problem-solving, algorithmic thinking, data modeling, and writing efficient code.
By the end of the course, students will be able to analyze problems computationally, build Python programs to solve them, and understand core CS concepts that serve as preparation for more advanced programming and data science studies.
Learning Objectives
Learners will be able to:
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Understand the basics of computer science and computational thinking
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Write Python programs using variables, conditionals, loops, and functions
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Apply problem-solving approaches using algorithms
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Work with simple data structures (lists, strings, dictionaries)
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Use recursion and understand algorithmic complexity
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Model and simulate real-world problems computationally
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Use debugging and testing strategies effectively
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Understand object-oriented programming fundamentals
Course Structure
| Module | Title | Description |
|---|---|---|
| 1 | Introduction to Computation | Importance of computing, what computers can do, computational problem-solving |
| 2 | Python Basics | Variables, expressions, basic data types, input/output |
| 3 | Control Flow | Conditionals, loops, iterations, program structure |
| 4 | Functions & Abstraction | Function design, parameters, return values, modular programming |
| 5 | Simple Data Structures | Strings, lists, tuples, dictionaries, mutable vs immutable types |
| 6 | Algorithms & Problem-Solving | Searching, sorting, algorithmic thinking |
| 7 | Recursion | Recursive functions, problem decomposition |
| 8 | Object-Oriented Programming | Classes, objects, methods, inheritance |
| 9 | Debugging & Testing | Debugging techniques, unit testing basics |
| 10 | Computational Models & Simulation | Modeling real-world problems, probabilistic simulations |
| 11 | Complexity & Optimization | Big-O notation, performance considerations |
| 12 | Final Project | Build a Python program that solves a real-world computational problem |
Course Duration
| Mode | Duration |
|---|---|
| Self-paced | 9–12 weeks (8–10 hours per week) |
| Instructor-paced (when available) | 6 weeks (10–12 hours per week) |
Assessment
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Weekly programming exercises
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Problem sets (graded)
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Quizzes
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Final programming project
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Auto-graded Python tasks
Prerequisites
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High school level algebra
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No prior programming experience needed
Included Materials
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MIT-provided lecture videos
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Programming exercises and autograders
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Example code files
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Problem-set templates
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Readings and lecture notes
Certification
MITx Verified Certificate in Introduction to Computer Science and Programming Using Python
Issued upon successful completion of all assignments and the final project.
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