Introduction to CS and Programming Using Python

Learning Python is more rewarding when you begin thinking about the problem as well as the code. MIT’s Introduction to CS and Programming using Python introduces computational problem solving through Python 3, with lectures and exercises designed for learners with little or no programming experience. You study how to break a task into steps, represent information and test whether your solution behaves as intended.

The linked material is the Fall 2022 archive of 6.100L on MIT OpenCourseWare. It is a self-study collection of published course resources, not a current classroom place. Its lecture-led format is useful if you want a broad introduction that develops from basic programming into functions, data structures, classes and algorithms.

Course at a glance

What you’ll learn

• Translate an idea into a program: use input, output, conditions and iteration to express a small computational task.
• Organise reusable logic: study decomposition, abstraction and functions instead of repeating every operation.
• Represent collections of information: work with strings, tuples, lists and dictionaries.
• Investigate program behaviour: explore testing, debugging, exceptions and assertions.
• Develop structured solutions: introduce recursion, classes and inheritance.
• Reason about efficiency: study counting operations, complexity and sorting examples.

Skills you’ll gain

• Python programming
• Computational thinking
• Functions and abstraction
• Data structures
• Testing and debugging
• Algorithmic reasoning

A slower-paced introduction with room to practise

6.100L is the full-semester version of MIT’s introductory programming subject. The archive explains that its pace gives new programmers time to practise. Its 26-lecture sequence begins with simple operations, then moves through functions and collections before introducing more advanced structures and reasoning about performance.

The available collection includes lecture videos, notes, lecture code, finger exercises, problem sets and recitations. These give you different ways to revisit a concept. Watching a demonstration can provide context; working with a short example gives you a way to check whether you can use the same idea yourself.

What the free archive includes

MIT OpenCourseWare is open without registration or enrolment. You can view and download the published resources at your own pace. The Problem Set 0 code archive includes standalone Spyder and Visual Studio Code setup guides for local Python practice, including package installation instructions. Follow the applicable guide before attempting the published exercises.

The classroom syllabus references a commercial textbook, which is not included in OCW. The free route described here is self-study using the published lectures, notes, code and exercises; it does not promise every classroom reading or service. You can begin this resource collection without purchasing the book. OCW does not provide instructor grading, an MIT certificate, academic credit or a degree.

Use lectures as the start of a study session

Pause after a new concept and work with one small example before continuing. Predict the output, run your version and change a single input. If the result surprises you, trace the values through the program. This is an optional study approach, not an additional MIT assessment.

Keep the code you wrote and a short note explaining the question it answers. As the material becomes more complex, revisit an earlier solution and consider whether a function or a different data structure would make it easier to understand. Comparing two versions can reveal progress more clearly than counting watched videos.

This archive suits independent learners who enjoy a lecture-based introduction and are comfortable managing their own study plan. Start with the opening lecture and a small exercise to see whether the pace and presentation fit how you learn.

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Frequently asked questions