Computer Science: Algorithms, Theory, and Machines

Princeton connects practical algorithms with the theoretical and physical models behind computing. Searching and data structures lead into Turing machines, intractability and processor design. It is a continuation after Programming with a Purpose or equivalent knowledge, for learners interested in why computers can solve some problems more readily than others.

What you will learn

• Compare classic algorithms and data structures.
• Explore models of computability and intractability.
• Connect logic circuits with processor architecture.

Who this suits

Learners with the programming foundations developed in the precursor course or equivalent preparation.

Before you enroll

Allow time to solve exercises independently rather than only following explanations. Software versions and setup instructions can change; compare them with current documentation. Mathematical and programming foundations develop through repeated practice, and completing an introductory course does not demonstrate expertise in every computing field.

Princeton states that the videos, exercises and programming assignments are available free. The course does not award a completion certificate. Optional textbooks and other external materials may have separate costs; review current enrollment conditions on the provider page.

Put it into practice

Compare two approaches to a small search problem, then explain separately their implementation, resource requirements and theoretical limits.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Coursera.

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