Principles of Computing (Part 2)

Coursera Principles of Computing (Part 2)

Platform
Coursera
Provider
Rice University
Effort
7-10 hours/week
Length
4 weeks
Language
English
Credentials
Paid Certificate Available
Part of
Course Link
Overview
This two-part course introduces the basic mathematical and programming principles that underlie much of Computer Science. Understanding these principles is crucial to the process of creating efficient and well-structured solutions for computational problems. To get hands-on experience working with these concepts, we will use the Python programming language. The main focus of the class will be weekly mini-projects that build upon the mathematical and programming principles that are taught in the class. To keep the class fun and engaging, many of the projects will involve working with strategy-based games.

In part 2 of this course, the programming portion of the class will focus on concepts such as recursion, assertions, and invariants. The mathematical portion of the class will focus on searching, sorting, and recursive data structures. Upon completing this course, you will have a solid foundation in the principles of computation and programming. This will prepare you for the next course in the specialization, which will begin to introduce a structured approach to developing and analyzing algorithms. Developing such algorithmic thinking skills will be critical to writing large scale software and solving real world computational problems.

Syllabus
Searching and Data Structures
This week, we will explain the importance of searching. We will also explore various data structures and learn about inheritance.

Recursion
This week, we will explain the importance of recursion.

Trees
This week, we will explain the importance of trees. We will also explore how to set up game trees so that we can efficiently search them.

Modeling, Assertions, and Invariants
This week, we will explain the importance of modeling. We will also explore how to use assertions and invariants to ensure that our models are always consistent and correct.

Taught by
Scott Rixner, Joe Warren and Luay Nakhleh
Author
Coursera
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