What you'll learn
Pillars of Computational Thinking
Expressing and Analyzing Algorithms
Fundamental Operations of a Modern Computer
Applied Computational Thinking Using Python
Description
Computational thinking is the process of approaching a problem in a systematic manner and creating and expressing a solution such that it can be carried out by a computer. But you don’t need to be a computer scientist to think like a computer scientist! In fact, we encourage students from any field of study to take this course. Many quantitative and data-centric problems can be solved using computational thinking and an understanding of computational thinking will give you a foundation for solving problems that have real-world, social impact.
In this course, you will learn about the pillars of computational thinking, how computer scientists develop and analyze algorithms, and how solutions can be realized on a computer using the Python programming language. By the end of the course, you will be able to develop an algorithm and express it to the computer by writing a simple Python program. This course will introduce you to people from diverse professions who use computational thinking to solve problems. You will engage with a unique community of analytical thinkers and be encouraged to consider how you can make a positive social impact through computational thinking.
Requirements
Access to a computer or mobile device with an internet connection.
Motivation to learn!
There are no special materials or prerequisite knowledge required for this course.
Who this course is for
Students who are new to this field
Students willing to put in a couple hours to learn about Computational Thinking for Problem Solving
Advanced students wanting to add another skill to their portfolio
Content Creator
Susan Davidson – Weiss Professor – Computer & Information Science
Chris Murphy – Associate Professor of Practice – Computer & Information Science
This course includes
Participation Confirmation/Certificate
Option for learning at your own pace
Videos and reading material about the course
Practice tests
Assessed tasks with feedback from other course participants
Evaluated tests with feedback
Evaluated programming tasks
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