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.
提供方
Computational Thinking for Problem Solving
宾夕法尼亚大学
课程信息
165,225 次近期查看
学生职业成果
42%
完成这些课程后已开始新的职业生涯
32%
通过此课程获得实实在在的工作福利
您将获得的技能
Simple AlgorithmPython ProgrammingProblem SolvingComputation
学生职业成果
42%
完成这些课程后已开始新的职业生涯
32%
通过此课程获得实实在在的工作福利
100% 在线
立即开始，按照自己的计划学习。
可灵活调整截止日期
根据您的日程表重置截止日期。
初级
完成时间大约为37 小时
建议：4 weeks, 6-8 hours/week
英语（English）
字幕：英语（English）
教学大纲 - 您将从这门课程中学到什么
完成时间为 3 小时
Pillars of Computational Thinking
Computational thinking is an approach to solving problems using concepts and ideas from computer science, and expressing solutions to those problems so that they can be run on a computer. As computing becomes more and more prevalent in all aspects of modern society -- not just in software development and engineering, but in business, the humanities, and even everyday life -- understanding how to use computational thinking to solve real-world problems is a key skill in the 21st century.
完成时间为 3 小时
6 个视频 （总计 44 分钟）
6 个视频
1.2 Decomposition6分钟
1.3 Pattern Recognition5分钟
1.4 Data Representation and Abstraction7分钟
1.5 Algorithms8分钟
1.6 Case Studies11分钟
4 个练习
1.2 Decomposition10分钟
1.3 Pattern Recognition10分钟
1.4 Data Representation and Abstraction15分钟
1.5 Algorithms15分钟
完成时间为 4 小时
Expressing and Analyzing Algorithms
When we use computational thinking to solve a problem, what we’re really doing is developing an algorithm: a step-by-step series of instructions. Whether it’s a small task like scheduling meetings, or a large task like mapping the planet, the ability to develop and describe algorithms is crucial to the problem-solving process based on computational thinking. This module will introduce you to some common algorithms, as well as some general approaches to developing algorithms yourself. These approaches will be useful when you're looking not just for any answer to a problem, but the best answer. After completing this module, you will be able to evaluate an algorithm and analyze how its performance is affected by the size of the input so that you can choose the best algorithm for the problem you’re trying to solve.
完成时间为 4 小时
7 个视频 （总计 69 分钟）
7 个视频
2.2 Linear Search5分钟
2.3 Algorithmic Complexity8分钟
2.4 Binary Search11分钟
2.5 Brute Force Algorithms13分钟
2.6 Greedy Algorithms9分钟
2.7 Case Studies12分钟
6 个练习
2.1 Finding the Largest Value10分钟
2.2 Linear Search10分钟
2.3 Algorithmic Complexity10分钟
2.4 Binary Search10分钟
2.5 Brute Force Algorithms15分钟
2.6 Greedy Algorithms10分钟
完成时间为 4 小时
Fundamental Operations of a Modern Computer
Computational thinking is a problem-solving process in which the last step is expressing the solution so that it can be executed on a computer. However, before we are able to write a program to implement an algorithm, we must understand what the computer is capable of doing -- in particular, how it executes instructions and how it uses data. This module describes the inner workings of a modern computer and its fundamental operations. Then it introduces you to a way of expressing algorithms known as pseudocode, which will help you implement your solution using a programming language.
完成时间为 4 小时
6 个视频 （总计 46 分钟）
6 个视频
3.2 Intro to the von Neumann Architecture8分钟
3.3 von Neumann Architecture Data6分钟
3.4 von Neumann Architecture Control Flow5分钟
3.5 Expressing Algorithms in Pseudocode8分钟
3.6 Case Studies10分钟
5 个练习
3.1 A History of the Computer10分钟
3.2 Intro to the von Neumann Architecture10分钟
3.3 von Neumann Architecture Data10分钟
3.4 von Neumann Architecture Control Flow10分钟
3.5 Expressing Algorithms in Pseudocode10分钟
完成时间为 7 小时
Applied Computational Thinking Using Python
Writing a program is the last step of the computational thinking process. It’s the act of expressing an algorithm using a syntax that the computer can understand. This module introduces you to the Python programming language and its core features. Even if you have never written a program before -- or never even considered it -- after completing this module, you will be able to write simple Python programs that allow you to express your algorithms to a computer as part of a problem-solving process based on computational thinking.
完成时间为 7 小时
9 个视频 （总计 91 分钟）, 12 个阅读材料, 12 个测验
9 个视频
4.2 Variables13分钟
4.3 Conditional Statements8分钟
4.4 Lists7分钟
4.5 Iteration14分钟
4.6 Functions10分钟
4.7 Classes and Objects9分钟
4.8 Case Studies11分钟
4.9 Course Conclusion8分钟
12 个阅读材料
Programming on the Coursera Platform10分钟
Python Playground
Variables Programming Activity20分钟
Solution to Variables Programming Activity10分钟
Conditionals Programming Activity20分钟
Solution to Conditionals Programming Activity10分钟
Solution to Lists Programming Assignment5分钟
Solution to Loops Programming Assignment10分钟
Solution to Functions Programming Assignment10分钟
Solution to Challenge Programming Assignment10分钟
Solution to Classes and Objects Programming Assignment10分钟
Solution to Project Part 410分钟
12 个练习
4.2 Variables10分钟
4.3 Conditional Statements5分钟
4.4 Lists10分钟
Lists Programming Assignment15分钟
4.5 Iteration10分钟
Loops Programming Assignment30分钟
4.6 Functions10分钟
Functions Programming Assignment20分钟
(Optional) Challenge Programming Assignment20分钟
4.7 Classes and Objects10分钟
Classes and Objects Programming Assignment20分钟
Project Part 4: Implementing the Solution in Python25分钟
审阅
4.8
来自COMPUTATIONAL THINKING FOR PROBLEM SOLVING的热门评论
由 J 提供Dec 18, 2018
Excellent course for beginners with enough depth, programming and computational theory to increase their computer science knowledge to a higher level. It builds a good foundation of how computers work
由 AW 提供Feb 3, 2019
The course is very well-designed and it helped me develop understand how to apply computational thinking in solving various types of problems as well as acquire basic skills of programming in Python.
由 JR 提供Jun 27, 2019
The course is great. I learned a lot. The support for the course is SUPER slow. It's hard to get any direct help for any questions or issues that you are having. Beware of assignment 4.7!
由 GB 提供Sep 30, 2019
Very well thought out. This course covers simple concepts while still being engaging and challenging. Examples from varying disciplines help illustrate concepts in a real-life context.
由 RK 提供Nov 1, 2019
Course content is good, graded assignments are good, I just had problems with my assignments in week 4 as I easily became confused with the implementation of all the lessons combined.
由 LS 提供Jan 17, 2019
Useful course taught at an adequate rate. I recommend it for people who are interested in learning the basics of computational thinking, i.e. a systematic approach to problem-solving.
由 JL 提供Feb 8, 2020
Great course - the non-programming parts (making flow charts etc) were actually more difficult than the programming (simple Python programming - my first time programming in python)
由 EN 提供Aug 18, 2019
The autograder can be somewhat of a pain and some of the solutions in the peer-review sections aren't the most efficient. Otherwise it helped teach the building blocks of coding.
由 DA 提供Nov 17, 2018
An excellent bridge into introductory computer science topics. Professors Susan Davidson and Chris Murphy exposed learners to computer science concepts within everyday problems.
由 BM 提供Feb 14, 2019
Thoroughly enjoyed the course. If you are new to computer science or need a refresher of the basic concepts you learned in high school / college, this is the perfect course
由 QT 提供Mar 3, 2019
The course is generally good. However, the assignment content and the lecture are not really getting along, especially the Python part. I suggest more "bridging" materials.
由 GS 提供Nov 17, 2018
Well taught with good examples and exercises that require thinking but still approachable. Very well laid out and taught. Definitely sparked an interest to go learn more.
由 MD 提供Oct 23, 2018
Value, conherence content, life-long skill for problem solving. It the best in Computational Thinking, you can also consider as good Introduction to Computer Sciences.
由 HT 提供Feb 25, 2019
I learned the methods to solve the problem with computational thinking and the course is really great. I will recommend this course to my friends and colleagues.
由 YY 提供Nov 12, 2018
More than yet another programming crash course. The only requirements here are your brain and desire to solve real-world problems. Simple and clear for everyone.
由 LY 提供Feb 28, 2019
A very thorough and engaging experience for student. Lots of video and very good explanation of computer science concept and practical problems involving python
由 YS 提供Jan 13, 2019
This is a good short starter course for people leaping into IT and good refresher course for some. I could finish the audit (minus assignment) in two days
由 CJ 提供Feb 15, 2019
Great course for an introduction to Computer Science. Learned a lot and enjoyed even more. The quality of the material (video production) is really goof.
由 HX 提供Jan 31, 2019
If you don't have programming experience, you may find the last module a bit challenge. But overall, it's a very good, structured, informative course.
由 DM 提供Feb 22, 2019
Great course for people new to computing, but also helpful for those who have dabbled in various languages but want to understand the bigger picture.
提供方
宾夕法尼亚大学
The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
常见问题
我什么时候能够访问课程视频和作业？
注册以便获得证书后，您将有权访问所有视频、测验和编程作业（如果适用）。只有在您的班次开课之后，才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程，可能无法访问某些作业。
我购买证书后会得到什么？
您购买证书后，将有权访问所有课程材料，包括评分作业。完成课程后，您的电子课程证书将添加到您的成就页中，您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容，可以免费旁听课程。
退款政策是如何规定的？
有助学金吗？
Do I need to know how to program or have studied computer science in order to take this course?
No, definitely not! This course is intended for anyone who has an interest in approaching problems more systematically, developing more efficient solutions, and understanding how computers can be used in the problem solving process. No prior computer science or programming experience is required.
How much math do I need to know to take this course?
Some parts of the course assume familiarity with basic algebra, trigonometry, mathematical functions, exponents, and logarithms. If you don’t remember those concepts or never learned them, don’t worry! As long as you’re comfortable with multiplication, you should still be able to follow along. For everything else, we’ll provide links to references that you can use as a refresher or as supplemental material.
Does this course prepare me for the Master of Computer and Information Technology (MCIT) degree program at the University of Pennsylvania?
This course will help you discover whether you have an aptitude for computational thinking and give you some beginner-level experience with online learning. In this course you will learn several introductory concepts from MCIT instructors produced by the same team that brought the MCIT degree online.
If you have a bachelor's degree and are interested in learning more about computational thinking, we encourage you to apply to MCIT On-campus (http://www.cis.upenn.edu/prospective-students/graduate/mcit.php) or MCIT Online (https://onlinelearning.seas.upenn.edu/mcit/). Please mention that you have completed this course in the application.
Where can I find more information about the Master of Computer and Information Technology (MCIT) degree program at the University of Pennsylvania?
Use these links to learn more about MCIT:
MCIT On-campus: http://www.cis.upenn.edu/prospective-students/graduate/mcit.php
MCIT Online: https://onlinelearning.seas.upenn.edu/mcit/
还有其他问题吗？请访问 学生帮助中心。