关于此课程： 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.
此课程适用人群： This course is intended for learners from all backgrounds who are interested in approaching problems more systematically, developing more efficient solutions, and understanding how computers can be used in the problem-solving process.
制作方： University of Pennsylvania
教学方： Susan Davidson, Weiss Professor
Computer & Information Science教学方： Chris Murphy, Associate Professor of Practice
Computer & Information Science
| 级别 | 初级 |
| 承诺学习时间 | 4 weeks, 6-8 hours/week |
| 语言 | 英语（English） |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
授课大纲
第 1 周
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.
Computational thinking is built on four pillars: decomposition, pattern recognition, data representation and abstraction, and algorithms. This module introduces you to the four pillars of computational thinking and shows how they can be applied as part of the problem solving process.
6 个视频
- 视频: 1.2 Decomposition
- Discussion Prompt: Applying Decomposition in Your Life
- 视频: 1.3 Pattern Recognition
- Discussion Prompt: Applying Pattern Recognition in Your Life
- 视频: 1.4 Data Representation and Abstraction
- Discussion Prompt: Applying Data Representation and Abstraction in Your Life
- 视频: 1.5 Algorithms
- Discussion Prompt: Applying Algorithms in Your Life
- 视频: 1.6 Case Studies
已评分: 1.2 Decomposition
已评分: 1.3 Pattern Recognition
已评分: 1.4 Data Representation and Abstraction
已评分: 1.5 Algorithms
已评分: Applying Computational Thinking in Your Life
已评分: Project Part 1: Applying the Pillars of Computational Thinking
第 2 周
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.
7 个视频
- 视频: 2.2 Linear Search
- 视频: 2.3 Algorithmic Complexity
- 视频: 2.4 Binary Search
- 视频: 2.5 Brute Force Algorithms
- 视频: 2.6 Greedy Algorithms
- 视频: 2.7 Case Studies
已评分: 2.1 Finding the Largest Value
已评分: Finding Minimum Values
已评分: 2.2 Linear Search
已评分: 2.3 Algorithmic Complexity
已评分: 2.4 Binary Search
已评分: Binary Search
已评分: 2.5 Brute Force Algorithms
已评分: 2.6 Greedy Algorithms
已评分: Greedy vs. Brute Force Algorithms
已评分: Project Part 2: Describing Algorithms Using a Flowchart
第 3 周
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.
6 个视频
- 视频: 3.2 Intro to the von Neumann Architecture
- 视频: 3.3 von Neumann Architecture Data
- 视频: 3.4 von Neumann Architecture Control Flow
- Peer Review: (Optional) von Neumann Architecture Control Instructions
- 视频: 3.5 Expressing Algorithms in Pseudocode
- Peer Review: (Optional) Understanding Pseudocode
- 视频: 3.6 Case Studies
已评分: 3.1 A History of the Computer
已评分: 3.2 Intro to the von Neumann Architecture
已评分: 3.3 von Neumann Architecture Data
已评分: von Neumann Architecture Data & Instructions
已评分: 3.4 von Neumann Architecture Control Flow
已评分: 3.5 Expressing Algorithms in Pseudocode
已评分: Reading & Writing Pseudocode
已评分: Project Part 3: Writing Pseudocode
第 4 周
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.
9 个视频, 12 个阅读材料, 1 个练习测验
- Reading: Programming on the Coursera Platform
- Reading: Python Playground
- 视频: 4.2 Variables
- Reading: Variables Programming Activity
- Reading: Solution to Variables Programming Activity
- 视频: 4.3 Conditional Statements
- Reading: Conditionals Programming Activity
- Reading: Solution to Conditionals Programming Activity
- 视频: 4.4 Lists
- Reading: Solution to Lists Programming Assignment
- 视频: 4.5 Iteration
- Reading: Solution to Loops Programming Assignment
- 视频: 4.6 Functions
- Reading: Solution to Functions Programming Assignment
- Practice Quiz: (Optional) Challenge Programming Assignment
- Reading: Solution to Challenge Programming Assignment
- 视频: 4.7 Classes and Objects
- Reading: Solution to Classes and Objects Programming Assignment
- 视频: 4.8 Case Studies
- Reading: Solution to Project Part 4
- 视频: 4.9 Course Conclusion
已评分: 4.2 Variables
已评分: 4.3 Conditional Statements
已评分: 4.4 Lists
已评分: Lists Programming Assignment
已评分: 4.5 Iteration
已评分: Loops Programming Assignment
已评分: 4.6 Functions
已评分: Functions Programming Assignment
已评分: 4.7 Classes and Objects
已评分: Classes and Objects Programming Assignment
已评分: Project Part 4: Implementing the Solution in Python
常见问题解答
运作方式
Coursework
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Help from Your Peers
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Certificates
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制作方
University of Pennsylvania
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.
价格
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| 访问课程材料 | 可用 |
| 访问评分的材料 | 可用 |
| 收到最终成绩 | 可用 |
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评分和审阅
An excellent bridge into introductory computer science topics. Professors Susan Davidson and Chris Murphy exposed learners to computer science concepts within everyday problems.
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.
To think what a difference just a few weeks can make… thank you for awakening a desire in us propelling us forward into the world as computational thinking evangelists!
Know that you will be challenged, but true satisfaction awaits at the end-- priceless!!
The instruction -- as well as production quality -- of the course was outstanding, the course was very well paced and laid out, with each new week building upon learned concepts from the previous. I had done some online tutorials in various coding languages in the past, but this course is different in that it devotes the majority of the time to really break down a problem and think about it -- computationally -- before writing a single line of code. This made the final portion of the course, implementing the solution in Python, so much more rewarding, enjoyable and fun. It all came together and I just felt so satisfied seeing the results.
The instructors (Chris Murphy and Susan Davidson) did a truly outstanding job explaining each concept and walking through examples to reinforce -- and both even threw in some humor for good measure, which helped to soften a very scientific and stereotyped field.
This course was a great intro for those questioning whether or not they are truly interested in Comp Sci, as it makes you take a simple everyday problem and think about it in a computational way. There are thousands of coding tutorials online but I would highly recommend taking this course first as it acts as almost a primer to getting your thought process in the right mindset before even looking at a programming language.