课程信息
93,952 次近期查看

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

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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)

您将获得的技能

Simple AlgorithmPython ProgrammingProblem SolvingComputation

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

1
完成时间为 3 小时

Pillars of Computational Thinking

6 个视频 (总计 44 分钟), 6 个测验
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
2
完成时间为 4 小时

Expressing and Analyzing Algorithms

7 个视频 (总计 69 分钟), 10 个测验
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
3
完成时间为 4 小时

Fundamental Operations of a Modern Computer

6 个视频 (总计 46 分钟), 10 个测验
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
4
完成时间为 7 小时

Applied Computational Thinking Using Python

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
4.8
131 个审阅Chevron Right

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

来自Computational Thinking for Problem Solving的热门评论

创建者 JDec 19th 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

创建者 AWFeb 4th 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.

讲师

Avatar

Susan Davidson

Weiss Professor
Computer & Information Science
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Chris Murphy

Associate Professor of Practice
Computer & Information Science

关于 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. ...

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  • 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.

  • 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.

  • This course will help you discover whether you have an aptitude for computational thinking. This is a useful predictor of success in the Master of Computer and Information Technology program at the University of Pennsylvania, which is offered both on-campus and online. In this course you will learn from MCIT instructors and become familiar with the quality and style of MCIT Online courses.

    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.

  • 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/

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