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初級

完成時間大約為36 小時

建議:4 weeks, 6-8 hours/week...

英語(English)

字幕:英語(English)

您將獲得的技能

Simple AlgorithmPython ProgrammingProblem SolvingComputation
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100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

初級

完成時間大約為36 小時

建議:4 weeks, 6-8 hours/week...

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 3 小時

Pillars of Computational Thinking

6 個視頻 (總計 44 分鐘), 6 個測驗
6 個視頻
1.2 Decomposition6分鐘
1.3 Pattern Recognition5分鐘
1.4 Data Representation and Abstraction7分鐘
1.5 Algorithms8分鐘
1.6 Case Studies11分鐘
4 個練習
1.2 Decomposition10分鐘
1.3 Pattern Recognition10分鐘
1.4 Data Representation and Abstraction15分鐘
1.5 Algorithms15分鐘
2
完成時間為 4 小時

Expressing and Analyzing Algorithms

7 個視頻 (總計 69 分鐘), 10 個測驗
7 個視頻
2.2 Linear Search5分鐘
2.3 Algorithmic Complexity8分鐘
2.4 Binary Search11分鐘
2.5 Brute Force Algorithms13分鐘
2.6 Greedy Algorithms9分鐘
2.7 Case Studies12分鐘
6 個練習
2.1 Finding the Largest Value10分鐘
2.2 Linear Search10分鐘
2.3 Algorithmic Complexity10分鐘
2.4 Binary Search10分鐘
2.5 Brute Force Algorithms15分鐘
2.6 Greedy Algorithms10分鐘
3
完成時間為 4 小時

Fundamental Operations of a Modern Computer

6 個視頻 (總計 46 分鐘), 10 個測驗
6 個視頻
3.2 Intro to the von Neumann Architecture8分鐘
3.3 von Neumann Architecture Data6分鐘
3.4 von Neumann Architecture Control Flow5分鐘
3.5 Expressing Algorithms in Pseudocode8分鐘
3.6 Case Studies10分鐘
5 個練習
3.1 A History of the Computer10分鐘
3.2 Intro to the von Neumann Architecture10分鐘
3.3 von Neumann Architecture Data10分鐘
3.4 von Neumann Architecture Control Flow10分鐘
3.5 Expressing Algorithms in Pseudocode10分鐘
4
完成時間為 7 小時

Applied Computational Thinking Using Python

9 個視頻 (總計 91 分鐘), 12 個閱讀材料, 12 個測驗
9 個視頻
4.2 Variables13分鐘
4.3 Conditional Statements8分鐘
4.4 Lists7分鐘
4.5 Iteration14分鐘
4.6 Functions10分鐘
4.7 Classes and Objects9分鐘
4.8 Case Studies11分鐘
4.9 Course Conclusion8分鐘
12 個閱讀材料
Programming on the Coursera Platform10分鐘
Python Playground
Variables Programming Activity20分鐘
Solution to Variables Programming Activity10分鐘
Conditionals Programming Activity20分鐘
Solution to Conditionals Programming Activity10分鐘
Solution to Lists Programming Assignment5分鐘
Solution to Loops Programming Assignment10分鐘
Solution to Functions Programming Assignment10分鐘
Solution to Challenge Programming Assignment10分鐘
Solution to Classes and Objects Programming Assignment10分鐘
Solution to Project Part 410分鐘
12 個練習
4.2 Variables10分鐘
4.3 Conditional Statements5分鐘
4.4 Lists10分鐘
Lists Programming Assignment15分鐘
4.5 Iteration10分鐘
Loops Programming Assignment30分鐘
4.6 Functions10分鐘
Functions Programming Assignment20分鐘
(Optional) Challenge Programming Assignment20分鐘
4.7 Classes and Objects10分鐘
Classes and Objects Programming Assignment20分鐘
Project Part 4: Implementing the Solution in Python25分鐘
4.8
130 個審閱Chevron Right

42%

完成這些課程後已開始新的職業生涯

32%

通過此課程獲得實實在在的工作福利

來自Computational Thinking for Problem Solving的熱門評論

創建者 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

創建者 GBOct 1st 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.

講師

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Susan Davidson

Weiss Professor
Computer & Information Science
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Chris Murphy

Associate Professor of Practice
Computer & Information Science

關於 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. ...

常見問題

  • 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。

  • 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。

  • 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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