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.
Computational Thinking for Problem Solving
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Computational Thinking for Problem Solving
University of Pennsylvania
Über diesen Kurs
165,225 kürzliche Aufrufe
Karriereergebnisse der Lernenden
42%
nahm einen neuen Beruf nach Abschluss dieser Kurse auf
32%
ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs
Kompetenzen, die Sie erwerben
Simple AlgorithmPython ProgrammingProblem SolvingComputation
Karriereergebnisse der Lernenden
42%
nahm einen neuen Beruf nach Abschluss dieser Kurse auf
32%
ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Stufe „Anfänger“
Ca. 37 Stunden zum Abschließen
Empfohlen: 4 weeks, 6-8 hours/week
Englisch
Untertitel: Englisch
Lehrplan - Was Sie in diesem Kurs lernen werden
3 Stunden zum Abschließen
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.
3 Stunden zum Abschließen
6 Videos (Gesamt 44 min)
6 Videos
1.2 Decomposition6m
1.3 Pattern Recognition5m
1.4 Data Representation and Abstraction7m
1.5 Algorithms8m
1.6 Case Studies11m
4 praktische Übungen
1.2 Decomposition10m
1.3 Pattern Recognition10m
1.4 Data Representation and Abstraction15m
1.5 Algorithms15m
4 Stunden zum Abschließen
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.
4 Stunden zum Abschließen
7 Videos (Gesamt 69 min)
7 Videos
2.2 Linear Search5m
2.3 Algorithmic Complexity8m
2.4 Binary Search11m
2.5 Brute Force Algorithms13m
2.6 Greedy Algorithms9m
2.7 Case Studies12m
6 praktische Übungen
2.1 Finding the Largest Value10m
2.2 Linear Search10m
2.3 Algorithmic Complexity10m
2.4 Binary Search10m
2.5 Brute Force Algorithms15m
2.6 Greedy Algorithms10m
4 Stunden zum Abschließen
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.
4 Stunden zum Abschließen
6 Videos (Gesamt 46 min)
6 Videos
3.2 Intro to the von Neumann Architecture8m
3.3 von Neumann Architecture Data6m
3.4 von Neumann Architecture Control Flow5m
3.5 Expressing Algorithms in Pseudocode8m
3.6 Case Studies10m
5 praktische Übungen
3.1 A History of the Computer10m
3.2 Intro to the von Neumann Architecture10m
3.3 von Neumann Architecture Data10m
3.4 von Neumann Architecture Control Flow10m
3.5 Expressing Algorithms in Pseudocode10m
7 Stunden zum Abschließen
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.
7 Stunden zum Abschließen
9 Videos (Gesamt 91 min), 12 Lektüren, 12 Quiz
9 Videos
4.2 Variables13m
4.3 Conditional Statements8m
4.4 Lists7m
4.5 Iteration14m
4.6 Functions10m
4.7 Classes and Objects9m
4.8 Case Studies11m
4.9 Course Conclusion8m
12 Lektüren
Programming on the Coursera Platform10m
Python Playground
Variables Programming Activity20m
Solution to Variables Programming Activity10m
Conditionals Programming Activity20m
Solution to Conditionals Programming Activity10m
Solution to Lists Programming Assignment5m
Solution to Loops Programming Assignment10m
Solution to Functions Programming Assignment10m
Solution to Challenge Programming Assignment10m
Solution to Classes and Objects Programming Assignment10m
Solution to Project Part 410m
12 praktische Übungen
4.2 Variables10m
4.3 Conditional Statements5m
4.4 Lists10m
Lists Programming Assignment15m
4.5 Iteration10m
Loops Programming Assignment30m
4.6 Functions10m
Functions Programming Assignment20m
(Optional) Challenge Programming Assignment20m
4.7 Classes and Objects10m
Classes and Objects Programming Assignment20m
Project Part 4: Implementing the Solution in Python25m
Bewertungen
Top-Bewertungen von COMPUTATIONAL THINKING FOR PROBLEM SOLVING
von JDec 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
von AWFeb 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.
von JRJun 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!
von GBSep 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.
von RKNov 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.
von LSJan 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.
von JLFeb 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)
von ENAug 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.
von DANov 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.
von BMFeb 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
von QTMar 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.
von GSNov 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.
Über 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.
Häufig gestellte Fragen
Wann erhalte ich Zugang zu den Vorträgen und Aufgaben?
Sobald Sie sich für ein Zertifikat angemeldet haben, haben Sie Zugriff auf alle Videos, Quizspiele und Programmieraufgaben (falls zutreffend). Aufgaben, die von anderen Kursteilnehmern bewertet werden, können erst dann eingereicht und überprüft werden, wenn Ihr Unterricht begonnen hat. Wenn Sie sich den Kurs anschauen möchten, ohne ihn zu kaufen, können Sie womöglich auf bestimmte Aufgaben nicht zugreifen.
Was bekomme ich, wenn ich das Zertifikat erwerbe?
Wenn Sie ein Zertifikat erwerben, erhalten Sie Zugriff auf alle Kursmaterialien, einschließlich bewerteter Aufgaben. Nach Abschluss des Kurses wird Ihr elektronisches Zertifikat zu Ihrer Seite „Errungenschaften“ hinzugefügt – von dort können Sie Ihr Zertifikat ausdrucken oder es zu Ihrem LinkedIn Profil hinzufügen. Wenn Sie nur lesen und den Inhalt des Kurses anzeigen möchten, können Sie kostenlos als Gast an dem Kurs teilnehmen.
Wie erfolgen Rückerstattungen?
Ist finanzielle Unterstützung möglich?
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/
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