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Untertitel: Englisch

Kompetenzen, die Sie erwerben

Simple AlgorithmPython ProgrammingProblem SolvingComputation

100 % online

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Flexible Fristen

Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.

Stufe „Anfänger“

Ca. 35 Stunden zum Abschließen

Empfohlen: 4 weeks, 6-8 hours/week...


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. 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 Videos (Gesamt 44 min), 6 Quiz
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.

7 Videos (Gesamt 69 min), 10 Quiz
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.

6 Videos (Gesamt 46 min), 10 Quiz
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.

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
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Top reviews from Computational Thinking for Problem Solving

von 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

von AAFeb 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.



Susan Davidson

Weiss Professor
Computer & Information Science

Chris Murphy

Associate Professor of Practice
Computer & Information Science

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

  • 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.

  • 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.

  • 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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