このコースについて
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自分のスケジュールですぐに学習を始めてください。

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初級レベル

約35時間で修了

推奨:4 weeks, 6-8 hours/week...

英語

字幕:英語

習得するスキル

Simple AlgorithmPython ProgrammingProblem SolvingComputation

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

初級レベル

約35時間で修了

推奨:4 weeks, 6-8 hours/week...

英語

字幕:英語

シラバス - 本コースの学習内容

1
3時間で修了

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.

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6件のビデオ (合計44分), 6 quizzes
6件のビデオ
1.2 Decomposition6 分
1.3 Pattern Recognition5 分
1.4 Data Representation and Abstraction7 分
1.5 Algorithms8 分
1.6 Case Studies11 分
4の練習問題
1.2 Decomposition10 分
1.3 Pattern Recognition10 分
1.4 Data Representation and Abstraction15 分
1.5 Algorithms15 分
2
4時間で修了

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.

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7件のビデオ (合計69分), 10 quizzes
7件のビデオ
2.2 Linear Search5 分
2.3 Algorithmic Complexity8 分
2.4 Binary Search11 分
2.5 Brute Force Algorithms13 分
2.6 Greedy Algorithms9 分
2.7 Case Studies12 分
6の練習問題
2.1 Finding the Largest Value10 分
2.2 Linear Search10 分
2.3 Algorithmic Complexity10 分
2.4 Binary Search10 分
2.5 Brute Force Algorithms15 分
2.6 Greedy Algorithms10 分
3
4時間で修了

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.

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6件のビデオ (合計46分), 10 quizzes
6件のビデオ
3.2 Intro to the von Neumann Architecture8 分
3.3 von Neumann Architecture Data6 分
3.4 von Neumann Architecture Control Flow5 分
3.5 Expressing Algorithms in Pseudocode8 分
3.6 Case Studies10 分
5の練習問題
3.1 A History of the Computer10 分
3.2 Intro to the von Neumann Architecture10 分
3.3 von Neumann Architecture Data10 分
3.4 von Neumann Architecture Control Flow10 分
3.5 Expressing Algorithms in Pseudocode10 分
4
7時間で修了

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.

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9件のビデオ (合計91分), 12 readings, 12 quizzes
9件のビデオ
4.2 Variables13 分
4.3 Conditional Statements8 分
4.4 Lists7 分
4.5 Iteration14 分
4.6 Functions10 分
4.7 Classes and Objects9 分
4.8 Case Studies11 分
4.9 Course Conclusion8 分
12件の学習用教材
Programming on the Coursera Platform10 分
Python Playground
Variables Programming Activity20 分
Solution to Variables Programming Activity10 分
Conditionals Programming Activity20 分
Solution to Conditionals Programming Activity10 分
Solution to Lists Programming Assignment5 分
Solution to Loops Programming Assignment10 分
Solution to Functions Programming Assignment10 分
Solution to Challenge Programming Assignment10 分
Solution to Classes and Objects Programming Assignment10 分
Solution to Project Part 410 分
12の練習問題
4.2 Variables10 分
4.3 Conditional Statements5 分
4.4 Lists10 分
Lists Programming Assignment15 分
4.5 Iteration10 分
Loops Programming Assignment30 分
4.6 Functions10 分
Functions Programming Assignment20 分
(Optional) Challenge Programming Assignment20 分
4.7 Classes and Objects10 分
Classes and Objects Programming Assignment20 分
Project Part 4: Implementing the Solution in Python25 分
4.8
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Computational Thinking for Problem Solving からの人気レビュー

by 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

by 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.

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

ペンシルベニア大学(University of Pennsylvania)について

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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  • 修了証を購入する際、コースのすべての教材(採点課題を含む)にアクセスできます。コースを完了すると、電子修了証が成果のページに追加されます。そこから修了証を印刷したり、LinkedInのプロフィールに追加したりできます。コースの内容の閲覧のみを希望する場合は、無料でコースを聴講できます。

  • 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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