Computational Thinking for Problem Solving
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Computational Thinking for Problem Solving
Instructor: Susan Davidson
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There are 4 modules in this course
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.
In this course, you will learn about the pillars of computational thinking, how computer scientists develop and analyze algorithms, and how solutions can be realized on a computer using the Python programming language. By the end of the course, you will be able to develop an algorithm and express it to the computer by writing a simple Python program. This course will introduce you to people from diverse professions who use computational thinking to solve problems. You will engage with a unique community of analytical thinkers and be encouraged to consider how you can make a positive social impact through 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.
What's included
6 videos1 reading5 assignments2 peer reviews4 discussion prompts
6 videos•Total 44 minutes
- 1.1 Introduction•5 minutes
- 1.2 Decomposition•6 minutes
- 1.3 Pattern Recognition•6 minutes
- 1.4 Data Representation and Abstraction•8 minutes
- 1.5 Algorithms•9 minutes
- 1.6 Case Studies•11 minutes
1 reading•Total 1 minute
- Opt-in to Penn Engineering Online Communications•1 minute
5 assignments•Total 80 minutes
- 1.2 Decomposition•10 minutes
- 1.3 Pattern Recognition•10 minutes
- 1.4 Data Representation and Abstraction•15 minutes
- 1.5 Algorithms•15 minutes
- Learning Style Preference Survey•30 minutes
2 peer reviews•Total 60 minutes
- Applying Computational Thinking in Your Life•30 minutes
- Project Part 1: Applying the Pillars of Computational Thinking•30 minutes
4 discussion prompts•Total 40 minutes
- Applying Decomposition in Your Life•10 minutes
- Applying Pattern Recognition in Your Life•10 minutes
- Applying Data Representation and Abstraction in Your Life•10 minutes
- Applying Algorithms in Your Life•10 minutes
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.
What's included
7 videos6 assignments4 peer reviews
7 videos•Total 69 minutes
- 2.1 Finding the Largest Value•9 minutes
- 2.2 Linear Search•5 minutes
- 2.3 Algorithmic Complexity•8 minutes
- 2.4 Binary Search•12 minutes
- 2.5 Brute Force Algorithms•13 minutes
- 2.6 Greedy Algorithms•9 minutes
- 2.7 Case Studies•12 minutes
6 assignments•Total 65 minutes
- 2.1 Finding the Largest Value•10 minutes
- 2.2 Linear Search •10 minutes
- 2.3 Algorithmic Complexity•10 minutes
- 2.4 Binary Search•10 minutes
- 2.5 Brute Force Algorithms•15 minutes
- 2.6 Greedy Algorithms•10 minutes
4 peer reviews•Total 115 minutes
- Finding Minimum Values•30 minutes
- Binary Search•30 minutes
- Greedy vs. Brute Force Algorithms•30 minutes
- Project Part 2: Describing Algorithms Using a Flowchart•25 minutes
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.
What's included
6 videos5 assignments5 peer reviews
6 videos•Total 46 minutes
- 3.1 A History of the Computer•8 minutes
- 3.2 Intro to the von Neumann Architecture•8 minutes
- 3.3 von Neumann Architecture Data•6 minutes
- 3.4 von Neumann Architecture Control Flow•6 minutes
- 3.5 Expressing Algorithms in Pseudocode•8 minutes
- 3.6 Case Studies•10 minutes
5 assignments•Total 50 minutes
- 3.1 A History of the Computer•10 minutes
- 3.2 Intro to the von Neumann Architecture•10 minutes
- 3.3 von Neumann Architecture Data•10 minutes
- 3.4 von Neumann Architecture Control Flow•10 minutes
- 3.5 Expressing Algorithms in Pseudocode•10 minutes
5 peer reviews•Total 120 minutes
- von Neumann Architecture Data & Instructions•30 minutes
- Reading & Writing Pseudocode•25 minutes
- Project Part 3: Writing Pseudocode•25 minutes
- (Optional) von Neumann Architecture Control Instructions•20 minutes
- (Optional) Understanding Pseudocode•20 minutes
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.
What's included
9 videos13 readings12 assignments
9 videos•Total 91 minutes
- 4.1 Introduction to Python•6 minutes
- 4.2 Variables•14 minutes
- 4.3 Conditional Statements•9 minutes
- 4.4 Lists•7 minutes
- 4.5 Iteration•14 minutes
- 4.6 Functions•11 minutes
- 4.7 Classes and Objects•9 minutes
- 4.8 Case Studies•12 minutes
- 4.9 Course Conclusion•8 minutes
13 readings•Total 126 minutes
- Programming on the Coursera Platform•10 minutes
- Python Playground•0 minutes
- Opt-in to Penn Engineering Online Communications•1 minute
- Variables Programming Activity•20 minutes
- Solution to Variables Programming Activity•10 minutes
- Conditionals Programming Activity•20 minutes
- Solution to Conditionals Programming Activity•10 minutes
- Solution to Lists Programming Assignment•5 minutes
- Solution to Loops Programming Assignment•10 minutes
- Solution to Functions Programming Assignment•10 minutes
- Solution to Challenge Programming Assignment•10 minutes
- Solution to Classes and Objects Programming Assignment•10 minutes
- Solution to Project Part 4•10 minutes
12 assignments•Total 185 minutes
- (Optional) Challenge Programming Assignment•20 minutes
- 4.2 Variables•10 minutes
- 4.3 Conditional Statements•5 minutes
- 4.4 Lists•10 minutes
- Lists Programming Assignment•15 minutes
- 4.5 Iteration•10 minutes
- Loops Programming Assignment•30 minutes
- 4.6 Functions•10 minutes
- Functions Programming Assignment•20 minutes
- 4.7 Classes and Objects•10 minutes
- Classes and Objects Programming Assignment•20 minutes
- Project Part 4: Implementing the Solution in Python•25 minutes
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Reviewed on Oct 1, 2019
It was amazing program to learn the computational thinking and the basic of python programming. Some tests were difficult for me, but it was good training to learn the programming.
Reviewed on Nov 21, 2021
The last week of the course was challenging, but I learned a lot! It was a great introductory course. I would use this to gauge your interest in this field. Worthwhile and you can't beat the cost!
Reviewed on Dec 23, 2019
Really great course about Computational Thinking and how to solve problems. Recommended for anyone interested in learning more about Computer Science and how computers can help us solve problems.
Frequently asked questions
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 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://www.coursera.org/degrees/mcit-penn). Please mention that you have completed this course in the application.
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Financial aid available,
¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.
