Computational Thinking for Problem Solving from Coursera

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.

What's inside

Syllabus

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.

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.

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.

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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores computational thinking as foundational knowledge for real-world problem-solving

Emphasizes how computational thinking can positively affect society if applied

Provides learners who are unfamiliar with coding the opportunity to develop foundational knowledge

Covers the fundamental operations of modern computers, which is useful knowledge for learners of all levels

Taught by Susan Davidson, who has extensive experience in computational thinking

Reviews summary

Computational thinking for programming

learners say this course is highly rated by learners and provides a strong introduction to computational thinking for solving real-world problems. This course emphasizes the four pillars of computational thinking: Decomposition, Pattern recognition, Data representation and abstraction, and Algorithms. It was well received by learners who wanted to enter the computer science field or who were curious about computational thinking. The first three weeks of the course are highly rated and learners say they are clear and easy to follow. The final week features an introduction to Python programming which many learners felt was rushed. Overall, the course is well received and it is suitable for beginners.

Learners say the instructors, Susan Davidson and Chris Murphy, provide clear and engaging explanations throughout the course.

"learners say the instructors...provide clear and engaging explanations"

The course provides real-world examples and case studies to demonstrate how computational thinking can be applied to solve problems.

"The course provides real-world examples and case studies"

This course is appropriate for beginners with little to no background in computer science.

"This course is appropriate for beginners"

This course is divided into four weeks covering different aspects of computational thinking: Decomposition, Pattern recognition, Data representation and abstraction, and Algorithms. The fourth week covers Python programming.

"This course is divided into four weeks"

"The fourth week covers Python programming."

Some learners reported experiencing limited support from instructors and teaching assistants, particularly in the discussion forums.

"Some learners reported experiencing limited support from instructors and teaching assistants"

While the first three weeks of the course are well received, many learners found the Python module in the fourth week to be challenging and rushed.

"many learners found the Python module in the fourth week to be challenging and rushed"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Computational Thinking for Problem Solving with these activities:

Read 'A Mind for Numbers' by Barbara Oakley

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Understand how your brain learns and how to use this knowledge to become a more effective learner of computational thinking.

View Learning How to Learn on Amazon

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Obtain and read the book paying particular attention to the sections corresponding to the course

Review Python syntax and semantics

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Review the basics of Python syntax and semantics to refresh your memory and strengthen your foundation for the course.

Browse courses on Python

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Read through Python tutorial or documentation
Complete online Python exercises or coding challenges
Work through simple Python projects

Read 'Algorithms to Live By' by Thomas Cormen

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Discover how to approach problems with computational thinking and use algorithms to solve them.

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Obtain and read the book paying particular attention to the sections corresponding to the course

Five other activities

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Join a study group with other students in the course

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Collaborate with peers to discuss course concepts, solve problems, and support each other's learning.

Browse courses on Computational Thinking

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Find other students in the course who are interested in forming a study group
Meet regularly to discuss the course material, work on assignments, and provide support

Solve practice problems on LeetCode or HackerRank

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Reinforce your understanding of algorithms and practice writing Python code to solve computational problems.

Browse courses on Algorithms

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Register for a LeetCode or HackerRank account
Select problems tagged with topics covered in the course
Solve problems and review solutions

Follow tutorials on Coursera or edX

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Supplement the course materials with additional tutorials to deepen your understanding of Python and computational thinking concepts.

Browse courses on Python

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Identify tutorials on Coursera or edX related to the topics covered in the course
Follow along with the tutorials, completing all exercises and assignments

Create a blog post or video explaining a computational thinking concept

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Solidify your understanding of computational thinking by explaining a concept to others.

Browse courses on Computational Thinking

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Choose a computational thinking concept to explain
Create a blog post, video, or other content explaining the concept
Share your content with others and get their feedback

Develop a Python program to solve a real-world problem

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Apply your computational thinking skills to a real-world problem and build a Python program to solve it.

Browse courses on Python

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Identify a real-world problem that can be solved using computational thinking
Design and implement a Python program to solve the problem
Test and iterate on your program to improve its efficiency and accuracy

Career center

Learners who complete Computational Thinking for Problem Solving will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts use computational thinking to solve problems related to data. They analyze data to identify patterns and trends, and develop algorithms to solve problems related to data. This course can help you build a foundation in computational thinking and data analysis, which are essential skills for Data Analysts.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers use computational thinking to design, develop, and maintain software applications. They use algorithms to solve problems and develop efficient software solutions. This course can help you build a foundation in computational thinking and software development, which are essential skills for Software Engineers.

See salaries and explore the career path for Software Engineer

Quantitative Analyst

Quantitative Analysts use computational thinking to solve problems related to finance and economics. They use algorithms to develop models for financial analysis and risk management. This course can help you build a foundation in computational thinking and quantitative analysis, which are essential skills for Quantitative Analysts.

See salaries and explore the career path for Quantitative Analyst

Data Scientist

Data Scientists use computational thinking to solve problems related to data. They use algorithms to analyze data and develop models for prediction and decision-making. This course can help you build a foundation in computational thinking and data science, which are essential skills for Data Scientists.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers use computational thinking to develop and deploy machine learning models. They use algorithms to train models and optimize their performance. This course can help you build a foundation in computational thinking and machine learning, which are essential skills for Machine Learning Engineers.

See salaries and explore the career path for Machine Learning Engineer

Computer Scientist

Computer Scientists use computational thinking to solve problems related to computer science. They use algorithms to design and develop computer systems and software. This course can help you build a foundation in computational thinking and computer science, which are essential skills for Computer Scientists.

See salaries and explore the career path for Computer Scientist

Systems Analyst

Systems Analysts use computational thinking to design and implement computer systems. They use algorithms to develop solutions for business problems. This course can help you build a foundation in computational thinking and systems analysis, which are essential skills for Systems Analysts.

See salaries and explore the career path for Systems Analyst

Business Analyst

Business Analysts use computational thinking to solve problems related to business. They use algorithms to develop models for business analysis and decision-making. This course can help you build a foundation in computational thinking and business analysis, which are essential skills for Business Analysts.

See salaries and explore the career path for Business Analyst

Project Manager

Project Managers use computational thinking to manage projects. They use algorithms to develop project plans and schedules. This course can help you build a foundation in computational thinking and project management, which are essential skills for Project Managers.

See salaries and explore the career path for Project Manager

Operations Research Analyst

Operations Research Analysts use computational thinking to solve problems related to operations research. They use algorithms to develop models for optimizing operations. This course can help you build a foundation in computational thinking and operations research, which are essential skills for Operations Research Analysts.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

Financial Analysts use computational thinking to solve problems related to finance. They use algorithms to develop models for financial analysis and risk management. This course can help you build a foundation in computational thinking and financial analysis, which are essential skills for Financial Analysts.

See salaries and explore the career path for Financial Analyst

Market Researcher

Market Researchers use computational thinking to solve problems related to marketing. They use algorithms to develop models for market analysis and forecasting. This course can help you build a foundation in computational thinking and market research, which are essential skills for Market Researchers.

See salaries and explore the career path for Market Researcher

Actuary

Actuaries use computational thinking to solve problems related to insurance and finance. They use algorithms to develop models for insurance pricing and risk management. This course can help you build a foundation in computational thinking and actuarial science, which are essential skills for Actuaries.

See salaries and explore the career path for Actuary

Statistician

Statisticians use computational thinking to solve problems related to statistics. They use algorithms to develop models for statistical analysis and inference. This course can help you build a foundation in computational thinking and statistics, which are essential skills for Statisticians.

See salaries and explore the career path for Statistician

Economist

Economists use computational thinking to solve problems related to economics. They use algorithms to develop models for economic analysis and forecasting. This course may help you build a foundation in computational thinking and economics, which are essential skills for Economists.

See salaries and explore the career path for Economist