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.

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Read about what's good

what should give you pause

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

Intro to computational thinking and python

According to learners, this course offers a strong foundation in computational thinking, breaking down complex concepts into manageable parts. Students appreciate the clear explanations of core principles like decomposition, pattern recognition, and algorithms. The introduction to Python programming is frequently mentioned as a valuable practical component, allowing students to apply theoretical knowledge. While some find the pace suitable for beginners, others with prior experience might find the initial Python modules too basic. Overall, it's seen as a highly engaging and practical course for developing problem-solving skills using a computational approach.

Pace suitable for beginners, potentially slow for others.

"As someone new to this field, I found the pace manageable and not overwhelming."

"The course moves at a good speed for true beginners in both CT and programming."

"I have some programming background, and the initial Python parts felt a bit slow."

"Might be too basic if you already have a solid grasp of programming fundamentals."

Engaging instructors and clear delivery.

"The instructors were excellent and explained things very clearly."

"Lectures were well-structured and kept me engaged throughout the modules."

"I appreciated the instructor's ability to simplify complex ideas."

"The video quality and presentation style were professional and easy to follow."

Helpful assignments reinforce learning.

"The assignments were well-designed and really made me think about applying the concepts."

"I found the exercises challenging but rewarding, helping solidify my understanding."

"The problem sets provided excellent practice in breaking down problems."

"Working through the coding problems was the most valuable part for me."

Practical application of CT using Python.

"The Python module was a great way to see how the theoretical concepts translate into code."

"Learning basic Python alongside CT was incredibly useful and well integrated."

"The programming exercises reinforced the computational thinking process effectively."

"I had no prior Python experience, and this course gave me a solid starting point for using it."

Excellent explanation of computational thinking basics.

"This course provides a very clear and easy-to-understand introduction to computational thinking."

"The way the pillars of CT were explained was insightful and practical for problem-solving."

"I feel like I finally grasp the core concepts of decomposition, pattern recognition, and abstraction."

"It really helped me understand how to approach problems from a computational perspective."

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

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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