Algorithmic Thinking

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8/10 stars
based on  3 reviews
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Cost FREE
Start Date TBA

Course Details

Cost

FREE

Upcoming Schedule

  • TBA

Course Provider

Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

Provider Subject Specialization
Humanities
Sciences & Technology
4733 reviews

Course Description

When presented with a problem from a scientific domain, a Computer Scientist goes through a set of steps in order to provide a solution for the problem. These steps include: (1) understanding the problem; (2) formulating the problem mathematically; (3) designing an algorithm; (4) implementing the algorithm; and (5) solving the original scientific problem. This course will train students in how to employ algorithmic thinking by following these five steps to solve real-world problems. Understanding the problem entails holding conversations with domain experts to understand the parameters of the problem, what data they can provide to the computer program, what answers they expect, etc. Formulating the problem mathematically is basically the step of turning the problem from an English description to a mathematical description that is amenable to further computational analyses. While the course emphasizes implementing the algori... When presented with a problem from a scientific domain, a Computer Scientist goes through a set of steps in order to provide a solution for the problem. These steps include: (1) understanding the problem; (2) formulating the problem mathematically; (3) designing an algorithm; (4) implementing the algorithm; and (5) solving the original scientific problem. This course will train students in how to employ algorithmic thinking by following these five steps to solve real-world problems. Understanding the problem entails holding conversations with domain experts to understand the parameters of the problem, what data they can provide to the computer program, what answers they expect, etc. Formulating the problem mathematically is basically the step of turning the problem from an English description to a mathematical description that is amenable to further computational analyses. While the course emphasizes implementing the algorithms and solving the original problems that gave rise to the need for these algorithms in the first place, much of the course will be devoted to the third step, namely, algorithm design. Here, the course will introduce students to different algorithm design strategies, as well as mathematical tools for reasoning about the correctness and efficiency of algorithms.
Reviews 8/10 stars
3 Reviews for Algorithmic Thinking

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Rankings are based on a provider's overall CourseTalk score, which takes into account both average rating and number of ratings. Stars round to the nearest half.

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Kristina Šekrst profile image
Kristina Šekrst profile image
8/10 starsCompleted
  • 102 reviews
  • 102 completed
4 years, 2 months ago
I took this course when it was still a single course, and I'm writing this review a bit late, so I believe that the issues have been changed so far. I think the better way was to expand the course into a couple of more weeks, rather than splitting it into two courses. The teaching style was great, and I enjoyed it very much. The programming assignments were a bit difficult sometimes, but enjoyable. However, I don't think this is a beginner's course, since there's lot of talk about algorithmic complexity (which was awesome). Maybe some more examples could be added to illustrate the complexities with real-life algorithms.
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Kristina Šekrst profile image
Kristina Šekrst profile image
10/10 starsCompleted
  • 5 reviews
  • 5 completed
4 years, 11 months ago
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Jeanne Boyarsky profile image
Jeanne Boyarsky profile image
6/10 starsDropped
  • 33 reviews
  • 29 completed
5 years, 2 months ago
There are four modules in the course. Each comes with videos, homeworks (graded quizzes), a project (autograded with unit tests) and an application (like a lab.) They put a lot of effort into making the course hard and score meaningful. * The homework/quizzes only allow two attempts so no trial and error. Many of the questions required entering a number of formula so were impossible to guess. I thought these were great as they really checked your understand. * The projects used an autograder. This was good as you got immediate feedback. For the first two modules, the autograder gave useful feedback. For the third, not so much. Many of the test cases were too long/obscured. We were also given a standalone test. The problem is the test cases are so involved that it is difficult to see what is wrong. And my code worked for many simple cases. At some point this became too time consuming. I got close enough to get the point, bu... There are four modules in the course. Each comes with videos, homeworks (graded quizzes), a project (autograded with unit tests) and an application (like a lab.) They put a lot of effort into making the course hard and score meaningful. * The homework/quizzes only allow two attempts so no trial and error. Many of the questions required entering a number of formula so were impossible to guess. I thought these were great as they really checked your understand. * The projects used an autograder. This was good as you got immediate feedback. For the first two modules, the autograder gave useful feedback. For the third, not so much. Many of the test cases were too long/obscured. We were also given a standalone test. The problem is the test cases are so involved that it is difficult to see what is wrong. And my code worked for many simple cases. At some point this became too time consuming. I got close enough to get the point, but decided not to spend more time debugging. And the rest of the questions in the project relied on that question (at least for grading, they worked with the non-performant version of the code.) * The application (labs) were peer graded and allowed looking at the performance implication of the project. In some ways, this is nice. In others, if you couldn't get the project working, you couldn't do the lab. Ultimately, I dropped the class because it was too time consuming. It was way more than 7-10 hours of work per week.
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