Exploring Neural Data

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6/10 stars
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Course Details

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FREE

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  • On demand

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
4906 reviews

Course Description

Try your hand at understanding the brain by learning to analyze neural data yourself! Working with real neural data sets from neuroscience research labs, you’ll learn data analysis techniques so you can discover for yourself how the brain works.
Reviews 6/10 stars
1 Review for Exploring Neural Data

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Henrietta Livingstone profile image
Henrietta Livingstone profile image
6/10 starsTaking Now
  • 1 review
  • 0 completed
5 years, 11 months ago
No neurology background is required: the basics of not only the theory of neural systems but the practice of laboratory work in the field is presented clearly and engagingly by the lecturers, which was perfect for me as a newcomer to the subject. However, this is primarily a data analysis course, so if you're after depth in neural science, you might look elsewhere. I have a strong background in computer science, so it is difficult for me to gauge this, but I suspect that, despite the whirlwind tour of python in the first week, some familiarity with basic programming control structures would make the course more enjoyable, even if it was only at the level of say Code Academy. I enjoyed the material on python libraries numpy, matplotlib and pandas. The assessed work includes four auto-graded programming assignments and associated multi-choice quizzes, and a final project. Each assignment focuses on specific analyses of specific data... No neurology background is required: the basics of not only the theory of neural systems but the practice of laboratory work in the field is presented clearly and engagingly by the lecturers, which was perfect for me as a newcomer to the subject. However, this is primarily a data analysis course, so if you're after depth in neural science, you might look elsewhere. I have a strong background in computer science, so it is difficult for me to gauge this, but I suspect that, despite the whirlwind tour of python in the first week, some familiarity with basic programming control structures would make the course more enjoyable, even if it was only at the level of say Code Academy. I enjoyed the material on python libraries numpy, matplotlib and pandas. The assessed work includes four auto-graded programming assignments and associated multi-choice quizzes, and a final project. Each assignment focuses on specific analyses of specific data sets. The data sets are interesting, but there's no cohesive overview of common approaches and the areas they're best applied to. This would be a four star review if not for the autograder. On a couple of assignments, my code worked flawlessly (and without overfitting) on all the practice datasets, but a couple of the grader tests failed without any response beyond "fail". With no further information to be wrung out of the system, and not willing to waste time with endless rounds of outguess-the- grader, for the first time in my MOOC career I had to just write a few marks off. (By way of comparison, I'm simultaneously taking Pavel Pevzner's brilliant Bioinformatics Algorithms I . It too gives a binary success/fail response on "code challenge" submissions, but in this case it is always very clear what is being tested, and there's never yet been a time where my code produced the correct output on the practice data sets but failed to on the test one. I'm running at full marks.) Each assignment also has a peer assessed portion, but these simply check that data plots match a detailed rubric, and there's a minimum of subjectivity involved. Only the other hand, the project is worth 40% of the final grade, is largely self-directed, and entirely peer assessed. If this is not your cup of tea or you don't feel taking your chances with which peer your work is assigned to, this may not be the best course for you.
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