This class provides an introduction to the Python programming language and the iPython notebook. This is the third course in the Genomic Big Data Science Specialization from Johns Hopkins University.
Python for Genomic Data Science
Python for Genomic Data Science
This course is part of Genomic Data Science Specialization
Instructors: Mihaela Pertea, PhD
Access provided by New York State Department of Labor
67,422 already enrolled
Gain insight into a topic and learn the fundamentals.
4.3
(1,759 reviews)
8 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
89%
Most learners liked this course
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Assessments
9 assignments
Taught in English
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This course is part of the Genomic Data Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
This week we will have an overview of Python and take the first steps towards programming.
What's included
5 videos3 readings2 assignments
In this module, we'll be taking a look at Data Structures and Ifs and Loops.
What's included
4 videos2 assignments
In this module, we have a long three-part lecture on Functions as well as a 10-minute look at Modules and Packages.
What's included
4 videos2 assignments
In this module, we have another long three-part lecture, this time about Communicating with the Outside, as well as a final lecture about Biopython.
What's included
4 videos2 readings3 assignments
Instructors
Instructor ratings
4.2 (250 ratings)Offered by
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4.3
1,759 reviews
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Showing 3 of 1759
NH
5
Reviewed on May 13, 2022
It was a great course, even if you do not have any knowlege about Python. I work with Python but it was so interesting for me especially with the application in genomic data science.Thank you so much.
SS
4
Reviewed on Apr 29, 2020
The lectures were very good. However, the final exam was poor with unclear instructions on how to solve the questions. Also, the final exam questions and the lectures were not very related.
DD
4
Reviewed on Jul 19, 2019
Compared to the lectures, the final exam was very difficult. It would be great if the professors provide more practical examples in the lectures similar to exam questions.
Recommended if you're interested in Data Science
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