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
Sponsored by Coursera Learning Team
66,096 already enrolled
Gain insight into a topic and learn the fundamentals.
4.3
(1,742 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 (244 ratings)Offered by
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4.3
1,742 reviews
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Showing 3 of 1742
DK
5
Reviewed on May 11, 2018
I learned a lot. The final exam was more difficult than the course content, but I think it was critical for ensuring that I was able to apply the course content.
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.
PL
4
Reviewed on Jul 2, 2016
This course was in line with my expectations. Sometimes exercises were a bit out of context. I would have probably dedicated more time to Biopython.
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