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
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Gain insight into a topic and learn the fundamentals.
1,810 reviews
8 hours to complete
Flexible schedule
Learn at your own pace
89%
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Skills you'll gain
- Computational Logic
- Package and Software Management
- Object Oriented Programming (OOP)
- Data Manipulation
- Programming Principles
- File I/O
- Jupyter
- Python Programming
- Data Structures
- Bioinformatics
- Skills section collapsed. Showing 9 of 10 skills.
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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.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
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
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Showing 3 of 1810
PL
Reviewed on Jul 1, 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.
MK
Reviewed on Dec 13, 2016
Started very easy, but became quite hard at the end. Nicely structured and conveyes everything that is needed. However, sometimes I missed why specific tools have been used and not others.
JD
Reviewed on Oct 6, 2017
Easy to understand and very powerful examples. Not just it made me familiar with python, it also made it easy for me to teach to my students and inspire them to pursue python further.
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