Python for Genomic Data Science
Master Python programming for genomic data analysis. Learn essential programming concepts and Biopython for bioinformatics.
Master Python programming for genomic data analysis. Learn essential programming concepts and Biopython for bioinformatics.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Genomic Data Science Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.3
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English
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What you'll learn
Learn Python programming fundamentals for genomic applications
Master data structures and control flow in Python
Develop skills in handling biological data with Biopython
Gain practical experience in file processing and manipulation
Understand how to apply programming concepts to genomic analysis
Skills you'll gain
This course includes:
3.2 Hours PreRecorded video
9 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course provides an introduction to Python programming with a focus on genomic data science applications. The curriculum covers fundamental programming concepts including data structures, control flow, functions, and file I/O, all within the context of biological data analysis. Students also learn to use Biopython, a powerful library for handling biological data. The course combines theoretical programming knowledge with practical applications in genomics, preparing students for real-world bioinformatics tasks.
Week One
Module 1 · 2 Hours to complete
Week Two
Module 2 · 1 Hours to complete
Week Three
Module 3 · 1 Hours to complete
Week Four
Module 4 · 2 Hours to complete
Fee Structure
Instructors
Distinguished Computational Biology and Genomics Expert at Johns Hopkins
Dr. Steven Salzberg serves as Professor of Biomedical Engineering, Computer Science, and Biostatistics at Johns Hopkins University, where he also directs the Center for Computational Biology and is a member of the McKusick-Nathans Institute of Genetic Medicine. His research group specializes in developing cutting-edge computational methods for DNA analysis using the latest sequencing technologies, making significant contributions to gene finding, genome assembly, comparative genomics, and evolutionary genomics. Beyond his groundbreaking research in DNA and RNA sequencing with next-generation technology, Dr. Salzberg is also known for his public engagement through his Forbes science blog, where he addresses critical issues ranging from pseudoscience and alternative medicine to gene patents and higher education, making complex scientific concepts accessible to the public.
Leading Computational Biologist and Gene Analysis Pioneer at Johns Hopkins
Dr. Mihaela Pertea serves as an Assistant Professor in the Center for Computational Biology at Johns Hopkins University School of Medicine, bringing expertise earned through her Computer Science PhD from the university's Whiting School of Engineering. Her groundbreaking research in gene finding and genome sequence analysis has produced essential open-source software systems used in annotating crucial species including Plasmodium falciparum, Arabidopsis thaliana, and several others. Her significant impact on the field is reflected in her publications garnering over 16,000 citations, placing her among the top 1% most cited researchers in her field. Beyond research, Dr. Pertea has developed specialized programming courses tailored for graduate students with strong biological backgrounds but limited computer science experience, bridging the gap between biology and computational analysis.
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4.3 course rating
1,713 ratings
Frequently asked questions
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