https://github.com/swamikannan/genomics-data-science-specialization
Programming assignments for Genomics Data Science Specialization by John Hopkins and Coursera
https://github.com/swamikannan/genomics-data-science-specialization
Last synced: about 1 month ago
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Programming assignments for Genomics Data Science Specialization by John Hopkins and Coursera
- Host: GitHub
- URL: https://github.com/swamikannan/genomics-data-science-specialization
- Owner: SwamiKannan
- License: mit
- Created: 2022-08-29T11:37:43.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2022-10-25T14:37:42.000Z (almost 3 years ago)
- Last Synced: 2025-03-03T16:48:50.067Z (7 months ago)
- Language: Jupyter Notebook
- Size: 3.53 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
- License: LICENSE
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README
# Genomics Data Science Specialization from John Hopkins
With genomics sparks a revolution in medical discoveries, it becomes imperative to be able to better understand the genome, and be able to leverage the data and information from genomic datasets. Genomic Data Science is the field that applies statistics and data science to the genome.
This Specialization covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments. It teaches the most common tools used in genomic data science including how to use the command line, along with a variety of software implementation tools like Python, R, and Bioconductor.
This Specialization is designed to serve as both a standalone introduction to genomic data science or as a perfect compliment to a primary degree or postdoc in biology, molecular biology, or genetics, for scientists in these fields seeking to gain familiarity in data science and statistical tools to better interact with the data in their everyday work.
## Courses:
### Course 1: Introduction to Genomic Technologies (Not included in this repo)
This course introduces you to the basic biology of modern genomics and the experimental tools that we use to measure it. We'll introduce the Central Dogma of Molecular Biology and cover how next-generation sequencing can be used to measure DNA, RNA, and epigenetic patterns. You'll also get an introduction to the key concepts in computing and data science that you'll need to understand how data from next-generation sequencing experiments are generated and analyzed.
### Course 2: Python for Genomics (Not included in this repo - may include the last course project later)
This class provides an introduction to the Python programming language and the iPython notebook.
### Course 3: Algorithms for DNA Sequencing
We will learn computational methods -- algorithms and data structures -- for analyzing DNA sequencing data. We will learn a little about DNA, genomics, and how DNA sequencing is used. We will use Python to implement key algorithms and data structures and to analyze real genomes and DNA sequencing datasets.