Microcredential

Genomic Data Science

Johns Hopkins University via Coursera Specialization

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Coursera Specialization
Paid Course
English
Certificate Available
43 weeks long, 2 hours a week

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Overview

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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, Bioconductor, and Galaxy. 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. To audit Genomic Data Science courses for free, visit https://www.coursera.org/jhu, click the course, click Enroll, and select Audit. Please note that you will not receive a Certificate of Completion if you choose to Audit.

Courses

17 Reviews
4 weeks long, 4-6 hours a week
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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.
11 Reviews
4 weeks long, 3-5 hours a week
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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.

This is the first course in the Genomic Data Science Specialization.
12 Reviews
4 weeks long, 3-5 hours a week
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Learn to use the tools that are available from the Galaxy Project. This is the second course in the Genomic Big Data Science Specialization.
9 Reviews
4 weeks long, 3-5 hours a week
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Introduces to the commands that you need to manage and analyze directories, files, and large sets of genomic data. This is the fourth course in the Genomic Big Data Science Specialization from Johns Hopkins University.
4 Reviews
4 weeks long, 3-5 hours a week
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Learn to use tools from the Bioconductor project to perform analysis of genomic data. This is the fifth course in the Genomic Big Data Specialization from Johns Hopkins University.
12 Reviews
4 weeks long, 3-5 hours a week
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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.
3 Reviews
4 weeks long, 5-7 hours a week
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An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University.
0 Reviews
10 weeks long, 2-4 hours a week
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In this culminating project, you will deploy the tools and techniques that you've mastered over the course of the specialization. You'll work with a real data set to perform analyses and prepare a report of your findings.

Taught by

Ben Langmead, PhD, Jacob Pritt, James Taylor, PhD, Jeff Leek, PhD, Kasper Daniel Hansen, PhD, Liliana Florea, PhD, Mihaela Pertea, PhD and Steven Salzberg, PhD

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Microcredential

Genomic Data Science

Overview

Class Central Tips

Courses

Algorithms for DNA Sequencing

Introduction to Genomic Technologies