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

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.

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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, 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.

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.

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What's inside

Six courses

Introduction to Genomic Technologies

This course introduces the basic biology of modern genomics and the experimental tools used to measure it. We'll cover the Central Dogma of Molecular Biology and 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 needed to understand how data from next-generation sequencing experiments are generated and analyzed.

Python for Genomic Data Science

This class introduces the Python programming language and the iPython notebook.

Algorithms for DNA Sequencing

We will learn computational methods for analyzing DNA sequencing data. We will learn about DNA, genomics, and how DNA sequencing is used. We will use Python to implement algorithms and data structures and to analyze real genomes and DNA sequencing datasets.

Command Line Tools for Genomic Data Science

Introduces the commands needed to manage and analyze directories, files, and large sets of genomic data.

Bioconductor for Genomic Data Science

Learn to use Bioconductor tools for genomic data analysis. This course is part of the Genomic Big Data Specialization from Johns Hopkins University.

Statistics for Genomic Data Science

An introduction to the statistics used in popular genomic data science projects.

Learning objectives

  • Next generation sequencing experiments
  • Genomic technologies
  • Dna, rna and epigenetic patterns
  • Genome analysis

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