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Introduction to Genomic Technologies

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Free Online Course: Introduction to Genomic Technologies provided by Coursera is a comprehensive online course, which lasts for 4 weeks long, 6-7 hours worth of material. The course is taught in English and is free of charge. Upon completion of the course, you can receive an e-certificate from Coursera. Introduction to Genomic Technologies is taught by Steven Salzberg, Jeff Leek and James Taylor.

Johns Hopkins University via Coursera
4 weeks long, 6-7 hours worth of material
Upcoming
Overview

  • 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.

Syllabus

    • Overview
      • In this Module, you can expect to study topics of "Just enough molecular biology", "The genome", "Writing a DNA sequence", "Central dogma", "Transcription", "Translation", and "DNA structure and modifications".
    • Measurement Technology
      • In this module, you'll learn about polymerase chain reaction, next generation sequencing, and applications of sequencing.
    • Computing Technology
      • The lectures for this module cover a few basic topics in computing technology. We'll go over the foundations of computer science, algorithms, memory and data structures, efficiency, software engineering, and computational biology software.
    • Data Science Technology
      • In this module on Data Science Technology, we'll be covering quite a lot of information about how to handle the data produced during the sequencing process. We'll cover reproducibility, analysis, statistics, question types, the central dogma of inference, analysis code, testing, prediction, variation, experimental design, confounding, power, sample size, correlation, causation, and degrees of freedom.

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2.8, based on 12 Course votes at GetYourEducation
4.6, based on 4107 Course reviews at Coursera

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