Johns Hopkins University
Introduction to Genomic Technologies
Johns Hopkins University

Introduction to Genomic Technologies

This course is part of Genomic Data Science Specialization

Taught in English

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Steven Salzberg, PhD
Jeff Leek, PhD

Instructors: Steven Salzberg, PhD

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Course

Gain insight into a topic and learn the fundamentals

4.6

(4,507 reviews)

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96%

6 hours (approximately)
Flexible schedule
Learn at your own pace

Details to know

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Assessments

5 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.6

(4,507 reviews)

|

96%

6 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the Genomic Data Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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There are 4 modules in this course

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".

What's included

8 videos2 readings1 quiz

In this module, you'll learn about polymerase chain reaction, next generation sequencing, and applications of sequencing.

What's included

3 videos1 quiz

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.

What's included

6 videos1 quiz

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.

What's included

10 videos2 readings2 quizzes

Instructors

Instructor ratings
4.7 (755 ratings)
Steven Salzberg, PhD
Johns Hopkins University
2 Courses130,730 learners

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