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⇱ Statistics for Genomic Data Science | Coursera

Statistics for Genomic Data Science

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Statistics for Genomic Data Science

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Gain insight into a topic and learn the fundamentals.
4.2

380 reviews

9 hours to complete
Flexible schedule
Learn at your own pace
91%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
4.2

380 reviews

9 hours to complete
Flexible schedule
Learn at your own pace
91%
Most learners liked this course

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

There are 4 modules in this course

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.

This course is structured to hit the key conceptual ideas of normalization, exploratory analysis, linear modeling, testing, and multiple testing that arise over and over in genomic studies.

What's included

21 videos3 readings1 assignment

21 videos•Total 129 minutes
  • Welcome to Statistics for Genomic Data Science•3 minutes
  • What is Statistics?•3 minutes
  • Finding Statistics You Can Trust (4:44)•5 minutes
  • Getting Help (3:44)•4 minutes
  • What is Data? (4:28)•4 minutes
  • Representing Data (5:23)•5 minutes
  • Module 1 Overview (1:07)•1 minute
  • Reproducible Research (3:42)•4 minutes
  • Achieving Reproducible Research (5:02)•5 minutes
  • R Markdown (6:26)•6 minutes
  • The Three Tables in Genomics (2:10)•2 minutes
  • The Three Tables in Genomics (in R) (3:46)•4 minutes
  • Experimental Design: Variability, Replication, and Power (14:17)•14 minutes
  • Experimental Design: Confounding and Randomization (9:26)•9 minutes
  • Exploratory Analysis (9:21)•9 minutes
  • Exploratory Analysis in R Part I (7:22)•7 minutes
  • Exploratory Analysis in R Part II (10:07)•10 minutes
  • Exploratory Analysis in R Part III (7:26)•7 minutes
  • Data Transforms (7:31)•8 minutes
  • Clustering (8:43)•9 minutes
  • Clustering in R (9:09)•9 minutes
3 readings•Total 30 minutes
  • Syllabus•10 minutes
  • Pre Course Survey•10 minutes
  • Introduction and Materials•10 minutes
1 assignment•Total 30 minutes
  • Module 1 Quiz•30 minutes

This week we will cover preprocessing, linear modeling, and batch effects.

What's included

14 videos1 assignment

14 videos•Total 97 minutes
  • Module 2 Overview (1:12)•1 minute
  • Dimension Reduction (12:13)•12 minutes
  • Dimension Reduction (in R) (8:48)•9 minutes
  • Pre-processing and Normalization (11:26)•11 minutes
  • Quantile Normalization (in R) (4:49)•5 minutes
  • The Linear Model (6:50)•7 minutes
  • Linear Models with Categorical Covariates (4:08)•4 minutes
  • Adjusting for Covariates (4:16)•4 minutes
  • Linear Regression in R (13:03)•13 minutes
  • Many Regressions at Once (3:50)•4 minutes
  • Many Regressions in R (7:21)•7 minutes
  • Batch Effects and Confounders (7:11)•7 minutes
  • Batch Effects in R: Part A (8:18)•8 minutes
  • Batch Effects in R: Part B (3:50)•4 minutes
1 assignment•Total 30 minutes
  • Module 2 Quiz•30 minutes

This week we will cover modeling non-continuous outcomes (like binary or count data), hypothesis testing, and multiple hypothesis testing.

What's included

15 videos1 assignment

15 videos•Total 86 minutes
  • Module 3 Overview (1:07)•1 minute
  • Logistic Regression (7:03)•7 minutes
  • Regression for Counts (5:02)•5 minutes
  • GLMs in R (9:28)•9 minutes
  • Inference (4:18)•4 minutes
  • Null and Alternative Hypotheses (4:45)•5 minutes
  • Calculating Statistics (5:11)•5 minutes
  • Comparing Models (7:08)•7 minutes
  • Calculating Statistics in R•10 minutes
  • Permutation (3:26)•3 minutes
  • Permutation in R (3:33)•4 minutes
  • P-values (6:04)•6 minutes
  • Multiple Testing (8:25)•8 minutes
  • P-values and Multiple Testing in R: Part A (5:58)•6 minutes
  • P-values and Multiple Testing in R: Part B (4:23)•4 minutes
1 assignment•Total 30 minutes
  • Module 3 Quiz•30 minutes

In this week we will cover a lot of the general pipelines people use to analyze specific data types like RNA-seq, GWAS, ChIP-Seq, and DNA Methylation studies.

What's included

14 videos1 reading1 assignment

14 videos•Total 74 minutes
  • Module 4 Overview (1:21)•1 minute
  • Gene Set Enrichment (4:19)•4 minutes
  • More Enrichment (3:59)•4 minutes
  • Gene Set Analysis in R (7:43)•8 minutes
  • The Process for RNA-seq (3:59)•4 minutes
  • The Process for Chip-Seq (5:25)•5 minutes
  • The Process for DNA Methylation (5:03)•5 minutes
  • The Process for GWAS/WGS (6:12)•6 minutes
  • Combining Data Types (eQTL) (6:04)•6 minutes
  • eQTL in R (10:36)•11 minutes
  • Researcher Degrees of Freedom (5:49)•6 minutes
  • Inference vs. Prediction (8:52)•9 minutes
  • Knowing When to Get Help (2:31)•3 minutes
  • Statistics for Genomic Data Science Wrap-Up (1:53)•2 minutes
1 reading•Total 10 minutes
  • Post Course Survey•10 minutes
1 assignment•Total 30 minutes
  • Module 4 Quiz•30 minutes

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Instructor

Instructor ratings
4.5 (38 ratings)
Johns Hopkins University
32 Courses•1,762,091 learners

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Learner reviews

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Showing 3 of 380

MR
·

Reviewed on Mar 3, 2020

Great course as a starting point for statistical genomics!

RH
·

Reviewed on Feb 11, 2017

Overall, a very good course. Not without its flaws (inconsistent video audio levels), but I have walked away knowing far more about Genomic Data Science than I expected to.

CJ
·

Reviewed on Jul 15, 2019

It is really great that told me lots of basic statistical information that I didn't know.

Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

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