Statistics for Genomic Data Science
Description
When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan .
- Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
- Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.
About 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.
Created by: Johns Hopkins University-
Taught by: Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Each course is like an interactive textbook, featuring pre-recorded…

Frequently asked questions
There are no frequently asked questions yet. If you have any more questions or need help, contact our customer service.
When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan .
- Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
- Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.
About 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.
Created by: Johns Hopkins University-
Taught by: Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from your peersConnect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
CertificatesEarn official recognition for your work, and share your success with friends, colleagues, and employers.
Johns Hopkins University The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.Syllabus
WEEK 1
Module 1
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.
21 videos, 3 readings expand
- Video: Welcome to Statistics for Genomic Data Science
- Reading: Syllabus
- Reading: Pre Course Survey
- Reading: Introduction and Materials
- Video: What is Statistics?
- Video: Finding Statistics You Can Trust (4:44)
- Video: Getting Help (3:44)
- Video: What is Data? (4:28)
- Video: Representing Data (5:23)
- Video: Module 1 Overview (1:07)
- Video: Reproducible Research (3:42)
- Video: Achieving Reproducible Research (5:02)
- Video: R Markdown (6:26)
- Video: The Three Tables in Genomics (2:10)
- Video: The Three Tables in Genomics (in R) (3:46)
- Video: Experimental Design: Variability, Replication, and Power (14:17)
- Video: Experimental Design: Confounding and Randomization (9:26)
- Video: Exploratory Analysis (9:21)
- Video: Exploratory Analysis in R Part I (7:22)
- Video: Exploratory Analysis in R Part II (10:07)
- Video: Exploratory Analysis in R Part III (7:26)
- Video: Data Transforms (7:31)
- Video: Clustering (8:43)
- Video: Clustering in R (9:09)
Graded: Module 1 Quiz
WEEK 2
Module 2
This week we will cover preprocessing, linear modeling, and batch effects.
14 videos expand
- Video: Module 2 Overview (1:12)
- Video: Dimension Reduction (12:13)
- Video: Dimension Reduction (in R) (8:48)
- Video: Pre-processing and Normalization (11:26)
- Video: Quantile Normalization (in R) (4:49)
- Video: The Linear Model (6:50)
- Video: Linear Models with Categorical Covariates (4:08)
- Video: Adjusting for Covariates (4:16)
- Video: Linear Regression in R (13:03)
- Video: Many Regressions at Once (3:50)
- Video: Many Regressions in R (7:21)
- Video: Batch Effects and Confounders (7:11)
- Video: Batch Effects in R: Part A (8:18)
- Video: Batch Effects in R: Part B (3:50)
Graded: Module 2 Quiz
WEEK 3
Module 3
This week we will cover modeling non-continuous outcomes (like binary or count data), hypothesis testing, and multiple hypothesis testing.
15 videos expand
- Video: Module 3 Overview (1:07)
- Video: Logistic Regression (7:03)
- Video: Regression for Counts (5:02)
- Video: GLMs in R (9:28)
- Video: Inference (4:18)
- Video: Null and Alternative Hypotheses (4:45)
- Video: Calculating Statistics (5:11)
- Video: Comparing Models (7:08)
- Video: Calculating Statistics in R
- Video: Permutation (3:26)
- Video: Permutation in R (3:33)
- Video: P-values (6:04)
- Video: Multiple Testing (8:25)
- Video: P-values and Multiple Testing in R: Part A (5:58)
- Video: P-values and Multiple Testing in R: Part B (4:23)
Graded: Module 3 Quiz
WEEK 4
Module 4
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.
14 videos, 1 reading expand
- Video: Module 4 Overview (1:21)
- Video: Gene Set Enrichment (4:19)
- Video: Enrichment (3:59)
- Video: Gene Set Analysis in R (7:43)
- Video: The Process for RNA-seq (3:59)
- Video: The Process for Chip-Seq (5:25)
- Video: The Process for DNA Methylation (5:03)
- Video: The Process for GWAS/WGS (6:12)
- Video: Combining Data Types (eQTL) (6:04)
- Video: eQTL in R (10:36)
- Video: Researcher Degrees of Freedom (5:49)
- Video: Inference vs. Prediction (8:52)
- Video: Knowing When to Get Help (2:31)
- Video: Statistics for Genomic Data Science Wrap-Up (1:53)
- Reading: Post Course Survey
Graded: Module 4 Quiz
Share your review
Do you have experience with this course? Submit your review and help other people make the right choice. As a thank you for your effort we will donate $1.- to Stichting Edukans.There are no frequently asked questions yet. If you have any more questions or need help, contact our customer service.