4.2
stars
318 ratings

Jeff Leek, PhD

30,639 already enrolled

Offered By

Johns Hopkins University

About this Course

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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.

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Shareable Certificate

Earn a Certificate upon completion

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Approx. 9 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

Skills you will gain

Statistics
Data Analysis
R Programming
Biostatistics

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Approx. 9 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

Instructor

Instructor rating

4.8/5 (29 Ratings)

Jeff Leek, PhD

Associate Professor, Biostatistics

Bloomberg School of Public Health

1,499,294 Learners

32 Courses

Offered by

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 - What you will learn from this course

Content Rating91%(1,495 ratings)

Week

Week 1

3 hours to complete

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.

3 hours to complete

21 videos (Total 129 min), 3 readings, 1 quiz

21 videos

Welcome to Statistics for Genomic Data Science2m

What is Statistics?2m

Finding Statistics You Can Trust (4:44)4m

Getting Help (3:44)3m

What is Data? (4:28)4m

Representing Data (5:23)5m

Module 1 Overview (1:07)1m

Reproducible Research (3:42)3m

Achieving Reproducible Research (5:02)5m

R Markdown (6:26)6m

The Three Tables in Genomics (2:10)2m

The Three Tables in Genomics (in R) (3:46)3m

Experimental Design: Variability, Replication, and Power (14:17)14m

Experimental Design: Confounding and Randomization (9:26)9m

Exploratory Analysis (9:21)9m

Exploratory Analysis in R Part I (7:22)7m

Exploratory Analysis in R Part II (10:07)10m

Exploratory Analysis in R Part III (7:26)7m

Data Transforms (7:31)7m

Clustering (8:43)8m

Clustering in R (9:09)9m

3 readings

Syllabus10m

Pre Course Survey10m

Introduction and Materials10m

1 practice exercise

Module 1 Quiz30m

Week

Week 2

2 hours to complete

Module 2

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

2 hours to complete

14 videos (Total 97 min)

14 videos

Module 2 Overview (1:12)1m

Dimension Reduction (12:13)12m

Dimension Reduction (in R) (8:48)8m

Pre-processing and Normalization (11:26)11m

Quantile Normalization (in R) (4:49)4m

The Linear Model (6:50)6m

Linear Models with Categorical Covariates (4:08)4m

Adjusting for Covariates (4:16)4m

Linear Regression in R (13:03)13m

Many Regressions at Once (3:50)3m

Many Regressions in R (7:21)7m

Batch Effects and Confounders (7:11)7m

Batch Effects in R: Part A (8:18)8m

Batch Effects in R: Part B (3:50)3m

1 practice exercise

Module 2 Quiz30m

Week

Week 3

2 hours to complete

Module 3

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

2 hours to complete

15 videos (Total 86 min)

15 videos

Module 3 Overview (1:07)1m

Logistic Regression (7:03)7m

Regression for Counts (5:02)5m

GLMs in R (9:28)9m

Inference (4:18)4m

Null and Alternative Hypotheses (4:45)4m

Calculating Statistics (5:11)5m

Comparing Models (7:08)7m

Calculating Statistics in R9m

Permutation (3:26)3m

Permutation in R (3:33)3m

P-values (6:04)6m

Multiple Testing (8:25)8m

P-values and Multiple Testing in R: Part A (5:58)5m

P-values and Multiple Testing in R: Part B (4:23)4m

1 practice exercise

Module 3 Quiz30m

Week

Week 4

2 hours to complete

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.

2 hours to complete

14 videos (Total 74 min), 1 reading, 1 quiz

14 videos

Module 4 Overview (1:21)1m

Gene Set Enrichment (4:19)4m

More Enrichment (3:59)3m

Gene Set Analysis in R (7:43)7m

The Process for RNA-seq (3:59)3m

The Process for Chip-Seq (5:25)5m

The Process for DNA Methylation (5:03)5m

The Process for GWAS/WGS (6:12)6m

Combining Data Types (eQTL) (6:04)6m

eQTL in R (10:36)10m

Researcher Degrees of Freedom (5:49)5m

Inference vs. Prediction (8:52)8m

Knowing When to Get Help (2:31)2m

Statistics for Genomic Data Science Wrap-Up (1:53)1m

1 reading

Post Course Survey10m

1 practice exercise

Module 4 Quiz30m

Reviews

4.2

59 reviews

5 stars
54.48%
4 stars
26.93%
3 stars
11.14%
2 stars
2.47%
1 star
4.95%

TOP REVIEWS FROM STATISTICS FOR GENOMIC DATA SCIENCE

by TKMar 24, 2019

Very good course and useful understanding statistical aspects of data.

by CJJul 15, 2019

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

by SFeb 18, 2018

Enjoyed it. One of better courses I have taken in Coursera. A good introduction to using statistics in Bioconductor with genomics data.

by MRMar 3, 2020

Great course as a starting point for statistical genomics!

View all reviews

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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. If you only want to read and view the course content, you can audit the course for free.
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Statistics for Genomic Data Science

About this Course

Skills you will gain