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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Genomic Data Science SpecializationJohns Hopkins UniversityAbout this Course
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Learner Career Outcomes
40%
started a new career after completing these courses
38%
got a tangible career benefit from this course
17%
got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
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Course 7 of 8 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 9 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish
Skills you will gain
StatisticsData AnalysisR ProgrammingBiostatistics
Learner Career Outcomes
40%
started a new career after completing these courses
38%
got a tangible career benefit from this course
17%
got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 7 of 8 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 9 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish
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
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
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
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
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
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
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
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
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
TOP REVIEWS FROM STATISTICS FOR GENOMIC DATA SCIENCE
by RHFeb 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.
by ZMJun 27, 2018
The professor is really enthusiasm, so I was really impreesed by him. And his teaching is brief, and I can learn key points through the lectures. Great course!
by NSFeb 18, 2019
sometimes termininology was used interchangeably, which can be confusing for a beginner but overall a good introduction to statistcs for genomic data analysis
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.
About the Genomic Data Science Specialization
With genomics sparks a revolution in medical discoveries, it becomes imperative to be able to better understand the genome, and be able to leverage the data and information from genomic datasets. Genomic Data Science is the field that applies statistics and data science to the genome.
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