About 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
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
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Approx. 9 hours to complete
English

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.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 9 hours to complete
English

Offered by

Placeholder

Johns Hopkins University

Syllabus - What you will learn from this course

Content RatingThumbs Up91%(1,247 ratings)Info
Week
1

Week 1

3 hours to complete

Module 1

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
Week
2

Week 2

2 hours to complete

Module 2

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
Week
3

Week 3

2 hours to complete

Module 3

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
Week
4

Week 4

2 hours to complete

Module 4

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

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About the Genomic Data Science Specialization

Genomic Data Science

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