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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教學大綱 - 您將從這門課程中學到什麼
週
1完成時間為 3 小時
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 個視頻 （總計 129 分鐘）, 3 個閱讀材料, 1 個測驗
21 個視頻
What is Statistics?2分鐘
Finding Statistics You Can Trust (4:44)4分鐘
Getting Help (3:44)3分鐘
What is Data? (4:28)4分鐘
Representing Data (5:23)5分鐘
Module 1 Overview (1:07)1分鐘
Reproducible Research (3:42)3分鐘
Achieving Reproducible Research (5:02)5分鐘
R Markdown (6:26)6分鐘
The Three Tables in Genomics (2:10)2分鐘
The Three Tables in Genomics (in R) (3:46)3分鐘
Experimental Design: Variability, Replication, and Power (14:17)14分鐘
Experimental Design: Confounding and Randomization (9:26)9分鐘
Exploratory Analysis (9:21)9分鐘
Exploratory Analysis in R Part I (7:22)7分鐘
Exploratory Analysis in R Part II (10:07)10分鐘
Exploratory Analysis in R Part III (7:26)7分鐘
Data Transforms (7:31)7分鐘
Clustering (8:43)8分鐘
Clustering in R (9:09)9分鐘
3 個閱讀材料
Syllabus10分鐘
Pre Course Survey10分鐘
Introduction and Materials10分鐘
1 個練習
Module 1 Quiz20分鐘
週
2完成時間為 2 小時
Module 2
This week we will cover preprocessing, linear modeling, and batch effects.
14 個視頻 （總計 97 分鐘）, 1 個測驗
14 個視頻
Dimension Reduction (12:13)12分鐘
Dimension Reduction (in R) (8:48)8分鐘
Pre-processing and Normalization (11:26)11分鐘
Quantile Normalization (in R) (4:49)4分鐘
The Linear Model (6:50)6分鐘
Linear Models with Categorical Covariates (4:08)4分鐘
Adjusting for Covariates (4:16)4分鐘
Linear Regression in R (13:03)13分鐘
Many Regressions at Once (3:50)3分鐘
Many Regressions in R (7:21)7分鐘
Batch Effects and Confounders (7:11)7分鐘
Batch Effects in R: Part A (8:18)8分鐘
Batch Effects in R: Part B (3:50)3分鐘
1 個練習
Module 2 Quiz20分鐘
週
3完成時間為 2 小時
Module 3
This week we will cover modeling non-continuous outcomes (like binary or count data), hypothesis testing, and multiple hypothesis testing.
15 個視頻 （總計 86 分鐘）, 1 個測驗
15 個視頻
Logistic Regression (7:03)7分鐘
Regression for Counts (5:02)5分鐘
GLMs in R (9:28)9分鐘
Inference (4:18)4分鐘
Null and Alternative Hypotheses (4:45)4分鐘
Calculating Statistics (5:11)5分鐘
Comparing Models (7:08)7分鐘
Calculating Statistics in R9分鐘
Permutation (3:26)3分鐘
Permutation in R (3:33)3分鐘
P-values (6:04)6分鐘
Multiple Testing (8:25)8分鐘
P-values and Multiple Testing in R: Part A (5:58)5分鐘
P-values and Multiple Testing in R: Part B (4:23)4分鐘
1 個練習
Module 3 Quiz20分鐘
週
4完成時間為 2 小時
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 個視頻 （總計 74 分鐘）, 1 個閱讀材料, 1 個測驗
14 個視頻
Gene Set Enrichment (4:19)4分鐘
More Enrichment (3:59)3分鐘
Gene Set Analysis in R (7:43)7分鐘
The Process for RNA-seq (3:59)3分鐘
The Process for Chip-Seq (5:25)5分鐘
The Process for DNA Methylation (5:03)5分鐘
The Process for GWAS/WGS (6:12)6分鐘
Combining Data Types (eQTL) (6:04)6分鐘
eQTL in R (10:36)10分鐘
Researcher Degrees of Freedom (5:49)5分鐘
Inference vs. Prediction (8:52)8分鐘
Knowing When to Get Help (2:31)2分鐘
Statistics for Genomic Data Science Wrap-Up (1:53)1分鐘
1 個閱讀材料
Post Course Survey10分鐘
1 個練習
Module 4 Quiz10分鐘
4.1
37 個審閱40%
完成這些課程後已開始新的職業生涯
38%
通過此課程獲得實實在在的工作福利
17%
加薪或升職
來自基因组数据科学所需的统计学的熱門評論
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!
I have really enjoyed the course and I have learnt different concepts relevant for my current study.\n\nYurany
講師
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
關於 约翰霍普金斯大学
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.
關於 基因组数据科学 專項課程
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.
This Specialization covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments. It teaches the most common tools used in genomic data science including how to use the command line, along with a variety of software implementation tools like Python, R, Bioconductor, and Galaxy.
This Specialization is designed to serve as both a standalone introduction to genomic data science or as a perfect compliment to a primary degree or postdoc in biology, molecular biology, or genetics, for scientists in these fields seeking to gain familiarity in data science and statistical tools to better interact with the data in their everyday work.
To audit Genomic Data Science courses for free, visit https://www.coursera.org/jhu, click the course, click Enroll, and select Audit. Please note that you will not receive a Certificate of Completion if you choose to Audit.
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