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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About this Course
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지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 전문 분야의 8개 강좌 중 7번째 강좌:
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영어
자막: 영어
귀하가 습득할 기술
StatisticsData AnalysisR ProgrammingBiostatistics
학습자 경력 결과
40%
이 강좌를 수료한 후 새로운 경력 시작하기
38%
이 강좌를 통해 확실한 경력상 이점 얻기
17%
급여 인상 또는 승진하기
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 전문 분야의 8개 강좌 중 7번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
완료하는 데 약 8시간 필요
영어
자막: 영어
제공자:
존스홉킨스대학교
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.
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 3시간 필요
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.
완료하는 데 3시간 필요
21개 동영상 (총 129분), 3 readings, 1 quiz
21개의 동영상
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
3개의 읽기 자료
Syllabus10m
Pre Course Survey10m
Introduction and Materials10m
1개 연습문제
Module 1 Quiz20m
완료하는 데 2시간 필요
Module 2
This week we will cover preprocessing, linear modeling, and batch effects.
완료하는 데 2시간 필요
14개 동영상 (총 97분)
14개의 동영상
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
1개 연습문제
Module 2 Quiz20m
완료하는 데 2시간 필요
Module 3
This week we will cover modeling non-continuous outcomes (like binary or count data), hypothesis testing, and multiple hypothesis testing.
완료하는 데 2시간 필요
15개 동영상 (총 86분)
15개의 동영상
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
1개 연습문제
Module 3 Quiz20m
완료하는 데 2시간 필요
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.
완료하는 데 2시간 필요
14개 동영상 (총 74분), 1 reading, 1 quiz
14개의 동영상
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
1개의 읽기 자료
Post Course Survey10m
1개 연습문제
Module 4 Quiz10m
검토
4.2
STATISTICS FOR GENOMIC DATA SCIENCE의 최상위 리뷰
RH 제공Feb 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.
ZM 제공Jun 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!
NS 제공Feb 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
S 제공Feb 18, 2018
Enjoyed it. One of better courses I have taken in Coursera. A good introduction to using statistics in Bioconductor with genomics data.
LR 제공May 22, 2016
I have really enjoyed the course and I have learnt different concepts relevant for my current study.\n\nYurany
TK 제공Mar 24, 2019
Very good course and useful understanding statistical aspects of data.
MR 제공Mar 3, 2020
Great course as a starting point for statistical genomics!
TN 제공Jan 22, 2020
new material would be great as well as new datasets
PM 제공Mar 25, 2020
Pretty good but a little superficial and outdated.
Genomic Data Science 전문 분야 정보
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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