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.
提供：
Genomic Data Science の専門講座ジョンズ・ホプキンズ大学（Johns Hopkins University）このコースについて
34,171 最近の表示
受講生の就業成果
40%
コース終了後に新しいキャリアをスタートした
38%
コースが具体的なキャリアアップにつながった
17%
昇給や昇進につながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における8の7コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
約8時間で修了
英語
字幕：英語
習得するスキル
StatisticsData AnalysisR ProgrammingBiostatistics
受講生の就業成果
40%
コース終了後に新しいキャリアをスタートした
38%
コースが具体的なキャリアアップにつながった
17%
昇給や昇進につながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における8の7コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
約8時間で修了
英語
字幕：英語
提供：
ジョンズ・ホプキンズ大学（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.
シラバス - 本コースの学習内容
3時間で修了
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時間で修了
21件のビデオ （合計129分）, 3 readings, 1 quiz
21件のビデオ
What is Statistics?2 分
Finding Statistics You Can Trust (4:44)4 分
Getting Help (3:44)3 分
What is Data? (4:28)4 分
Representing Data (5:23)5 分
Module 1 Overview (1:07)1 分
Reproducible Research (3:42)3 分
Achieving Reproducible Research (5:02)5 分
R Markdown (6:26)6 分
The Three Tables in Genomics (2:10)2 分
The Three Tables in Genomics (in R) (3:46)3 分
Experimental Design: Variability, Replication, and Power (14:17)14 分
Experimental Design: Confounding and Randomization (9:26)9 分
Exploratory Analysis (9:21)9 分
Exploratory Analysis in R Part I (7:22)7 分
Exploratory Analysis in R Part II (10:07)10 分
Exploratory Analysis in R Part III (7:26)7 分
Data Transforms (7:31)7 分
Clustering (8:43)8 分
Clustering in R (9:09)9 分
3件の学習用教材
Syllabus10 分
Pre Course Survey10 分
Introduction and Materials10 分
1の練習問題
Module 1 Quiz20 分
2時間で修了
Module 2
This week we will cover preprocessing, linear modeling, and batch effects.
2時間で修了
14件のビデオ （合計97分）
14件のビデオ
Dimension Reduction (12:13)12 分
Dimension Reduction (in R) (8:48)8 分
Pre-processing and Normalization (11:26)11 分
Quantile Normalization (in R) (4:49)4 分
The Linear Model (6:50)6 分
Linear Models with Categorical Covariates (4:08)4 分
Adjusting for Covariates (4:16)4 分
Linear Regression in R (13:03)13 分
Many Regressions at Once (3:50)3 分
Many Regressions in R (7:21)7 分
Batch Effects and Confounders (7:11)7 分
Batch Effects in R: Part A (8:18)8 分
Batch Effects in R: Part B (3:50)3 分
1の練習問題
Module 2 Quiz20 分
2時間で修了
Module 3
This week we will cover modeling non-continuous outcomes (like binary or count data), hypothesis testing, and multiple hypothesis testing.
2時間で修了
15件のビデオ （合計86分）
15件のビデオ
Logistic Regression (7:03)7 分
Regression for Counts (5:02)5 分
GLMs in R (9:28)9 分
Inference (4:18)4 分
Null and Alternative Hypotheses (4:45)4 分
Calculating Statistics (5:11)5 分
Comparing Models (7:08)7 分
Calculating Statistics in R9 分
Permutation (3:26)3 分
Permutation in R (3:33)3 分
P-values (6:04)6 分
Multiple Testing (8:25)8 分
P-values and Multiple Testing in R: Part A (5:58)5 分
P-values and Multiple Testing in R: Part B (4:23)4 分
1の練習問題
Module 3 Quiz20 分
2時間で修了
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時間で修了
14件のビデオ （合計74分）, 1 reading, 1 quiz
14件のビデオ
Gene Set Enrichment (4:19)4 分
More Enrichment (3:59)3 分
Gene Set Analysis in R (7:43)7 分
The Process for RNA-seq (3:59)3 分
The Process for Chip-Seq (5:25)5 分
The Process for DNA Methylation (5:03)5 分
The Process for GWAS/WGS (6:12)6 分
Combining Data Types (eQTL) (6:04)6 分
eQTL in R (10:36)10 分
Researcher Degrees of Freedom (5:49)5 分
Inference vs. Prediction (8:52)8 分
Knowing When to Get Help (2:31)2 分
Statistics for Genomic Data Science Wrap-Up (1:53)1 分
1件の学習用教材
Post Course Survey10 分
1の練習問題
Module 4 Quiz10 分
レビュー
STATISTICS FOR GENOMIC DATA SCIENCE からの人気レビュー
RHにより2017年2月11日
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により2018年6月27日
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により2019年2月18日
sometimes termininology was used interchangeably, which can be confusing for a beginner but overall a good introduction to statistcs for genomic data analysis
Sにより2018年2月18日
Enjoyed it. One of better courses I have taken in Coursera. A good introduction to using statistics in Bioconductor with genomics data.
Genomic Data Science専門講座について
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.
よくある質問
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
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.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。
©2020 Coursera Inc.All rights reserved.
