Learning Outcome
Learn how to organize high throughput data
Individual Course
Statistical Inference and Modeling for High-throughput Experiments
Course Length
4 weeks
2-4 hours per week
Featuring faculty from:
Harvard T.H. Chan School of Public Health
Enroll as Individual
Certificate Price:
$ 219
Enroll as Individual
Certificate Price:
$ 219
A focus on the techniques commonly used to perform statistical inference on high throughput data.
In this course you’ll learn various statistics topics including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory data analysis. We then introduce statistical modeling and how it is applied to high-throughput data. In particular, we will discuss parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation. We provide several examples of how these concepts are applied in next generation sequencing and microarray data. Finally, we will discuss hierarchical models and empirical bayes along with some examples of how these are used in practice. We provide R programming examples in a way that will help make the connection between concepts and implementation.
The course will be delivered via edX and connect learners around the world.
Prerequisites
PH525.1x and PH525.2x or basic programming, intro to statistics, intro to linear algebra
Self-Guided
EDX
Learning Outcome
Understand multiple comparison problems
Learning Outcome
Understand Family Wide Error Rates
- Learn from Harvard faculty
- Do it on your own time
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- Be part of the Harvard Community
Faculty
Your Instructor
Michael Love
Assistant Professor, UNC Gillings School of Global Public Health
Dr. Love received his bachelor’s in mathematics in 2005 from Stanford University, his master’s in statistics in 2010 from Stanford University, and his Ph.D. in Computational Biology in 2013 from the Freie Universität Berlin.
Your Instructor
Rafael Irizarry
Professor of Biostatistics, Harvard T.H. Chan School of Public Health
Rafael Irizarry is a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health and a Professor of Biostatistics and Computational Biology at the Dana Farber Cancer Institute. For the past 15 years, Dr. Irizarry’s research has focused on the analysis of genomics data. During this time, he has also taught several classes, all related to applied statistics. Dr. Irizarry is one of the founders of the Bioconductor Project, an open source and open development software project for the analysis of genomic data. His publications related to these topics have been highly cited and his software implementations widely downloaded.
Read full bio.
Complete your journey with this Professional Certificate Series
These courses can be bundled together to receive a Professional Certificate at a discounted price.
Learn More- 4 Courses
- 4 Months
- Earn Your Certificate
Introduction to Linear Models and Matrix Algebra
2-4 hours per week
Statistics and R
2-4 hours per week
High-Dimensional Data Analysis
2-4 hours per week
Ways to take this course
Audit or Pursue a Verified Certificate
A Verified Certificate costs $219 and provides unlimited access to full course materials, activities, tests, and forums. At the end of the course, learners who earn a passing grade can receive a certificate.
Alternatively, learners can Audit the course for free and have access to select course material, activities, tests, and forums. Please note that this track does not offer a certificate for learners who earn a passing grade.
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