Harvard University is offering free online course on High-Dimensional Data Analysis. A focus on several techniques that are widely used in the analysis of high-dimensional data.
Students will learn about the batch effect: the most challenging data analytical problem in genomics today and describe how the techniques can be used to detect and adjust for batch effects. The course will start on July 1, 2017.
Course At A Glance
Length: 4 weeks
Effort: 2-4 hours pw
Subject: Biology & Life Sciences
Institution: Harvard University and edx
Certificate Available: Yes, Add a Verified Certificate for $49
Session: Course Starts on July 1, 2017
Harvard University is devoted to excellence in teaching, learning, and research, and to developing leaders in many disciplines who make a difference globally. Harvard faculty are engaged with teaching and research to push the boundaries of human knowledge. The University has twelve degree-granting Schools in addition to the Radcliffe Institute for Advanced Study.
About This Course
If you’re interested in data analysis and interpretation, then this is the data science course for you. We start by learning the mathematical definition of distance and use this to motivate the use of the singular value decomposition (SVD) for dimension reduction and multi-dimensional scaling and its connection to principle component analysis. We will learn about the batch effect: the most challenging data analytical problem in genomics today and describe how the techniques can be used to detect and adjust for batch effects. Specifically, we will describe the principal component analysis and factor analysis and demonstrate how these concepts are applied to data visualization and data analysis of high-throughput experimental data.
Why Take This Course?
This is a free online course. This MOOC will be offered with Video Transcripts in English. Applicants can get a verified certificate.
- Mathematical Distance
- Dimension Reduction
- Singular Value Decomposition and Principal Component Analysis
- Multiple Dimensional Scaling Plots
- Factor Analysis
- Dealing with Batch Effects
- Basic Machine Learning Concepts
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.
Michael Love is a postdoctoral fellow with Dr. Irizarry in the Department of Biostatistics at the Dana Farber Cancer Institute and Harvard T.H. Chan School of Public Health.
PH525.1x and PH525.2x or basic programming, intro to statistics, intro to linear algebra, OR PH525.3x
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