High-Dimensional Data Analysis

By: Harvard University
Duration: 4 Weeks

Course Highlights

  • Dimension Reduction
  • Factor Analysis
  • Dealing with Batch Effects

A focus on several techniques that are widely used in the analysis of high-dimensional data.

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 principal component analysis.

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Course ByInstructor
Harvard UniversityRafael Irizarry, Michael Love

Course Details

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.

Finally, we give a brief introduction to machine learning and apply it to high-throughput data. We describe the general idea behind clustering analysis and descript K-means and hierarchical clustering and demonstrate how these are used in genomics and describe prediction algorithms such as k-nearest neighbors along with the concepts of training sets, test sets, error rates, and cross-validation.

What you will learn?

  • Mathematical Distance
  • Dimension Reduction
  • Singular Value Decomposition and Principal Component Analysis
  • Multiple Dimensional Scaling Plots
  • Factor Analysis
  • Dealing with Batch Effects

Other Details

Course Instructors

Rafael Irizarry: Professor of Biostatistics, T.H. Chan School of Public Health
Michael Love: Assistant Professor, Departments of Biostatistics and Genetics, UNC Gillings School of Global Public Health

Check Course Content, Faqs, Rating and other important information about this course.

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