Introduction to Linear Models and Matrix Algebra
2-4 hours per week • Start today
Individual Course
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 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 of high-dimensional data sets, 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.
Finally, we give a brief introduction to machine learning and apply it to high-throughput, large-scale 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.
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, OR PH525.3x
Self-Guided
edX
Understand Mathematical Distance
Explore Dimension Reduction
Understand Singular Value Decomposition and Principal Component Analysis
Your Instructor
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
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.
These courses can be bundled together to receive a Professional Certificate at a discounted price.
Learn More2-4 hours per week • Start today
2-4 hours per week • Start today
2-4 hours per week • Start today
Ways to take this course
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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