Master advanced techniques for analyzing high-dimensional data using SVD, PCA, and machine learning methods.
Master advanced techniques for analyzing high-dimensional data using SVD, PCA, and machine learning methods.
This advanced course explores sophisticated techniques for analyzing high-dimensional data, with a focus on genomics applications. Students learn essential concepts including singular value decomposition, principal component analysis, and clustering methods. The curriculum covers dimension reduction, batch effect detection, and machine learning applications in high-throughput data analysis. Through practical implementations, participants develop skills in data visualization, factor analysis, and prediction algorithms, with special emphasis on genomics data interpretation.
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What you'll learn
Master mathematical concepts of distance and dimension reduction
Apply singular value decomposition and principal component analysis
Implement multiple dimensional scaling and factor analysis
Develop skills in clustering and machine learning applications
Handle batch effects in high-throughput experimental data
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
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Limited Access access
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Module Description
This advanced course provides comprehensive coverage of techniques for analyzing high-dimensional data sets. The curriculum begins with mathematical foundations of distance metrics and progresses through sophisticated analytical methods including SVD, PCA, and clustering algorithms. Students learn to handle practical challenges like batch effects in genomics data and implement machine learning approaches for large-scale data analysis. The course emphasizes both theoretical understanding and practical application through R programming.
Fee Structure
Instructors

1 Course
Senior Lecturer in Public Leadership at Harvard Kennedy School
Ronald Heifetz is among the world's foremost authorities on the practice and teaching of leadership. He speaks extensively and advises heads of governments, businesses, and nonprofit organizations across the globe. In 2016, President Juan Manuel Santos of Colombia highlighted Heifetz's advice in his Nobel Peace Prize Lecture. Heifetz founded the Center for Public Leadership at Harvard Kennedy School where he has taught for nearly four decades. He is the King Hussein bin Talal Senior Lecturer in Public Leadership. Heifetz played a pioneering role in establishing leadership as an area of study and education in the United States and at Harvard. His research addresses two challenges: developing a conceptual foundation for the analysis and practice of leadership; and developing transformative methods for leadership education, training, and consultation. Heifetz co-developed the adaptive leadership framework with Riley Sinder and Marty Linsky to provide a basis for leadership research and practice. His first book, Leadership Without Easy Answers (1994), is a classic in the field and one of the ten most assigned course books at Harvard and Duke Universities. Heifetz co-authored the best-selling Leadership on the Line: Staying Alive through the Dangers of Change with Marty Linsky, which serves as one of the primary go-to books for practitioners across sectors (2002, revised 2017). He then co-authored the field book, The Practice of Adaptive Leadership: Tools and Tactics for Changing your Organization and the World with Alexander Grashow and Marty Linsky (2009). Heifetz began his focus on transformative methods of leadership education and development in 1983. Drawing students from throughout Harvard's graduate schools and neighboring universities, his courses on leadership are legendary; his core course is considered the most influential in their career by Kennedy School alumni. His teaching methods have been studied extensively in doctoral dissertations and in Leadership Can Be Taught by Sharon Daloz Parks (2005). A graduate of Columbia University, Harvard Medical School, and the Kennedy School, Heifetz is both a physician and cellist. He trained initially in surgery before deciding to devote himself to the study of leadership in public affairs, business, and nonprofits. Heifetz completed his medical training in psychiatry, which provided a foundation to develop more powerful teaching methods and gave him a distinct perspective on the conceptual tools of political psychology and organizational behavior. As a cellist, he was privileged to study with the great Russian virtuoso, Gregor Piatigorsky.

14 Courses
Leading Expert in Computational Biology and Statistical Genomics
Dr. Michael Love serves as an Associate Professor in the Departments of Biostatistics and Genetics at UNC Gillings School of Global Public Health, where he also directs the Bioinformatics and Computational Biology PhD program. After completing his BS in Mathematics and MS in Statistics from Stanford University, he earned his PhD in Computational Biology from Freie Universität Berlin in 2013. His research focuses on developing statistical and computational methods for analyzing high-throughput sequencing data, particularly through his widely-used DESeq2 package for RNA sequencing analysis. As director of the Love Lab, he collaborates extensively with the Genetics Department and Lineberger Comprehensive Cancer Center, studying relationships between genetic variants and disease-related molecular changes. His expertise spans gene regulation, neuropsychiatric disorders, cancer genomics, and statistical software development. His contributions to the field have earned him multiple honors, including the UNC Center for Environmental Health and Susceptibility Recruitment Award and the Junior Faculty Development Award. Currently, he leads a $9.25 million NIH-funded study as part of the Impact of Genomic Variation on Function consortium, working to understand how genomic variation influences human health and disease.
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