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Statistical Analysis for High-throughput Data

This course is part of Data Analysis for Life Sciences.

Dive into statistical analysis techniques for high-throughput experiments in this comprehensive course from Harvard. Learn essential concepts including multiple testing, error rates, false discovery rates, and statistical modeling. Master practical applications in next-generation sequencing and microarray data analysis using R programming. The course covers parametric distributions, maximum likelihood estimation, hierarchical models, and empirical Bayes methods with hands-on implementation.

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Statistical Analysis for High-throughput Data

This course includes

4 Weeks

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

19,023

Audit For Free

What you'll learn

  • Master multiple testing procedures and error rate control

  • Understand false discovery rates and q-values

  • Apply statistical modeling to high-throughput data

  • Implement hierarchical models and Bayesian statistics

  • Perform exploratory data analysis for large datasets

  • Use R programming for statistical analysis implementation

Skills you'll gain

Statistical Inference
R Programming
Data Analysis
Bioinformatics
Multiple Testing
Error Rate Analysis
Statistical Modeling
Bayesian Statistics
High-throughput Data
Exploratory Analysis

This course includes:

PreRecorded video

Graded assignments, exams

Access on Mobile, Tablet, Desktop

Limited Access access

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Module Description

This comprehensive course focuses on statistical techniques for analyzing high-throughput experimental data. Students learn about multiple testing problems, error rate control procedures, and false discovery rates. The curriculum covers statistical modeling techniques, including parametric distributions and maximum likelihood estimation, with applications in next-generation sequencing and microarray analysis. Advanced topics include hierarchical models and empirical Bayes methods, all implemented through R programming examples to bridge theory and practice.

Fee Structure

Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Data Analysis for Life Sciences

Instructors

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.

Michael Love
Michael Love

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.

Statistical Analysis for High-throughput Data

This course includes

4 Weeks

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

19,023

Audit For Free

Testimonials

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Frequently asked questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.