Data Analysis for Life Sciences by Harvard

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Skills Covered: Data Analysis, Statistics, R, Linear Models, Matrix Algebra, Statistical Inference, Modeling for High-throughput Experiments, High-Dimensional Data Analysis, Random variables, Distributions, p-values, confidence intervals, Exploratory Data Analysis, Non-parametric statistics, Matrix algebra, Linear models, QR decomposition, Statistical Modeling, Hierarchical Models, Bayesian Statistics, Mathematical Distance, Dimension Reduction, Singular Value Decomposition, Component Analysis, Dimensional Scaling Plots, Factor Analysis, Batch Effects, Clustering, Heatmaps, Machine Learning Concepts

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Technological advances have transformed fields that rely on data by providing a wealth of information ready to be analyzed. From working with single genes to comparing entire genomes, biomedical research groups around the world are producing more data than they can handle and the ability to interpret this information is a key skill for any practitioner. The skills necessary to work with these massive datasets are in high demand, and this series will help you learn those skills.

Using the open-source R programming language, you’ll gain a nuanced understanding of the tools required to work with complex life sciences and genomics data. You’ll learn the mathematical concepts — and the data analytics techniques — that you need to drive data-driven research. From a strong foundation in statistics to specialized R programming skills, this series will lead you through the data analytics landscape step-by-step.

Taught by Rafael Irizarry from the Harvard T.H. Chan School of Public Health, these courses will enable new discoveries and will help you improve individual and population health. If you’re working in the life sciences and want to learn how to analyze data, enroll now to take your research to the next level.



  • Basic statistical concepts and R programming skills for analyzing data in the life sciences.
  • The underlying math of linear models useful for data analysis in the life sciences.
  • The techniques used to perform statistical inference on high-throughput and high-dimensional data.
  • Several techniques widely used in the analysis of high-dimensional data.



  • 32% of full-time data scientists started learning machine learning or data science through a MOOC, while 27% were self-taught. 
  • Data Scientists are few in number and high in demand. (source: TechRepublic)

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Data Analysis for Life Sciences by Harvard
Data Analysis for Life Sciences by Harvard

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