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Perform RNASeq, ChIPSeq, and DNA methylation data analyses, using open source software, including R and Bioconductor.Free^{*}5 weeks longAvailable now

Learn advanced approaches to genomic visualization, reproducible analysis, data architecture, and exploration of cloudscale...Free^{*}5 weeks longAvailable now

The structure, annotation, normalization, and interpretation of genome scale assays.Free^{*}5 weeks longAvailable now

A focus on several techniques that are widely used in the analysis of highdimensional data.Free^{*}4 weeks longAvailable now

A focus on the techniques commonly used to perform statistical inference on high throughput data.Free^{*}4 weeks longAvailable now

Learn to use R programming to apply linear models to analyze data in life sciences.Free^{*}4 weeks longAvailable now

An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.Free^{*}4 weeks longAvailable now

Develop skills in digital research and visualization techniques across subjects and fields within the humanities.Free^{*}7 weeks longAvailable now

Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science.Free^{*}8 weeks longAvailable now

Show what you’ve learned from the Professional Certificate Program in Data Science.Free^{*}2 weeks longAvailable now

Learn basic data visualization principles and how to apply them using ggplot2.Free^{*}8 weeks longAvailable now

Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008.Free^{*}8 weeks longAvailable now

Learn inference and modeling: two of the most widely used statistical tools in data analysis.Free^{*}8 weeks longAvailable now

Keep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio.Free^{*}8 weeks longAvailable now

Learn to process and convert raw data into formats needed for analysis.Free^{*}8 weeks longAvailable now