Data Analysis for Life Sciences XSeries

Currently, biomedical research groups around the world are producing more data than they can handle.
The training and skills acquired by taking the Data Analysis for Life Sciences XSeries will result in greater success in biological discovery and improving individual and population health.
In this XSeries, you will gain the tools to analyze and interpret life sciences data. You will learn the basic statistical concepts and R programming skills necessary for analyzing real data.
R is an open source free statistical software and is the most widely used data analysis platforms among academic statisticians.
Taught by Rafael Irizarry from the Harvard T.H. Chan School of Public Health, who for the past 15 years has focused on the analysis of genomics data, this XSeries is perfect for anyone in the life sciences who wants to learn how to analyze data. Problem sets will require coding in the R language to ensure learners fully grasp and master key concepts.

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Introduction to Linear Models and Matrix Algebra (edX) EdX
HarvardX,Harvard University

Introduction to Linear Models and Matrix Algebra (edX)

Sci: Biology & Life Sciences Statistics & Data Analysis

Discover the essential principles of Linear Models and Matrix Algebra in this introductory data analysis course on edX. Perfect for those interested in life sciences, this program teaches you how to represent complex analyses with matrix algebra and perform statistical inference using R programming. Enhance your understanding of experimental design and high-dimensional data analysis.

Self Paced
Self-Paced
Algebra Linear Algebra Matrix
Details
Statistics and R (edX) EdX
HarvardX,Harvard University

Statistics and R (edX)

Sci: Biology & Life Sciences Statistics & Data Analysis

Dive into the world of data analysis with 'Statistics and R' on edX. This course is perfect for beginners in the life sciences who want to understand basic statistical concepts and learn how to use R programming to analyze data. Gain proficiency in computing p-values and constructing confidence intervals, all while enhancing your R skills.

Self Paced
Self-Paced
Statistics Data Analysis Statistical Inference
Details
Statistical Inference and Modeling for High-throughput Experiments (edX) EdX
HarvardX,Harvard University

Statistical Inference and Modeling for High-throughput Experiments (edX)

Sci: Biology & Life Sciences Statistics & Data Analysis

Dive into Statistical Inference and Modeling for High-throughput Experiments to gain a deep understanding of statistical techniques applied to large-scale biological datasets. This course covers essential topics such as error rate controlling procedures, false discovery rates, q-values, and parametric modeling with applications in high-throughput data analysis.

Self Paced
Self-Paced
Statistics Modeling Statistical Inference
Details
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