EdX

Data Analysis for Life Sciences 1: Statistics and R (edX)

Offered by HarvardX, Harvard University,

An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.

Class Deals by MOOC List - Click here and see EdX's Active Discounts, Deals, and Promo Codes.

A newer version of this course is available here:
Statistics and R

We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R. We provide R programming examples in a way that will help make the connection between concepts and implementation. Problem sets requiring R programming will be used to test understanding and ability to implement basic data analyses. We will use visualization techniques to explore new data sets and determine the most appropriate approach. We will describe robust statistical techniques as alternatives when data do not fit assumptions required by the standard approaches. By using R scripts to analyze data, you will learn the basics of conducting reproducible research.
Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

The courses in this series will be released sequentially each month and are self-paced:
PH525.1x: Statistics and R for the Life Sciences
PH525.2x: Introduction to Linear Models and Matrix Algebra
PH525.3x: Statistical Inference and Modeling for High-throughput Experiments
PH525.4x: High-Dimensional Data Analysis
PH525.5x: Introduction to Bioconductor: annotation and analysis of genomes and genomic assays
PH525.6x: High-performance computing for reproducible genomics
PH525.7x: Case studies in functional genomics

Note: This course is currently not available.

Related Courses

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

Learn to use R programming to apply linear models to analyze data in life sciences. Matrix Algebra underlies many of the current tools for experimental design and the analysis of high-dimensional data. In this introductory data analysis course, we will use matrix algebra to represent the linear models that commonly used to model differences between experimental units. We perform statistical inference on these differences. Throughout the course we will use the R programming language.

Self Paced

Self-Paced

Algebra Linear Algebra Matrix

Data Science: Inference and Modeling (edX)

EdX

HarvardX,Harvard University

Data Science: Inference and Modeling (edX)

Statistics & Data Analysis Data Science

Learn inference and modeling, two of the most widely used statistical tools in data analysis. Statistical inference and modeling are indispensable for analyzing data affected by chance, and thus essential for data scientists. In this course, you will learn these key concepts through a motivating case study on election forecasting.

Self Paced

Self-Paced

Statistics Inference Modeling

Probability and Statistics I: A Gentle Introduction to Probability (edX)

EdX

Georgia Institute of Technology,GTx

Probability and Statistics I: A Gentle Introduction to Probability (edX)

Statistics & Data Analysis

This course provides an introduction to basic probability concepts. Our emphasis is on applications in science and engineering, with the goal of enhancing modeling and analysis skills for a variety of real-world problems. In order to make the course completely self-contained (and to bring back long-lost memories), we’ll start off with Bootcamp lessons to review concepts from set theory and calculus.

Self Paced

Self-Paced

Calculus Statistics Analysis

Estadística Aplicada a los Negocios (edX)

EdX

Galileo University,GalileoX

Estadística Aplicada a los Negocios (edX)

Statistics & Data Analysis

Aprende las principales herramientas y técnicas de la estadística descriptiva y la estadística inferencial para analizar e interpretar datos desde la perspectiva de negocios facilitando la toma de decisiones. Este curso proporciona una introducción al análisis de datos en base a las principales herramientas estadísticas, enfocándose en la estadística descriptiva y la estadística inferencial.

Self Paced

Self-Paced

Statistics Econometrics Data Analysis

Introduction to Digital Humanities (edX)

EdX

HarvardX,Harvard University

Introduction to Digital Humanities (edX)

Humanities Computer Science

Develop skills in digital research and visualization techniques across subjects and fields within the humanities. This course will show you how to manage the many aspects of digital humanities research and scholarship. Whether you are a student or scholar, librarian or archivist, museum curator or public historian — or just plain curious — this course will help you bring your area of study or interest to new life using digital tools.

Self Paced

Self-Paced

Data Analysis Command Line Interface Digital Humanities

Introductory Statistics : Analyzing Data Using Graphs and Statistics (edX)

EdX

Seoul National University,SNUx

Introductory Statistics : Analyzing Data Using Graphs and Statistics (edX)

Economics & Finance Statistics & Data Analysis

This course teaches basic statistical concepts and explores many compelling applications of statistical methods using real-life applications of Statistics. Why do we study statistics? The field of statistics provides professionals and scientists withconceptual foundations and useful techniques for evaluating ideas, testing theories, and - ultimately -uncovering the truth in any situation.

Self Paced

Self-Paced

Statistics Regression Data Analysis

EdX

Davidson College,DavidsonX

RShiny for Everyone (edX)

Statistics & Data Analysis

Use R’s Shiny package to create data-driven, interactive web applications. In this course, you will use R Shiny to create an interactive web application that highlights the biodiversity of America’s National Parks. Your application will feature an interactive map, biodiversity calculator, trail journal and species images. Using R Shiny, you will expand your data analysis and visualization skills while developing your workflow through web application deployment.

Self Paced

Self-Paced

Data Analysis Web Applications Data Visualization

EdX

DoaneX,Doane University

BioStatistics (edX)

Sci: Biology & Life Sciences Statistics & Data Analysis

This college-level, credit-eligible Biostatistics course will teach you the skills required for success in future analytical studies in biology. In this undergraduate-level biostatistics course, the learners will be introduced to the use of statistics and study designs in biology. Upon successful completion of this course, learners will be able to design experimental, quasi-experimental and observational studies that will meet regulatory guidelines; collect, analyze, and interpret data using appropriate statistical tools.

Self Paced

Self-Paced

Biology Statistics Biostatistics

Técnicas Cuantitativas y Cualitativas para la Investigación (edX)

EdX

Universitat Politècnica de València,UPValenciaX

Técnicas Cuantitativas y Cualitativas para la Investigación (edX)

Statistics & Data Analysis

El curso pretende acercar al alumno al método científico y, en concreto, cómo éste se aplica al estudio y análisis de los métodos de casos. El curso que se propone es ideal para investigadores y alumnos que se encuentren cursando trabajos de fin de grado, trabajos de fin de máster o realizando tesis, así como todos aquellos del área de la administración que quieran realizar un análisis cuantitativo o cualitativo en sus estudios.

Self Paced

Self-Paced

Research Methods Data Analysis Scientific Methods

EdX

KU Leuven University,KULeuvenX

How to analyze a microbiome (edX)

Sci: Biology & Life Sciences Statistics & Data Analysis

Learn common analysis techniques to make sense of microbial sequencing data. Microorganisms play a major role in the biosphere and within our bodies, but only a tiny fraction has been cultured so far. Microbiome data, that is the genetic information of microorganisms, is therefore an important window into the hidden microbial world.

Self Paced

Self-Paced

Biology Data Analysis Microbiome

EdX

HarvardX,Harvard University

Data Science: Linear Regression (edX)

Statistics & Data Analysis Data Science

Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science. Linear regression is commonly used to quantify the relationship between two or more variables. It is also used to adjust for confounding.

Self Paced

Self-Paced

Linear Regression Data Science R Language

EdX

Indian Institute of Management, Bangalore,IIMBx

Predictive Analytics (edX)

Management & Leadership Statistics & Data Analysis

Master the tools of predictive analytics in this statistics based analytics course. Decision makers often struggle with questions such as: What should be the right price for a product? Which customer is likely to default in his/her loan repayment? Which products should be recommended to an existing customer? Finding right answers to these questions can be challenging yet rewarding.

Self Paced

5-12 Weeks

Statistics Analysis Regression