Showing 1–10 of 12 results
In this course you will learn how to program in R and how to use R for effective data analysis
Advanced R Programming$50.00
This course covers advanced topics in R programming that are necessary for developing powerful, robust, and reusable data science tools.
Building Data Visualization Tools$50.00
Visualization remains one of the most powerful ways draw conclusions from data, but the influx of new data types requires the development of new visualization techniques and building blocks. This course provides you with the skills for creating those new visualization building blocks.
Building R Packages$0.00
This course covers the primary means by which R software is organized and distributed to others. We cover R package development, writing good documentation and vignettes, writing robust software, cross-platform development, continuous integration tools, and distributing packages via CRAN and GitHub. Learners will produce R packages that satisfy the criteria for submission to CRAN.
Building R Packages$50.00
This course covers the primary means by which R software is organized and distributed to others.
Exploratory Data analysis$44.00
This course covers the essential exploratory techniques for summarizing data
Getting and Cleaning Data$44.00
Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained.
Introduction to Neurohacking$44.00
Neurohacking describes how to use the R programming language (https://cran.r-project.org/) and its associated package to perform manipulation, processing, and analysis of neuroimaging data
This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them.
This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.