Professional Data Science and R Training
We provide hands-on training sessions to drive your organization's data science capabilities.
From introduction to advanced courses in R, Python, SQL and Git, to machine learning and industry-specific modeling, our professional training courses can be modified to fit your organization's needs.
Our instructors are either published authors in the field of data science or professors at prominent universities. Most are both.
COURSES WE OFFER
Introduction to R
We begin with an introduction to the Posit user interface, familiarizing students with the R programming language and providing a general overview of its efficiency and capacity via tools and packages.
Building upon the general concepts of Introduction to R, this course leads to more advanced programming concepts, including assigning variables and using dplyr. Topics include: writing functions, control statements, iterating with loops, reshaping data, group manipulation and more.
Workflow & Visualization in R
Among the most important steps in analysis is visualization. This course focuses on ggplot2 (a powerful tool for plotting) and RMarkdown (used to interweave R code with ordinary text to produce easy-to-modify, well-formatted, automated data analysis reports).
Shiny is a new paradigm in data analytics, providing interactive dashboards and advanced analytics in R. Shiny supports the development of web-based dashboards to run statistics, machine learning and deep learning methods. Training in Shiny covers key aspects of dashboard design and development, code optimization and reactivity.
Working in the Tidyverse
The Tidyverse is defined as a collection of R packages that share common philosophies, grammar and data structures. This class will be a hands-on instructional tutorial covering topics on the workflow of data manipulation (import, tidy, transform, visualize, model).
Elegant Reporting and Presentations in RMarkdown
Compelling presentation of an analysis is as important as the analysis itself, and yet is too frequently treated as an afterthought. This course covers numerous means of communicating data, Markdown for PDF and HTML reports and ioslides for presentations.
Advanced Statistics in R: Modeling and Analytics
We begin with the basics (descriptive statistics like mean and variance) and progress to more advanced modeling techniques (like linear models and time series). The focus will be on applied programming, though theoretical properties and derivations will be taught where appropriate.
Machine Learning in R
We focus on the available methods for implementing machine learning algorithms in R, and will examine some of the underlying theory. We will explore several models which includes linear regression, elastic net, tree-based models, clustering, bootstrapping and cross-validation.
High Performance Computing in R
In the era of complex data, in which more of the data is stored in the cloud, handling data in volume has become a necessity. There are a variety of ways in which R handles the processing of large amounts of data. This course focuses on improving processing speed in R.
Building R Packages
One of the benefits of coding is that your work is repeatable. This course will walk through the whole process of converting code into a package, writing documentation for help files and writing tests to ensure everything works.
Applied Programming in R
Apply the techniques you learned from our courses to future or existing project work. These one-day capstone projects have included automation, performance improvement, dashboards and other tools to solve a key business problems.
Integrating C++ and R
Looking to migrate from C++ to R? Learn how to write high performance C++ code that integrates smoothly into R, with topics including: Rcpp, C++ data types, writing functions, syntactic sugar and the differences between C++ and R style
Modeling with Caret
This course will utilize the Caret package to quickly and easily run machine learning models. Caret seamlessly standardizes tools to make the data science process more efficient and accessible for users. In addition to pre-processing functions, over 200 models can be fitted through the package.
Forecasting with R
This course walks through the whole process of fitting time series models. First, we learn about the time series object in R and learn about various ways to plot time-based data. Then we work our way through forecasting techniques, starting with simple methods like mean forecasting and working our way to exponential and ARMA models.
Putting R in Production
Using R in production is easier than ever thanks to tools like renv, plumber and Docker. In this course we start with a fresh Posit project and git repo. We will populate it with functionality and expose the project as a REST API using the plumber package.