R Course for Data Science Essentials

Essential Data Science in R

Resources
Essential Data Science in R

EVALUATE

Essential Data Science in REVALUATE

EVALUATE

Course Modules & Assignments

R: Describing a Numerical Data Set - Variables
- Using Statistical Functions on Vectors
- Coding Exercises

R: Importing and Describing Mixed Data Sets - Wrangling Data with Tidyverse
- Data Frames
- Coding Exercises

R: Using Statistical Tests to Compare Populations - Comparison Tests
- Coding Exercises

R: Using Statistical Tests to Describe Relationships - Correlation Tests
- Regression Analysis
- Coding Exercises

R Data Analysis Lab - R Data Analysis Lab

R: Creating Comparison and Composition Charts - Comparison Charts
- Composition Charts
- Coding Exercises

R: Creating Distribution Charts - Scatter Plots
- Box Plots and Histograms
- Coding Exercises

R: Creating Specialized Visualizations - Specialized Visualizations
- Coding Exercises

R: Communicating Data Using R Markdown - Presenting Data as a Document or Report
- Presenting Data as a Presentation
- Presenting Interactive Data
- Practice Exercises

Visualizing Data and Communicating Results with R Lab - Visualizing Data and Communicating Results with R Lab

EVALUATE

Constructing Knowledge Through Coding

Essential Data Science in R emphasizes the importance of students applying and exploring the information presented. With Codio’s interactive interface, a code editor accompanies each page with new concepts. This way, students can actively see how the computer responds to code, rather than simply showing the end result. In addition, the course content provides code snippets that allow students to become familiar with the language, as well as suggested avenues for investigation.

Auto-Graded Assessments

We believe in the value of active feedback, which is why students receive immediate, rich feedback. In addition to feedback on the validity of a specific answer, students will also be provided with an explanation that includes the complete solution, as well as the steps that reached that destination. There are a wide variety of questions — all of which are auto-graded, giving students a sense of their understanding of the material right after they are introduced to it and as they attempt harder and harder problems.

Lowering the Barrier to Entry

Essential Data Science in R reflects the need for computer science education to meet students where they are. Like any specialized community, computer science has its own jargon. The formal teaching of computer science should not burden students with the assumption that they are fluent in this special language. The material is presented in smaller units that are more manageable for the students. The same vocabulary and concepts are covered, but in a more approachable way — state things as plainly as possible, and, when appropriate, use images, tables, or lists.

RStudio Integration

RStudio is the industry-standard tool for programming in R. This powerful IDE is integrated directly into Codio. No installation of any kind is needed, so students can start programming in R right away!

Encouraging Customization Through Modularity

This R course content is not a one-size-fits-all solution. Rather, it implements a modular format. Natural breakpoints occur in the curriculum where instructors can make the changes they deem necessary. Instructors can re-name, re-order, or remove units.

Using Codio’s excellent content authoring tools, they can author new material. This modular approach gives instructors flexibility when designing the learner’s experience.