Course Details
Course Outline
1 - From Excel or SAS to R (Optional)
Common challenges with Excel / SASThe R EnvironmentHello, R
2 - Working with R Studio
RshinyRpresentationsRmarkdown
3 - R Basics
Simple Math with RWorking with VectorsFunctionsComments and Code StructureUsing Packages
4 - Vectors
Vector PropertiesCreating, Combining, and IteratoratingPassing and Returning Vectors in FunctionsLogical Vectors
5 - Reading and Writing
Text ManipulationFactors
6 - Dates
Working with DatesDate Formats and formattingTime Manipulation and Operations
7 - Multiple Dimensions
Adding a second dimensionIndices and named rows and columns in a MatrixMatrix calculationn-Dimensional ArraysData FramesLists
8 - R in Data Science
AI Grouping TheoryK-meansLinear RegressionLogistic RegressionElastic Net
9 - R with MadLib
Importing and Exporting static Data (CSV, Excel)Using Libraries with CRANK-means with MadlibRegression with MadlibOther libraries
10 - Data Visualization
Powerful Data through Visualization: Communicating the MessageTechniques in Data VisualizationData Visualization ToolsExamples
11 - Databases, Data lakes & Additional Topics
Building connections to Databases and Data lakes, for both Python and R (using Hive server)Methods to “query” data from database and data lakes, for both Python and RCreating and passing macro variables. Specifically, R sprint, paste, paste0, and paste3 (not sure of the equivalent in Python).
12 - R with Hadoop
Overview of HadoopOverview of Distributed DatabasesOverview of PigOverview of MahoutExploiting Hadoop clusters with RHadoop, Mahout, and R
13 - Business Rule Systems
Rule Systems in the EnterpriseEnterprise Service BussesDrools & Using R with Drools
14 - R with AWS
Best practices for working with AWS (completely outside of R and Python)
Actual course outline may vary depending on offering center. Contact your sales representative for more information.
Who is it For?
Target Audience
This course, geared for Data Analyst and Data Scientists who need to learn the essentials of how to program in R. Incoming students should have prior experience working with Excel or SAS, and should know the basics of SQL. Students should have intermediate-level experience in their field, and prior experience working with programming languages.
Other Prerequisites
This course, geared for Data Analyst and Data Scientists who need to learn the essentials of how to program in R. Incoming students should have prior experience working with Excel or SAS, and should know the basics of SQL. Students should have intermediate-level experience in their field, and prior experience working with programming languages.
Follow On Courses: Our core R and Python programming, data science, analytics, AI and machine learning training courses provide students with a solid foundation for continued learning based on role, goals, or their areas of specialty. Please inquire for next step recommendations based on your goals