Data Analytics with R Training in Jefferson City

Enroll in or hire us to teach our Data Analytics with R class in Jefferson City, Missouri by calling us @303.377.6176. Like all HSG classes, Data Analytics with R may be offered either onsite or via instructor led virtual training. Consider looking at our public training schedule to see if it is scheduled: Public Training Classes
Provided there are enough attendees, Data Analytics with R may be taught at one of our local training facilities.

Answers to Popular Questions:

Yes, this class can be tailored to meet your specific training needs.
Yes, we provide Python consulting services.
Yes, group discounts are provided.

Course Description

This course provides an overview of the basic to advanced features of the R programming language. It is presented as a combination of lectures and hands-on exercises. Course Topics: ... Data Science Basics ... R Language Basics ... Intermediate R ... Charting and Graphing ... Statistical Processing ... Introduction to Text Analytics and the tm Package ... Introduction to Collaborative Filtering ... Implementing a Recommendation Engine
Course Length: 3 Days
Course Tuition: $1190 (US)


Prior to attending this course, students should be familiar with statistical analysis concepts. Prior programming or scripting experience is strongly recommended but not required.

Course Outline

Upon completion of this course attendees will be able to:
• Describe the data science basics
• Write R programs that perform data analysis
• Use scalars, vectors, and functions in the programs
• Use matrices, factors, and data frames
• Generate R graphs and charts
• Perform statistical processing
• Implement a recommendation engine using collaborative filtering
I. Data Science Basics
II. R Language Basics
A. Scalars
B. Vectors
C. Functions
III. Intermediate R
A. Matrices
B. Factors
C. Data Frames
IV. Charting and Graphing
V. Statistical Processing
A. Linear Regression
B. Logistic Regression
VI. Text Analytics
A. Introduction to Text Analytics
B. Introduction to the ‘tm’ Package
VII. Collaborative Filtering
A. Introduction
B. Implementing a Recommendation Engine

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