This event has been UPDATED since it was first published. View the UPDATED & Detailed R Programming language Training course for beginners Information here.

4 Weeks R Programming language Training course is being delivered from February 9, 2021 - March 4, 2021 for 16 Hours over 4 weeks, 8 sessions, 2 sessions per week, 2 hours per session.

• All Published Ticket Prices are in US Dollars
• The course will be taught in English language

4 Weeks R Programming language Development Training Schedule

• February 9, 2021 - March 4, 2021 US Pacific time
• 4 Weeks | 2 Hours on Tuesdays, 2 Hours on Thursdays every week US Pacific time
• 5:30 PM - 7:30 PM US Pacific time each of those days
• Please click here to add your city name and check your local date and time for the first session to be held on February 9, 2021 at 5.30 PM US Pacific Time.

Features and Benefits

• 16 Hours, 8 sessions, 4 weeks of total Instructor-led and guided training
• Training material, instructor handouts and access to useful resources on the cloud provided
• Practical Hands-on Lab exercises provided
• Actual code and scripts provided
• Real-life Scenarios

Course Objectives

• Understand programming fundamentals of R language
• Understand various data import methods in R
• Understand the Data Manipulation in R
• Create visualizations and Plots using R
• Understand and Implement Linear Regression
• Perform Text Analysis
• Understand Machine Learning concepts
• Real-time implementation of R on a live project and provide Business Insights

Course Prerequisites

Programming background like C, C++, Python will be an added advantage but not mandatory to learn R, but introductory statistics is a prerequisite.

Who can take this course?

• Software engineers and data analysts
• Business intelligence professionals
• SAS developers wanting to learn open source technology
• Those aspiring for a career in data science
• Professionals and Students looking to enter the Data Science industry

Course Outline

1. Essential to R programming

• An Introduction to R
• Introduction to the R language
• Programming statistical graphics
• Programming with R
• Simulation
• Computational linear algebra
• Numerical optimization

2. Data Manipulation Techniques using R programming

• Data in R
• Reading and Writing Data
• R and Databases
• Dates
• Factors
• Subscribing
• Character Manipulation
• Data Aggregation
• Reshaping Data

3. Statistical Applications using R programming

• Basics
• The R Environment
• Probability and distributions
• Descriptive statistics and graphics
• One- and two-sample tests
• Regression and correlation
• Analysis of variance and the Kruskal–Wallis test
• Tabular data
• Power and the computation of sample size
• Advanced data handling
• Multiple Regression
• Linear models
• Logistic regression
• Survival analysis
• Rates and Poisson regression
• Nonlinear curve fitting