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 October 15, 2020 - November 10, 2020 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

16 Hours R Programming Training Schedule

• October 15, 2020 - November 10, 2020 US Pacific time
• 4 Weeks | 2 hours on Tuesdays, 2 hours on Thursdays every week US Pacific time
• 8:30 AM - 10:30 AM US Pacific time each of those days
• Please click here to add your location and check your local date and time for first session to be held on October 15, 2020 at 8:30 AM 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 Pre-requisites

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