Real Time Experts
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Learn how to use R Programming from beginner level to advanced techniques which is taught by experienced working professionals. With our R Programming Training in Bangalore you’ll learn concepts in expert level with practical manner.
R Programming is a powerful statistical programming language which is used for predictive modelling and other data mining related techniques. R programming can be used for data manipulation,data aggregation,statistical Modelling,Creating charts and plots. R programming is becoming the most sought after skill in the field of analytics for its open source credibility. It is a simple programming language which requires no pre-requisites unlike other programming languages. There are many spectacular packages available in R that will help in a brief data analysis. R can also be collaborated with other data management tools like excel, access, Oracle, sql server which makes it a powerful tool.
R programming along with a substantial knowledge of statistics can help candidates to have a great career in data Analytics. R is also an widely used tool in many big firms like top Banks, IT, Retail, Healthcare, Pharma, Supply chain and logistics firms. Analyzing large datasets can be done in a shorter period with the help of R programming. There is a huge shortage in the market for professionals with skills in R programming which makes it more interesting to pursue. Since R is a free software it is being widely used which creates a lot of opportunities for professional who are looking to pursue a career in R Programming.
In order to become a successful professional in the field of analytics real time applications should be studied in detail. Hands on Experience with the combination of statistical concepts will be provided by only experts who are dealing with real scenarios in R programming on a daily basis in their respective industry
Introduction to R
R as a language
Working with data in R
The R ecosystem
Why use R?
Installation and setup
Date and time
Importing data from multiple sources/formats like .csv, .txt, .xlsx, SAS and SPSS files
Exporting data to multiple formats
Handling data frames: filtering, sorting, merging
PLYR package for easy data manipulation
Commonly used built in functions
Writing user defined functions
The "apply" family of functions
Basic statistics in R
Graphics in R
Graphics for exploratory data analysis
Standard graphic displays
The R environment
R in the cloud
Statistical analysis with R
Generalized linear models
Advanced statistical modeling with R
Introduction to Writing R Packages
Integrating with other tools.
Real time problems.
Data manipulation with data.table package.