Course Curriculum

• Introduction to R Programming
• Business Analytics
• Analytics concepts
• The importance of R in analytics
• R Language community and eco-system
• Usage of R in industry
• Installing R and other packages
• Perform basic R operations using command line
• Usage of IDE R Studio and various GUI

R Programming Concepts

• The datatypes in R and its uses
• Built-in functions in R
• Subsetting methods
• Summarize data using functions
• Use of functions like head(), tail(), for inspecting data
• Use-cases for problem solving using R

Data Manipulation in R

• Various phases of Data Cleaning
• Functions used in Inspection
• Data Cleaning Techniques
• Uses of functions involved
• Use-cases for Data Cleaning using R

Data Import Techniques in R

• Import data from spreadsheets and text files into R
• Importing data from statistical formats
• Packages installation for database import
• Connecting to RDBMS from R using ODBC and basic SQL queries in R
• Web Scraping
• Other concepts on Data Import Techniques

Exploratory Data Analysis (EDA) using R

• What is EDA?
• Why do we need EDA?
• Goals of EDA
• Types of EDA
• Implementing of EDA
• Boxplots, cor() in R
• EDA functions
• Multiple packages in R for data analysis
• Some fancy plots
• Use-cases for EDA using R

Data Visualization in R

• Storytelling with Data
• Principle tenets
• Elements of Data Visualization
• Infographics vs Data Visualization
• Data Visualization & Graphical functions in R
• Plotting Graphs
• Customizing Graphical Parameters to improvise the plots
• Various GUIs
• Spatial Analysis
• Other Visualization concepts

Exam & Certification

• Once you complete this master’s program, you will receive the course completion certificate by ICIT

ICIT Course Completion Certificate will be awarded upon the completion of the project work (after the expert review) and upon scoring at least 50% marks in the quiz. ICIT certification is well recognized in top  MNCs .

Who should attend?

 R programming training is pursued by working IT professionals who want to enhance their skills in data analysis, statistical analysis, machine learning, data mining.

FAQ's

1) Explain about data import in R language (get solved code examples for hands-on experience)

R Commander is used to import data in R language. To start the R commander GUI, the user must type in the command Rcmdr into the console. There are 3 different ways in which data can be imported in R language-

•           Users can select the data set in the dialog box or enter the name of the data set (if they know).

•           Data can also be entered directly using the editor of R Commander via Data->New Data Set. However, this works well when the data set is not too large.

•           Data can also be imported from a URL or from a plain text file (ASCII), from any other statistical package or from the clipboard.

2) Two vectors X and Y are defined as follows – X <- c(3, 2, 4) and Y <- c(1, 2). What will be output of vector Z that is defined as Z <- X*Y.

In R language when the vectors have different lengths, the multiplication begins with the smaller vector and continues till all the elements in the larger vector have been multiplied.

The output of the above code will be –

Z <- (3, 4, 4)

3) How missing values and impossible values are represented in R language?

NaN (Not a Number) is used to represent impossible values whereas NA (Not Available) is used to represent missing values. The best way to answer this question would be to mention that deleting missing values is not a good idea because the probable cause for missing value could be some problem with data collection or programming or the query. It is good to find the root cause of the missing values and then take necessary steps handle them.