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Data Science with R


Data Science Machine Learning with R

R is a language gaining incredible importance in the IT industry. It is used for statistical computations, data analysis and graphical representation of data. Created in the 1990s by Ross Ihaka and Robert Gentleman, R was designed as a statistical platform for data cleaning, analysis and representation. Google trends show the rapidly rising popularity of R Programming.
Advantages of R in data science and why it proves to be an ideal choice are as below:

Why use R for Data Science?

1.Spreading quickly

A language’s popularity is judged by the number of active users working on it. R is a very popular language amongst educationists. Many researchers and scholars use R for experimenting with data science. Many popular books and learning resources on data science use R for statistical analysis as well. The number of users of R is increasing significantly.

2. Data wrangling

Data wrangling is the process of cleaning large, messy and complex data sets to enable convenient usage and further analysis. This is a very important and time taking process in data science. R has an extensive library of tools for database manipulation and wrangling.

3. Data visualization

Data visualization is the visual representation of data in graphical form. This allows analyzing data from angles which are not clear in unorganized or tabulated data. R has many tools that can help in data visualization, analysis, and representation.

4. Focused Approach

R is a language designed with a focus on statistical analysis and data reconfiguration. All the R libraries ensure to make data analysis easier, more comprehendible and illustrative. Any new statistical methodology is first initiated in R libraries. This makes R best choice for data analysis and projection. This all gives R a special edge, making it a perfect choice for data science projects.

5. Machine learning

A programmer may need to train the algorithm and bring in automation and learning capabilities to make predictions possible. R provides range of tools to developers to train and evaluate an algorithm and predict future events. Thus, R makes machine learning (a branch of data science) lot more easy and approachable.

6. Open Source

R programming language is open source. This makes it highly cost effective for a project of any size. Since it is open source, developments in R happen at a rapid scale and the community of developers is increasing rapidly.


Following forms part of the course Data Science with R:

Introduction of R-Module 1

  • Overview of R
  • Features of R
  • Installing R
  • R interface

Introduction of R’s IDE [Rstudio]-Module 2

  • Overview of Rstudio
  • Installation of Rstudio
  • Rstudio User Interface

Basic of R-Module 3

  • Comments in R
  • Variable
  • Input & Output
  • Arithmetic Operations
  • Precedence and Associativity
  • Sequence
  • Repeat
  • Remove function
  • Quite R
  • ls function

Datatypes-Module 4

  • Vector
  • Number
  • String
  • List
  • Factor
  • Matrix
  • Array
  • Dataframe
  • Missing Value (NA )

File Handling-Module 5

  • File import
  • File export

Data Manipulation-Module 6

  • Apply function
  • Lapply function
  • Sapply function
  • Tapply function
  • Columns Delete, Rename, Add new column
  • Rows Delete, Add new row
  • Index & Slicing
  • Filtering & Sorting
  • String operation
  • String Concatenation
  • Missing Value Treatment
  • Aggregate function
  • Contingency Table
  • Merge
  • Ifelse function
  • Transpose

Date & Time-Module 7

  • Date difference
  • Day,Month,Year, Weekday,Quarter
  • Current date & time

Package Tutorial-Module 8

  • Tibble Package
  • Dplyr Package
  • Lubridate Package
  • RMySQL Package

Control Structure-Module 9

  • IF Else statement
  • For loop
  • While loop
  • Repeat Statement
  • Break Statement
  • next Statement
  • switch Statement
  • return

Functions-Module 10

  • User Defined Function
  • Functions with argument & without argument

Text Manipulation-Module 11

  • Regular Expression
  • Stringr Package

Data Visualization-Module 12

  • GGPLOT2 Package

Statistics-Module 13

  • Central Tendency (Mean, Median, Mode, Quartile)
  • Range, Variance & Standard Deviation
  • Skewness & Kurtosis
  • Sample vs Population
  • Hypothesis Testing
  • Correlation
  • Regression
  • Supervised Learning vs Unsupervised Learning

Machine Learning Algorithm-Module 14

  • Linear Regression
  • Logistic Regression
  • Decision Tree
  • Clustering
  • Neural Network
  • Text Sentiment Analytics with Twitter Data


  • Course Duration: : 1 Month (4 Weeks)
  • Approximate training period: 40 hours
  • Fees: INR 19,900
  • Sessions: Weekdays/ Weekends
  • Number of modules covered: 14 modules
  • Learning method: Offline/Online

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