9007116752
c3creativedomain@gmail.com
CCube
Centre of Computer Education
Data Science with R Training
data science career.
2) R for data science can be used for statistical analysis
and other functions.
3) R allows users to explore ,model,visualize data.
4) R has various statistical and graphical capabilities.
What is R Programming Language ?
R is an interpreted computer programming language which was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand.” The R Development Core Team currently develops R. It is also a software environment used to analyze statistical information, graphical representation, reporting, and data modeling. R is the implementation of the S programming language, which is combined with lexical scoping semantics.
Why Learn R Programming Language ?
There are several tools available in the market to perform data analysis. Learning new languages is time taken. The data scientist can use two excellent tools, i.e., R and Python. We may not have time to learn them both at the time when we get started to learn data science. Learning statistical modeling and algorithm is more important than to learn a programming language. A programming language is used to compute and communicate our discovery.
The important task in data science is the way we deal with the data: clean, feature engineering, feature selection, and import. It should be our primary focus. Data scientist job is to understand the data, manipulate it, and expose the best approach. For machine learning, the best algorithms can be implemented with R. Keras and TensorFlow allow us to create highend machine learning techniques. R has a package to perform Xgboost. Xgboost is one of the best algorithms for Kaggle competition.
R communicate with the other languages and possibly calls Python, Java, C++. The big data world is also accessible to R. We can connect R with different databases like Spark or Hadoop.
Course Curriculum
R Programming For Data Science For Beginners

Module 1
R introduction , Basic Syntax , Data Types , Data Structures , Variables , Keywords , Operators.

Module 2
R If Statement , Ifelse Statement , ElseIf Statement , Switch Statement , Next Statement , Break Statement , RLoops , R Functions.

Module 3
R Data Structures  Vectors , Lists , Arrays , Matrix , Data Frame , Factors
Quick Contact Form
Minimum Eligibility 10 + 2 Pass
Course Duration 3 Months
Mode of Training Online & Offline
R Programming For Data Science & ML For Advanced Level

Module 1
R introduction , Basic Syntax , Data Types , Data Structures , Variables , Keywords , Operators.

Module 2
R If Statement , Ifelse Statement , ElseIf Statement , Switch Statement , Next Statement , Break Statement , RLoops , R Functions.

Module 3
R Data Structures  Vectors , Lists , Arrays , Matrix , Data Frame , Factors , Strings

Module 4 (Advanced Level)
R packages , Data Reshaping , ObjectOriented programming

Module 5
Data Interfaces R CSV File , R Excel File , R Database , Data Manipulation

Module 6
Data Visualization  Pie Charts , Bar Charts , Boxplot , Histogram , Line Graphs , Scatterplots

Module 7
Linear Regression , Multiple Regression , Logistic Regression , Poisson Regression

Module 8
Normal Distribution , Binomial Distribution , Time Series Analysis , Random Forest , TTest in R , ChiSquare Test