Data Science With R
Course Rating

MTA Certifications:
Python: MTA 98-381
Database SQL: MTA 98-364

Course Description

For data analysis, the R programming language is commonly used by data miners and statisticians. Many statisticians who wish to create statistical models to solve complex problems favour it. A solid R programming tutorial, such as this Data Science with R programme, will walk you through the fundamentals of R programming. You’ll learn linear regression, logistic regression, decision trees, and other Data Science algorithms. Completing a R language course will help you build your resume, as many employers are looking for Data Science specialists that know R.

Why should one choose this course?

In India, a Data Scientist earn approximately Rs. 6.5 L. PA on an average. It can go up to Rs. 20 L. PA for experts who gain a lot of experience and skill.

In current data world, Data Scientists are in high demand. Data Science’s findings are already making an impact in our daily lives. Take the Internet for example; there are more websites than one can fathom – a single Google search yields 1 billion results, which is a staggering statistic. When you use Google to search for something, a data scientist is sifting through the 1 billion websites in the background to get you the information you need.

Companies have a lot of information. That won’t help if corporations don’t engage data scientists to examine the data and provide actionable insights.
After learning all the concepts provided in this programme you will be able to think like Data Scientist, solve complex real-world problems and provide best solutions to company.

What is the scope of this course?


Businesses and enterprises collect data on a daily basis for transactions and online interactions. Many businesses have the same problem: analysing and categorising the data they collect and store. In a case like this, a data scientist becomes the saviour. Companies can make significant progress if data is handled properly and efficiently, resulting in increased production.
Data science is a vast professional path that is always evolving, promising a plethora of options in the future. Job responsibilities in data science are projected to become more specialised, leading to specialties in the subject. People who are interested in this field can take advantage of their opportunities and pursue what best suits them by using these standards and specialities.
Course Structure

Introduction of statistics
Types of Statistics
Types of variables
Levels of Measurements
Construction of frequency polygon
Constructing frequency table
Plotting histogram
Concept of population and sample
Measures of central tendency
Measures of dispersion
Measures of position
Probability concepts
Discrete probability distribution
Continuous probability distribution
Central limit theorem
Sampling methods
Estimation and confidence interval
Hypothesis testing
Types of Error
Analysis of Variance

Introduction to R
Installation R and R Studio Basic R Operators in R Data types in R Data Structures in R Data Frame
Read and write file
Useful Functions in R
Control Structure
Graphs in R

Classification techniques
Time Series Forecasting

Terminologies – Records, Fields, Tables, Introduction to database
Concept of ER Modelling
Relational Algebra: The fundamental operations of relational algebra are as follows −

Set different
Cartesian product
Introduction to SQL, SQL Syntax, SQL data Types, SQL Operators, Table creation in SQL- Create, Insert, Drop, Delete, and Update, Table access & Manipulation, Select with Where Clause (In between, logical, operators, wild cards, order, group by), Concepts of Join – Inner, Outer

Ridge and Lasso regression
Principle Component Analysis (PCA)
Gradient Boosting


Introduction, Natural language processing, Natural language processing, Text clustering, Topic modelling, Document summarization, Sentiment analysis, Text visualization

Introduction to deep learning, Neural Networks Basics, Shallow neural networks, Deep Neural Networks

1.Understanding the project topic
2.Research about the project
3.Constructing the project using R programming
4.Step by step building of project
5.Mentoring sessions for the project
6.Final product

Course Duration

40Hrs Technical + 15Hrs Soft skills

Entry Level 
Approx. average Rs. 3.58 LPA

Approx. average Rs. 8 LPA

Expert Level 
Approx. average Rs. 1 Cr. PA
Accreditation Partner(s)


Are you ready to take the next step toward your future career?

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Education Details