Data Science with R Training & Certification Courses
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This training will help in understanding programming with R and its implementation for Big Data Analytics and Data Science. This training will give hands-on experience along with practical use cases approach, which will make participants experience in understanding how R is helpful to solve practical data science problems. With the training they will be able to learn how to load data, assemble and disassemble data objects, navigate R’s environment system, write your own functions, and use all of R’s programming tools.


  • Social media sites like Facebook, twitter and LinkedIn for analyzing likes, tweets and profiles searched by people.
  • E-commerce sites like Flipkart, Amazon, and Alibaba for analyzing sales of products and search for products.
  • Sentimental analysis like customer satisfaction and feedbacks, moods.
  • Helps in business decisions based on visualization of data and trends.
  • Analyzing large objects like satellite pictures, images and graphs.
  • Live streaming analysis for providing real time results.
  • Adoption of storage where data grows very fast.
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Schedule & Prices

Delivery Mode Location Course Duration Fees  Schedule
Instructor-Led Online Training (1-on-1) Client's Home/Office5 Days $ 1,700 As per mutual convenience (4-Hours Evenings & Weekends Possible
Classroom Training * Dubai 5 Days $ 2,600 On Request
Fly-Me-a-Trainer Client's Location5 Days On Request As per mutual convenience
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Course Content / Exam(s)

Schedule for Data Science with R

Course Name Duration (days)
Data Science with R5

Course Prerequisites

Basic understanding of R language.

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Data Science with R Benefits

This course is best suited for fresher and experienced IT professionals, software programmers, statisticians and data miners who are looking forward for developing statistical software using R programming.


Day 1

1: Introduction to Basics
Take your first steps with R. Discover the basic data types in R and assign your first variable.
2: Vectors
Analyse gambling behaviour using vectors. Create, name and select elements from vectors.
3: Matrices
Learn how to work with matrices in R. Do basic computations with them.
4: Factors
R stores categorical data in factors. Learn how to create, subset and compare categorical data.
5: Data Frames
When working R, you’ll probably deal with Data Frames all the time. Therefore, you need to know how to create one, select the most interesting parts of it, and order them.
6: Lists
Lists allow you to store components of different types.

Day 2

1: Functions

Learn to create functions in R.

2: Reading in Data

Reading the data from a specific folder, file or a source.

3: Writing Data

Writing data to a text file.

4: Graphics in R

Discover R’s packages to do graphics and create your own data visualizations.

5: Type Of Data

Qualitative Data vs. Quantitative Data, examples and uses.

6: Numerical Measures

The mean, median, mode, percentiles, range, variance, and standard deviation are the most commonly used numerical measures for quantitative data.

Day 3

1: Qualitative Data

2: Quantitative Data

3. Probability Distributions

Learn to create functions in R.

4: Interval estimation

The use of sample data to calculate an interval of possible (or probable) values of an unknown population parameter, in contrast to point estimation, which is a single number

5: Linear Regression

Simple Linear Regression and Multiple Linear Regression

6: Logistic Regression

To represent binary / categorical outcome, we use dummy variables.

Day 4

1: Predictive Modelling using R

Read the data from various sources and perform data cleansing operations, such as identification of noisy data and removal of outliers to make the prediction more accurate.

2: Naïve Bayes Algorithm

Probability of finding an item using the naïve bayes classifier algorithm.

3: Decision Trees

It helps us explore the stucture of a set of data, while developing easy to visualize decision rules for predicting a categorical (classification tree)

Day 5

1: Twitter data Analysis In R

2: Artificial Neural Networks

Neural networks are built from units called perceptrons (ptrons)

3: Data Mining

To find patterns and learn classification rules hidden in data sets.

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