Data Science using R
PRICE: 39,500
Sessions : 24       Hours: 192
Inclusive all of taxes
Machine Learning using R training program is designed by the highly professional industry experts. It includes certain analytical skill set which exactly fulfil the requirement of the industry. There are number of job opportunities with this skill set.

Upcoming Batches



Timings - 08:00 AM to 11:00 AM (IST)



Timings - 11:30 AM to 02:30 PM (IST)



Timings - 03:00 PM to 06:00 PM (IST)

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Data Science using R Curriculum

By embracing data science tools and technologies, companies can more effectively inform strategic decision-making, reducing uncertainty and eliminating analysis-paralysis.

  • What is Data Analytics & Data Science
  • Different types of Data Analytics (Descriptive, Predictive, Prescriptive)
  • What is Artificial Intelligence
  • What is IOT
  • Machine Learning (Supervised & Unsupervised Learning)
  • Deep Learning (Artificial Neural Networks, CNN)
  • Overview of Banking, Healthcare, Telecom domain
  • Working with multiple data sources – RDBMS (SQL Server, Oracle, My SQL, DB2),  NOSQL (MongoDB, Cassandra, CouchDB)
  • Real world Applications of Machine Learning & Deep Learning
  • What to expect from this course (Salary, Market trends, job roles, Domain)

What is RDBMS?
What is SQL?

ü  What is SQL

ü  SQL Process

ü  SQL Architecture

Why SQL?

SQL Commands

ü  Data Definition Language

ü  Data Manipulation Language

ü  Data Control Language

ü  Data Query Language


Fundamental of SQL

ü  Creating Database

ü  Creating Tables

ü  Insertion of rows

ü  Deletion of rows

ü  Field

ü  Record

ü  Tuple

ü  Column

ü  NULL Value

ü  Constraints

ü  Not NULL

ü  Clauses

ü  Removing Duplicate data

ü  Sorting data

ü  Alteration of Data

ü  Default Constraints

ü  Unique

ü  Primary Keys

ü  Foreign Keys

ü  Composite Keys


SQL Syntax

ü  Select Statement

ü  Distinct Clause

ü  Where Clause

ü  AND/OR Clause

SQL Operators

ü  Arithmetic Operators

ü  Comparison Operators

ü  Logical Operators


SQL Sorting


SQL Joins

ü  Relationships in SQL

ü  Understanding Joins

ü  Types of Joins

·         Inner Join

·         Outer Join

·         Right Join

·         Left Join

·         Full Join

·         Self Join

·         Cartesian Join

ü  Concept of Keys



ü  Brief about Function


SQL Union Clause

ü  The Union All Clause


SQL Alias Syntax

SQL Truncate Table

SQL Using Views

ü  Performing dataset with MYSQL

ü  Introduction to  R Programming

ü  Data Types in R

ü  Functions in R

ü  Summarizing data by using various functions

ü  Indulge into a class activity to summarize the data

ü  Various subsetting methods

    Data Importing

ü  Data import technique in R

ü  Import data from spreadsheets and text files into R

ü  Install packages used for data import

ü  Connect to RDBMS from R using ODBC and basic SQL queries in R

ü  Perform basic web scrapping

Data Manipulation in R

ü  Know the various steps involved in data cleaning

ü  Functions used for data inspection

ü  Tacking the problem faced during data cleaning

ü  How and when to use functions like grep, grepl, sub, gsub,
regexpr, gregexpr, strsplit

ü  How to coerce the data

ü  What is data exploration

ü  Data exploring using Summary(), mean(), var(), sd(), unique()

ü  Using Hmisc package and using summarize, aggregatefunction

ü  Learning correlation and cor() function and visualizing the same using corrgram

ü  Visualizing data using plot and its different flavours

ü  Boxplots

ü  Dist function

ü  Gain understanding on data visualization

ü  Learn the various graphical functions present in R

ü  Plot various graph like tableplot, histogram, boxplot etc.

ü  Customize graphical parameters to improvise the plots.

ü  Understand GUIs like Deducer and R commander

ü  Introduction to spatial analysis.

Fundamentals of Statistics

ü  Basic statistics; descriptive and summary

ü  Inferential statistics

ü  Statistical tests

Data Prep and Reduction techniques

ü  Need for data preparation

ü  Outlier treatment

ü  Flat-liners treatment

ü  Missing values treatment

ü  Factor Analysis

Basic Analytics

ü  Statistics Basics Introduction to Data Analytics and Statistical Techniques

ü  Types of Variables, measures of central tendency and dispersion

ü  Variable Distributions and Probability Distributions

ü  Normal Distribution and Properties

ü  Central Limit Theorem and Application

ü  Hypothesis Testing Null/Alternative Hypothesis formulation

ü  One Sample, two sample (Paired and Independent) T/Z Test

ü  P Value Interpretation

ü  Analysis of Variance (ANOVA)

ü  Chi Square Test

ü  Non Parametric Tests (Kruskal-Wallis, Mann-Whitney, KS)

ü  Correlation

Introduction to Business Analytics

ü  Relevance in industry and need of the hour

ü  Types of analytics – Marketing, Risk, Operations etc.

ü  Future of analytics and critical requirement

ü  Basics of regression analysis

ü  Linear regression

·         Simple Linear Regression

·         Multiple Regression

ü  Logistic regression

ü  Interpretation of results

ü  Multivariate Regression modeling

ü  K-Nearest Neighbours (K-NN) 

ü  Support Vector Machines

ü  Understand what is Decision Tree

ü  Algorithms for Decision Tree

ü  Decision Tree Classification

ü  Greedy approach: Entropy and information gain.

ü  A perfect decision tree

ü  Understand the concept of random forest

ü  Random Forest Classification

ü  How random forest work

ü  Features of random forest

ü  Time series modeling

ü  Cross-sell and Up-sell opportunities and modeling

ü  Churn prediction models and management

ü  Basics clustering

ü  Deciles analysis

ü  K-Means Clustering

ü  Hierarchical Clustering

ü  Cluster evaluation and profiling

ü  Interpretation of results

ü  Introduction to Data Visualization with Tableau

ü  Data Import and Management

ü  Data Type and Operation

ü  Visualizations Deep Dive

ü  Data Organization and Scripting

ü  Playing with Time Dimension

ü  What is Your Location?

ü  Incremental Loading and Blending

ü  The World is Your Visualization

ü  Statistical Analysis with Tableau & R

ü   Sharing Insights with Enterprise Dashboards

ü  Analyse Project data and extract meaningful information from it

ü  Create Dashboards and Stories from data sets

Project:  A dashboard should be created that would contain

ü  A list of Orders returned by the Customers (in terms of refund).

ü  Top 10 Countries mapped on the World Map that had most of the refunds.

ü  Predict the refund for next 1 year category-wise.

ü  Actions to be Performed to Create the Dashboard

ü  Create hierarchies and folders in the dataset provided.

ü  Generate a list of order returned from customers and compare it to the original sales, sort the visualization in ascending. 

ü  Order in terms of returned order, for top 10 countries in terms of refunds.

ü  Map top 10 countries, with most of the refunds on the world map.

ü  Predict the returned sale with lowest and actual forecast of the data

ü  Add a URL action to represent details of the countries

ü  Finally publish your work to Tableau Server/ Online

(Live Project to get hands on tools with one of these specified domains)

  • Specialization in Healthcare
  • Specialization in Telecom
  • Specialization in Insurance 

Data Science using R Reviews

I can proudly say that I took a good decision by joining data science course by palin analytics as they helped me getting placed and provide industry specific training as well.

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I have completed R training in palin. I would recommend you palin because of their trainers, placement assistance and training quality of course.thank you Palin!!!!

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You guys have the best trainers who provides the industry exposure to the freshers that helps candidate to get placed.thank you palin for giving me a right path.

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Vikas Nirban

Trainer is highly knowledgable and energetic and keeps participants engaged. Really good for beginners

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Astik Pandey

Great learning environment. Trainers are industry professionals and have in depth knowledge.

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Rajat Narula

The best institute for Data Science training. I had chosen the predictive analytics course and the training was excellent.

Read More

Saquib Ahsan

Data Science using R Project



Min 8+ years of industry Experience.For end to end implementation in analytics life cycle


Training as per industry requirement with mock interviews training


Designed by professionals according to industry requirement


Choose the timimg as per your convenience


For end to end implementation in analytics life cycle


Life time access for recorded sessions

Students Questions and answers

Any graduate who’s good at maths or stats and willing to work on Advance Modeling with R as Data Analyst, Business Analyst etc can go for Data Science using R training.

Advance modeling techniques in R like Linear & Logistic regression, classification algorithms, clustering are used to analyse large data and helps bringing inference and decision making for different business needs.

When it comes to data science, R is a great language to master. First of all, it’s a language friendly to beginners — there are certain features in the language that make it easy to get started and develop prototypes quickly.

Candidates who are good at maths or stats is an advantage for them. Any Graduate/diploma candidate can apply for this training and get mentored by the experts of the industry.

Palin Analytics offers you both the training modes. You can opt either classroom or online.

Both the training modes are good to get trained. But online training is more better than the classroom training because online trainings are live interactive where you can raise your concerns at any point during the session. Additionally recording of the same session will be provided which you can access anytime anywhere.

R Analytics includes Basic Fundamental of R, Data Manipulation in R, Data import techniques in R, Data Exploration, Data Visualisation in R, Data Mining, Linear & Logistic Regression, ANOVA, Z-Test, Hypothesis Testing, Cluster Analysis, Customer Segmentation, Time Series Analysis, Decision Tree and Random Forest. Real Cases to get hands on working experience on R with Advance Modeling, Reporting and dashboards in Tableau and SQL

Almost every organisation which has a large customer base is shifting to R Analytics. Companies like Amazon, Infosys, TCS, Accenture, IBM, Flipkart, Airtel are highly in need for analytics professionals.

An Entry-Level Data Scientist, IT earns an average salary of Rs 579,714 per year. The highest paying skills associated with this job are Machine Learning, R, and SQL.

We'll be starting from Fundamental of R to understand working with files and databases. Then we'll cover advance statistical techniques to get hands on business analytics with regression modeling, classification & unsupervised learning and then we'll cover reporting and dashboarding using tableau and SQL.

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