Data Science and Data Analytics Using R , Python , Statistics and BigData Training and Certification Course In Pune

Data Science With R & Python
Duration of course: 40 hrs

Best Blended Syllabus for Data Science And Data Analytics with R & Python Training in Pune by a 100% Placement-Oriented Training Institute

Data Science with R Programming Training provides in-depth training in the most in-demand data science and machine learning skills, as well as hands-on experience with key tools and technologies like R, data analytics, and visualisation, predictive models, regression techniques, etc.

Instructor-led Data Science Live Online Training

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26th Jun
26th Jun

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  • What is Data Science
  • Differentiate between Database Datawarehouse Hadoop Bigdata and Data Science
  • Why Data Science is in demand on the top of Hadoop Ecosystem
  • Components in data Science
  • Real time examples and applications of Data Science
  • What is Statistics
  • Introduction to R Language
  • Introduction to R Language and Statistics
  • Statistics in Excel Sheet
  • Introduction to Python Language
  • Questions and Answers

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  • Harnessing the power of R
  • Assigning Variables
  • Printing an output
  • Numbers are of type numeric
  • Characters and Dates
  • Logicals

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  • Creating an Array
  • Indexing an Array
  • Operations between 2 Arrays
  • Operations between an Array and a Vector
  • Outer Products
  • Data Structures are the building blocks of R
  • Creating a Vector, The Mode of a Vector
  • Vectors are Atomic
  • Doing something with each element of a Vector
  • Aggregating Vectors
  • Operations between vectors of the same length
  • Operations between vectors of different length
  • Generating Sequences
  • Using conditions with Vectors
  • Find the lengths of multiple strings using Vectors
  • Generate a complex sequence (using recycling)
  • Vector Indexing (using numbers)
  • Vector Indexing (using conditions)
  • Vector Indexing (using names)
  • A Matrix is a 2-Dimensional Array
  • Creating a Matrix
  • Matrix Multiplication
  • Merging Matrices
  • Solving a set of linear equations

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  • What is a factor?
  • Find the distinct values in a dataset (using factors)
  • Replace the levels of a factor
  • Aggregate factors with table()
  • Aggregate factors with tapply()
  • Introducing Lists
  • Introducing Data Frames
  • Reading Data from files
  • Indexing a Data Frame
  • Aggregating and Sorting a Data Frame
  • Merging Data Frames
  • Introducing Regression
  • What is Linear Regression?
  • A Regression Case Study : The Capital Asset Pricing Model (CAPM)

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  • Linear Regression in Excel : Preparing the data
  • Linear Regression in Excel : Using LINEST()
  • Linear Regression in R : Preparing the data
  • Linear Regression in R : lm() and summary()
  • Multiple Linear Regression
  • Adding Categorical Variables to a Linear model
  • Robust Regression in R : rlm()
  • Parsing Regression Diagnostic Plots
  • Data Visualization
  • The plot() function in R
  • Control color palettes with RColorbrewer
  • Drawing barplots
  • Drawing a Heatmap
  • Drawing a Scatterplot Matrix
  • Plot a line chart with ggplot2

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  • Introduction to Python Language
  • Getting What You Need in Python Library
  • Installation
  • Python language Basics
  • Running Python Scripts
  • Types of Data
  • Mean, Median, Mode
  • Using mean, median, and mode in Python
  • Variation and Standard Deviation
  • Probability Density Function; Probability Mass Function
  • Common Data Distributions
  • Percentiles and Moments
  • matplotlib plotting library
  • Covariance and Correlation
  • Conditional Probability
  • Conditional Probability usecases
  • Bayes’ Theorem

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  • Linear Regression
  • Polynomial Regression
  • Multivariate Regression, and Predicting Analysis
  • Multi-Level Models
  • Supervised vs. Unsupervised Learning, and Train/Test
  • Using Train/Test to Prevent Overfitting a Polynomial Regression
  • Bayesian Methods: Concepts
  • Implementing a Spam Classifier with Naive Bayes
  • K-Means Clustering
  • Clustering Example
  • Measuring Entropy
  • Install GraphViz
  • Decision Trees: Concepts
  • Decision Trees: Predicting Hiring Decisions
  • Ensemble Learning
  • Support Vector Machines (SVM) Overview
  • Using SVM to cluster people using scikit-learn

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  • How to work in Real time Project
  • Real time Project Scenarios
  • Frequent Challanges in Projects and solutions
  • Mock Interview session
  • Profile discussion
  • Mock Test
  • Questions and Answers

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  • Trainer is Working It Professionals
  • POCs and Material will be provided by Institute
  • Once Registered can come and join multiple batches
  • We also provide Combination of Hadoop and Data Science

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Data Science And Data Analytics with R & Python Training Course​

Best Blended Syllabus for Data Science And Data Analytics Using R & Python Training in Pune by a 100% Placement-Oriented Training Institute

Data Science And Data Analytics with R & Python Training Completion Certificate​

To Obtain The Data Science And Data Analytics with R & Python Course Completion Certification ,You Have To Fulfill The Following Criteria

  • Complete the Data Science And Data Analytics Course Using R & Python  Online/Classroom Training
  • You can attend multiple batches of the same trainer & complete the Data Science And Data Analytics Course Using R & Python Training
  • Completion of all exercises, assignments & capstone project.

Batches Completed

Industry Oriented Syllabus

Designed By Expert


Happy Students

Self Assessments

Quizzes, POC


8+ Years Of Experience

Recorded Sessions

1 Year Of Access

Data Science And Data Analytics with R & Python Training Completion Certificate


Course Benefits​

  • Pay only after attending one FREE TRIAL OF RECORDED LESSON
  • No prerequisite
  • Course designed for non-IT as well as IT professionals
  • Flexible batch switch is available
  • Classroom & Online Training – Can switch from online training to classroom training with nominal fee
  • 100% placement calls guaranteed till you get placed
  • Working professional as instructor
  • Hands-on Experience with Real-Time Projects.
  • Proof of concept (POC) to demonstrate or self-evaluate the concept or theory taught by the instructor. 2 – Python POC, 5 – Data Science POC
  • Hands-on Experience with Real-Time Projects
  • Resume Building & Mock Interviews
  • Evaluation after each Topic completion
  • Interview Preparation Support


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About Course

You will learn all the latest techniques in data science and data analytics using R. This Data Science and Data Analytics Using R course is basically designed to provide the hands-on knowledge required to handle any data science project based on R programming. This training teaches the whole process of data analysis, including how to process data, run statistics, and make predictions based on different mathematical models.You will also learn about predictive models and machine learning with Python.

The course consists of both basic and advanced concepts in Python and R programming. This course will help you master concepts like statistics, linear regression, data visualisation, factors, lists, data frames, and regression. 

You will also learn to quantify relationships between variables in the R language.

Module 01 - Data Science and Data Analytics Introduction
Module 02 - Introduction to R Language
Module 03 - Arrays, Vectors and Matrices in R Language
Module 04 - Factors, Lists, Data Frames,Regression Quantifies Relationships Between Variables in R Language
Module 05 - Linear Regression and Data Visualization using R and Excel
Module 06 - Getting Started With Python and Statistics, Probability Refresher in Python
Module 07 - Predictive Models and Machine Learning with Python

  • Batches Completed – 
  • Students Trained -
  • Real Time Projects - 
  • Assignment Duration - hrs
  • Capstone Projects - Real-world projects from industry experts.
  • Course Completion Certificate with unique verification ID.

Training Projects​


Yes, you can attend demo session before you enroll either we can provide you the recorded lecture so that you can watch it as per your schedule or you can attend live demo lecture either online or offline


Yes, we do provide the placement assistance in which how we work on real time projects will be taught,resume preparation and Job openings will also be provided.More than 80% of candidates have changed their profile by getting either promotion or getting new job offers on good package.

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If you miss classes, you can get recording sessions of the lectures.

R is language and environment for statistical computing and visualization.The R programming language is frequently used for statistical methods research, and R offers an Open Source option for participating in that activity.

R is  open source programming language available under the provisions of the Free Software Foundation's GNU General Public License. It compiles and operates on a variety of UNIX and related platforms (including FreeBSD and Linux), as well as Windows and MacOS.

R was developed by academics and scientists. It is designed to answer statistical problems, machine learning, and data science. R is a good programming language for data science because of its powerful communication libraries.

R includes objects, operators, and functions that allow users to explore, model, and display data. R is used for data analysis and to handle data science, statistics, and visualisation projects. R in data science is used to handle, store, and analyse data. R is a good environment for statistical analysis. R lets you build and run statistical models with Sisense data, and these models are automatically updated as new information is added to them.

R and Python are both common choices when it comes to data science. They are both open ource programming languages, so they are free to use. R can be used for a variety of applications, but it is most commonly used for statistical analysis and data visualization. However, Python is better suited for web development, machine learning, and artificial intelligence.

So R is not compulsory and neither is Choice of programming language depends on what kind of data you are working upon and your expertise in solving the problems in the domain.

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