SAS Training

Data Science

Enterprises and businesses need high-end tools and technology to drive business insights and perform critical data analysis, developing predictive models. Data Science helps businesses manage large sets of data, using different algorithms and mathematical analysis for extracting valuable insights to apply strategic decisions.

JPIE impart high-end Data Science training in Noida, providing complete know-how of how to analyze Big Data using R Programming and Hadoop. The Data Science certification training also explains what are the roles played by Data Science professionals.

After completing the Data Scientist training, the professionals will be able to understand:

  • Analyze Big Data using Machine Learning, Hadoop and R
  • Data Transformation using different techniques
  • R Programming algorithms
  • Data analysis using Hadoop Mappers and Reducers
  • Data Analysis Life Cycle
  • Data Visualization and optimization
  • Data Scientist roles
Target audience

This course is ideal for aspirants who are looking to enhance their data analysis understanding, along with Big Data Analytics, Analytics Managers, Data Scientists, Hadoop Professionals, Information Architects and more.


Aspirants opting for this course should have basic knowledge core Java and functional programming, and mathematical aptitude.

1. Introduction to Data Science

  • Introduction to Big Data
  • Roles played by a Data Scientist
  • Analyzing Big Data using Hadoop and R
  • Methodologies used for analysis
  • The Architecture and Methodologies used to solve the Big Data problems

2. Basic Data Manipulation using R

  • Understanding vectors in R
  • Reading Data, Combining Data
  • Subsetting data
  • Sorting data and some basic data generation functions

3. Machine Learning Techniques Using R Part-1

  • Machine Learning Overview,
  • ML Common Use Cases
  • Understanding Supervised and Unsupervised Learning
  • Techniques, Clustering
  • Similarity Metrics
  • Distance Measure Types: Euclidean, Cosine Measures, Creating predictive models

4. Machine Learning Techniques Using R Part-2

  • Understanding K-Means Clustering
  • Understanding TF-IDF and Cosine Similarity and their application to Vector Space Model
  • Implementing Association rule mining in R

5. Machine Learning Techniques Using R Part-3

  • Understanding Process flow of Supervised Learning Techniques
  • Decision Tree Classifier
  • How to build Decision trees
  • Random Forest Classifier
  • What is Random Forests
  • Features of Random Forest
  • Out of Box Error Estimate and Variable Importance
  • Naive Bayes Classifier

6. Introduction to Hadoop Architecture

  • Hadoop Architecture
  • Common Hadoop commands
  • MapReduce and Data loading techniques (Directly in R and in Hadoop using SQOOP, FLUME, and other Data Loading Techniques)
  • Removing anomalies from the data

7. Integrating R with Hadoop

  • Integrating R with Hadoop using RHadoop and RMR package
  • Exploring RHIPE (R Hadoop Integrated Programming Environment)
  • Writing MapReduce Jobs in R and executing them on Hadoop

8. Mahout Introduction and Algorithm Implementation

  • Implementing Machine Learning Algorithms on larger Data Sets with Apache Mahout

9. Additional Mahout Algorithms and Parallel Processing using R

  • Implementation of different Mahout algorithms
  • Random Forest Classifier with parallel processing Library in R