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6 weeks Data Science training in Delhi | Summer training India

DATA SCIENCE Training for 6 weeks

Join With Our Courses To Develop Yourself.


Courses Overview

Data Science Training @ Yami Services GreenPark, Delhi

  1. Introduction To Data Science




Will this module introduce you to throw data science to throw data science? To solve big data issues, data visualization, etc., analyze Big Data, Architecture and Methods ...


Introduction to Big Data

Roles played by data scientists

Analysis of Big Data using Hadop and R

Various methods used for analysis in data science

Architectures and methods are used to solve big data issues

Data acquisition from different sources

data preparation

Data conversion using map reduction (RMR)

Use of Machine Learning Techniques, Data Visualization, etc.

Problem statement of some data science problems which we will solve during the course


Basic data manipulation using R in data science.


This module instructs us how to utilize information and utilize R for information transformation and rebuilding forms, regularly experienced in the underlying phases of information examination in Data Science Training.


Understanding vectors in R


Perusing information


Information expansion


Buying in Data



Machine Learning Techniques Using R Part 1


The aim of machine learning is to create a prediction model, which is not different from the right model.

This module begins with giving you an outline about taking in the machine in information science preparing.


Machine learning outline


Ml normal utilize cases and strategies


Grouping and Similarity Metrics


Separation estimation write: Euclidean, cosine cure, influencing forecast to show


Machine Learning Techniques Using R Part 2


This module is intended to show you 'K' bunching, affiliation manage mining and that's only the tip of the iceberg.


Understanding KMens Clustering in Data Science


Understanding TFIDF and cosine correspondence and their application vector space show


Execution Association Rule Mining in R


Information Science Machine Learning Techniques Using R Part 3


The last piece of the machine learning module of the information science course, the prepare about the choice tree, the arbitrary timberland idea in information science.


Understanding TFIDF and cosine equality and their application vector space model

Implementation Association Rule Mining in R


Data Science Machine Learning Techniques Using R Part 3


The last part of the machine learning module of the data science course, the train about the decision tree, the random forest concept in data science.


Understanding the process flow of supervised learning techniques

Decree tree classification

How to make decision trees

Random forest classification

What is Random Forest Concept in Data Science?

Features of Random Forest

Out of box error estimates and variable value

Stupid beta classified


Introduction to Hadop Architecture


Understand this in this module, headop architecture, its commands, SQOPP and other data loading techniques.


Hadop architecture

General headop order

MapReduce and data loading techniques (straightening R and loading techniques using SQOOP, FLUME, and other data in Haddop)

Removing discrepancies with data


Integrating with Rhythm


This module of the information science course is incorporated with R, will give great learning about the coordinated programming condition and how to compose MapReduce occupations.


Incorporating R with HADOP utilizing R


Hadop and RMR bundle


Finding RHIPE (R-Hadop Integrated Programming Environment)


Data Science Introduction and Algorithm Implementation


By the end of this module, you will be able to apply Machine Learning Algorithm with Mahout

Applying machine learning algorithms to large data sets with Apache Mahout


Using additional Mahout algorithms and parallel processing r



In this module, you will learn how to implement Random Forest Classifier with Parallel Processing Library using R.

Implementation of different Mahout algorithms

Random Forest Classifier with parallel processing Library in R

  • Duration: 30 Days
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