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"Best Data Science training in Sgraph Infotech with 100% placements" is no longer available

9 Reviews

Marathahalli, Bangalore

Course ID: 30101

Marathahalli, Bangalore

9 Reviews

About the Course

DATA SCIENCE COURSE CONTENT WITH "R" LANGUAGE Module 1 Introduction to Data Science Learning Objectives - This module will give you an understanding of Big Data and the Roles and Responsibilities of a Data Scientist. You will learn how Hadoop and R are used in Big Data Analytics and what are the methodologies used in the Analysis. This module will cover common Big Data as well as non-Big Data problems and available methods in Data Science to solve these problems. We will also solve few real-life data sets a Data Scientist encounter in his day to day work using R Hadoop and Mahout. Topics 1.Introduction to Big Data 2.Roles played by a Data Scientist 3.Analysing Big Data using Hadoop and R 4.Methodologies used for analysis 5.The Architecture and Methodologies used to solve the Big Data problems, For example Data Acquisition from various sources, Data preparation, Data transformation using Map Reduce (RMR) 6.Application of Machine Learning Techniques 7.Data Visualization etc. 8.Problem statement of few data science problems, which we shall solve during the course. Module 2 Basic Data Manipulation using R Learning Objectives - In this module, you will learn the various data manipulation techniques using Fl. Topics 1.Understanding vectors in R 2.Reading Data 3.Combining Data 4.Sub setting data 5.Sorting data and some basic data generation functions. Module 3 Machine Learning Techniques Using R Part-1 Learning Objectives - In this module, you will get an overview of the Machine learning Algorithms, and Supervised and Unsupervised Learning Techniques. Topics 1.Machine Learning Overview 2.ML Common Use Cases 3.Understanding Supervised and Unsupervised Learning Techniques 4.Clustering, Similarity Metrics, Distance Measure 5.Types: Euclidean, Cosine Measures, Creating predictive models. Module 4 Machine Learning Techniques Using R Part-2 Learning Objectives - In this module, you will learn Unsupervised Machine Learning Techniques and the implementation of different algorithms, for example, K-Means Clustering, TF-IDF and Cosine Similarity. Topics 1.Understanding K-Means Clustering 2.Understanding TF-IDF and Cosine Similarity and their application to Vector Space Model 3.Implementing Association rule mining in R Module 5 Machine Learning Techniques Using R Part-3 Learning Objectives -In this module, you will learn the Supervised Learning Techniques and the implementation of various Techniques, for example, Decision Trees, Random Forest Classifier etc. Topics 1.Understanding Process flow of Supervised Learning Techniques Decision Tree Classifier 2.How to build Decision trees 3.Random Forest Classifier 4.What is Random Forests 5.Features of Random Forest 6.Out of Box Error Estimate and Variable Importance 7.Naive Bayes Classifier. Module 6 Introduction to Hadoop Architecture Learning Objectives -In this module, you will learn the HDFS Architecture, MapReduce Paradigm and few data acquisition techniques in Hadoop. Topics 1.Hadoop Architecture, Common Hadoop commands 2.MapReduce and Data loading techniques (Directly in R and in Hadoop using SQOOP, FLUME, and other Data Loading Techniques) 3.Removing anomalies from the dat Module 7 Integrating R with Hadoop Learning Objectives - In this module, you will learn the methods to integrate two popular open source software for Big Data analytics: R and Hadoop. You will also learn techniques to write your own Mappers and Reducers. Topics 1.Integrating R with Hadoop using R Hadoop and RMR package 2.Exploring RHIPE (R Hadoop Integrated Programming Environment) 3.Writing MapReduce Jobs in R and executing them on Hadoop. Module 8 Mahout Introduction And Algorithm Implementation Learning Objectives - In this module, you will understand Apache Mahout Machine Learning Library and will also gain an insight into the methods to achieve Parallel Processing using Algorithms in Mahout. Topics 1.Implementing Machine Learning Algorithms on larger Data Sets with Apache Mahout Module 9 Additional Mahout Algorithms and Parallel Processing using R Learning Objectives - In this module, you will learn how to implement Random Forest Classifier with Parallel Processing Library in R Topics 1.Implementation of different Mahout algorithms 2.Random Forest Classifier with parallel processing Library in R Module 10 Project discussion Learning Objectives - In this module, you will learn various approaches to solve a Data Science problem and how different technologies and Tools (R Hadoop, and Mahout) work together in a typical Data Science Project. Topics 1.Project Discussion 2.Problem Statement and Analysis 3.Various approaches to solve a Data Science Problem 4.Pros and Cons of different approaches and algorithms. S graph Infotech.

Date and Time

Not decided yet.

About the Trainer

4.96 Avg Rating

84 Reviews

92 Students

4 Courses

Trainer is working in one of the MNC. Having 10+yrs of experience in various domains. He will provide you real time scenarios along with project.

Student Feedback


Average Rating





I have completed my data science training from sgraph. Believe me he is extremely talented and well experienced. The way he delivers each topic and interacting with students was awesome. I know data science is very Big course, but I learnt it very easily the credit goes to only snoop sir. Sir thank you so much for your support and excellent training.



Good training institute for data science. Staff is nice and very helpful. Got placed through the placements provided by the institute.



It is a Best institute for learning Data science and NLP ,They also provide internship and real time scenarios , It is a good place to learn for beginners. Thanks for sgraph whole team for my success



One of the best institute to learn, we have got into data science training, Trainer was one of the finest trainer, The way of his training was very nice and we were able to grasp things very soon. Sgraph infotech is one of the best institute to learn all Training.



Great data science orientation training, they cleared many myths about data science that we generally have, providing great insight on the subject, held group discussions to get clear focus on the topic, and practical training experience was a thorough exposure to the subject really happy attending it.



Excellent training institute to learn data science in Marathahalli, with real time scenarios , faculty teaching excellent.


Arundati Lohi

I joined for DataScience at Sgraph Infotech. Training duration was 3 months and covered all the concepts with practical scenarios. I am happy with the trainer support and regular classes. I am attending the interviews and management helping me on the placements.



I joined for Data Science training in at Sgraph Infotech on my friends reference.Trainer was expert , helped me on placements. I got offer from IBM.Thank you Sgraph.



Completed Data Science at Sgraph Infotech. Trainer was well experienced and covered Data Science with Python and R Language. I am happy with the training and i will suggest this institute for Data Science.


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