Data Science is a process of extracting knowledge from data. Data science is emerging to meet the challenges of processing large data sets which require versatile skill set and specialized in specific domain. Data scientist analyse the complex problems and ensure rich consistency of data sets with creating visualizations to aid in understanding data. Data science training is designed to teach the techniques of data mining and gain knowledge on insight of visualization and optimization of data to become a successful Data Scientist. Data Science Training drops an insight on data visualization and techniques of Data Mining.This course gives an overview of the data, and gives answers to all questions, and tools that data analysts and data scientists work with.

- Introduction to Big Data
- Roles played by a Data Scientist
- Analysing Big Data using Hadoop and R
- Different Methodologies used for analysis in Data Science
- 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)
- Application of Machine Learning Techniques
- Data Visualization etc.,
- problem statement of few data science problems which we shall solve during the course
- Basic Data Manipulation using R in Data Science.
- This module teaches how to manipulate data and use R for all kinds of data conversion and restructuring processes that are frequently encountered in the initial stages of data analysis in Data Science Training.
- Understanding vectors in R
- Reading Data
- Combining Data
- sub-setting data
- sorting data and some basic data generation functions

- The goal of machine learning is to create a predictive model, that is indistinguishable from a correct model. This module, starts off giving you an overview about machine learning in Data science Training.
- Machine Learning Overview
- ML Common Use Cases and techniques
- Clustering and Similarity Metrics
- Distance Measure Types: Euclidean, Cosine Measures, Creating predictive models

- The module is designed to teach you ‘k’ means clustering, association rule mining and much more..
- Understanding K-Means Clustering in Data Science
- Understanding TF-IDF and Cosine Similarity and their application to Vector Space Implementing Association rule mining in R.

- The last part of machine learning module of Data Science course, trains on Decision Tree’s , Random forests concept in Data Science.
- Understanding Process flow of Supervised Learning Techniques Decision Tree Classifier
- How to build Decision trees
- Random Forest Classifier
- What is Random Forests concept in data science
- Features of Random Forest
- Out of Box Error Estimate and Variable Importance
- Naive Bayes Classifier

- This module of Data science course, will give good knowledge on how R is integrated with R, the integrated programming environment and writing MapReduce jobs.
- Integrating R with Hadoop using R
- Exploring RHIPE (R Hadoop Integrated Programming Environment)
- Writing MapReduce Jobs in R and executing them on Hadoop

- Understand the Hadoop architecture, its commands, SQOOP and other data loading techniques in this module.
- 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
- Data Science Mahout Introduction and Algorithm Implementation
- By the end of this module , you will be able to implement machine learning algorithms with Mahout
- Implementing Machine Learning Algorithms on larger Data Sets with Apache Mahout Additional Mahout Algorithms and Parallel Processing using R
- In this module of Data Science Training you will learn how to implement Random Forest Classifier with Parallel Processing Library using R in this module of Data Science Training.
- Implementation of different Mahout algorithms Random Forest Classifier with parallel processing Library in R

- The aim of the project module is to let you have and idea of what a project is, problem statement, various approaches and solving algorithms.
- Project Discussion
- Problem Statement and Analysis
- Various approaches to solve a Data Science Problem
- Pros and Cons of different approaches and algorithms