# Data Science

edureka$ 431 - (Rs 28,796)

+ VAT

## Important information

- Course
- Online

This Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.

## What you'll learn on the course

Science |

## Course programme

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 - 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, 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.

2. Basic Data Manipulation using RLearning Objectives - In this module, you will learn the various data manipulation techniques using R.

Topics - 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-1Learning Objectives - In this module, you will get an overview of the Machine learning Algorithms, and Supervised and Unsupervised Learning Techniques.

Topics - 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-2Learning 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 - 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-3Learning 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 - 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 ArchitectureLearning Objectives - In this module, you will learn the HDFS Architecture, MapReduce Paradigm and few data acquisition techniques in Hadoop.

Topics - 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 HadoopLearning Objectives - In this module, you will learn the methods to integrate two popular open source softwares for Big Data analytics: R and Hadoop. You will also learn techniques to write your own Mappers and Reducers.

Topics - 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 ImplementationLearning 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 - Implementing Machine Learning Algorithms on larger Data Sets with Apache Mahout.

9. Additional Mahout Algorithms and Parallel Processing using RLearning Objectives - In this module, you will learn how to implement Random Forest Classifier with Parallel Processing Library in R

Topics - Implementation of different Mahout algorithms, Random Forest Classifier with parallel processing Library in R.

10. ProjectLearning Objectives - In this module, you will learn various approaches to solve a Data Science problem and How different technologies and Tools (R, Hadoop, Mahout) work together in a typical Data Science Project.

Topics - Project Discussion, Problem Statement and Analysis, Various approaches to solve a Data Science Problem, Pros and Cons of different approaches and algorithms.