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.

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Course Highlights

Upon successful completion of this course, participants should be able to:

  • 10 live classes of 3 hrs each by Industry practitioners
  • Gain insight into the 'Roles' played by a Data Scientist
  • Analyse Big Data using R, Hadoop and Machine Learning
  • Understand the Data Analysis Life Cycle
  • Work with different data formats like XML, CSV and SAS, SPSS, etc.
  • Learn tools and techniques for data transformation
  • Understand Data Mining techniques and their implementation
  • Analyse data using machine learning algorithms in R
  • Work with Hadoop Mappers and Reducers to analyze data
  • Implement various Machine Learning Algorithms in Apache Mahout
  • Gain insight into data visualization and optimization techniques
  • Explore the parallel processing feature in R
United States
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Curriculum

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, 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

Understanding vectors in R, Reading Data, Combining Data, subsetting data, sorting data and some basic data generation functions.

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.

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.

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.

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.

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.

Mahout Introduction and Algorithm Implementation

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

Additional Mahout Algorithms and Parallel Processing using R

Implementation of different Mahout Algorithms, Random Forest Classifier with parallel processing Library in R.

Who is this Course for?

The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:

  • Developers aspiring to be a 'Data Scientist'
  • Analytics Managers who are leading a team of analysts
  • SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
  • Business Analysts wanting to understand Machine Learning (ML) Techniques
  • Information Architects wanting to gain expertise in Predictive Analytics
  • 'R' professionals who want to captivate and analyse Big Data
  • Hadoop Professionals who want to learn R and ML techniques
  • Analysts wanting to understand Data Science methodologies
  • Statisticians looking to implement the statistics techniques on Big data
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Certification

Participant can attend the certifications exam.

It is mandatory that a participant to clear the online exam with minimum score of 80% to be Certified in Data Science.

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FAQs

1. Who will be the trainer for the training?

Highly qualified and certified instructors with industry relevant experience deliver trainings.

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