Data Science with R Certification Training

Online Classroom

Online Classroom

30 Contact Hours/PDUs

10 live classes of 3 hrs each by Industry practitioners

Data Science

Gain insight into the 'Roles' played by a Data Scientist
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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.

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

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
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.

 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-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.

1. Who will be the trainer for the training?

Highly qualified and certified instructors with industry relevant experience deliver the training.

2. How do I enrol for the training?

You can enrol for the training through our website. You can make online payment using any of the following options:

  • Visa debit/credit card
  • American Express and Diners Club cards
  • Master Card
  • PayPal

Once the online payment is done, you will automatically receive payment receipt, via email.

3. Can I cancel my enrolment? Do I get a refund?

Yes, you can cancel your enrolment. We provide you complete refund after deducting the administration fee. To know more please go through our Refund Policy.

4. Do you provide money back guarantee for the training programs?

Yes, we do provide money back guarantee for some of our training programs. You can contact support@redstonelearning.com for more information.

5. Do you provide assistance for the exam?

Yes, we do provide guidance and assistance for some of our certification exams.

There is no such governing body administers Data Science with SAS exam. However, many training providers conduct exam to evaluate a candidate’s skills on Data Science with SAS training.


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