Participants are recommended to have preferably min. 2 years of experience in software development, business domain or data/business analysis. However, if you do not have any experiences, you can still consider taking up the course and we will advise you accordingly.

This is a 4-day intensive training program with the following assessment components.

Component 1: Written Examination (MCQ)

Component 2.  Project Work Component (PWC)

These components are individual based. Participants will need to obtain 70% in both the components in order to qualify for this certification. If the participant fails one of the components, they will not pass the course and have to re-take that particular failed component. If they fail both components, they will have to re-take the assessment.

• Understanding of Business Analytics and its impact in enterprises
• Understand the Data structures, operators and functions in R
• Learn the data loading and perform data analysis using an open source Business Analytical tool(R Analytical Tool)
• Perform data visualization using an open source Business Analytical tool(R Analytical Tool)
• Acquire knowledge on Data Mining with R
• Acquire knowledge on NoSQL databases
• Acquire knowledge on Big data analysis

Unit 1: Introduction of Business Analytics

• Introduction to Business Analytics
• Types of Business Analytics
• Business Analytics tools

Unit 2: Introduction to R

• What is R?
• Installation Procedure of R Cmdr. and RStudio interface.
• R Libraries 
• Create and Execute R Scripts
• Installing a Packages
• Working with R Objects
• Obtain help while using  R

Unit 3:  Data Structures, Operators and Functions in R 

• Vector
• Matrices 
• Data Frames
• Lists
• Factors
• Types of Operators
• Built-in Functions

Unit 4: Exploring and Visualizing Data in R 

• Import Data into R 
• Analyze and Visualize data in R 
• Pie chart
• Bar plot
• Scatterplot
• Histogram

Unit 5: NoSQL Database with R for Data Analysis 

• Introduction to NoSQL Databases
• Types of NoSQL Databases
• CouchDB
• Cassandra
• MongoDB
• Installation of MongoDB
• Import data into MongoDB
• Install RMongo package
• Write R Script
• Perform Data Analysis

Unit 6: Big Data Analysis in R 

• RHadoop
• RStorm

Unit 7: Data Mining in R

• Introduction to Data Mining, Machine Learning. 
• Clustering and its Application.
• Clustering Techniques.

Unit 8: Predictive Modelling in R

• Introduction to Predictive Modelling
• Linear Regression
• Logistic Regression.
• Neural Networks
• Support Vector Machines
• K-nearest neighbor Classification
• Decision trees

Unit 9: R Applications

• Medical Image Analysis 
• Natural Language Processing 
• Credit Risk Analysis
• Time series modeling and geospatial analysis
• Statistical Analysis

Unit 10: R Case Studies

• R for Reporting
• R for Data Analysis
• R for flood forecasting

Participants will also be exposed to various software tools in the market for analytics and would be having hands on session on R analytical tool. The course would be ideal for professionals/executives to gain an insight on the analytics, how to align to the business vision and get the value out of it.

As part of the written examination, each participant will be assessed individually on the last day of the training for their understanding of the subject matter and ability to evaluate, choose and apply them in specific context and also the ability to identify and manage risks. The assessment focuses on higher levels of learning in Bloom’s taxonomy: Application, Analysis, Synthesis and Evaluation. This written examination will primarily consist of 40 multiple choice questions spanning various aspects as covered in the program. It is an individual, competency-based assessment.

The objective of the certification examination is to evaluate the knowledge and skills acquired by the participants during the course. The weightage in key topics of the course as follows:

• Introduction to Business Analytics [10%]
• Introduction to R [15%]
• Data Structures, Operators and Functions in R [10%]
• Exploring and Visualizing Data in R [10%]
• NoSQL Database with R for Data Analysis [10%]
• Big Data Analysis in R [10%]
• Data Mining in R [10%]
• Predictive Modelling in R [15%]
• R Applications [5%]
• R Case Studies [5%] 

• R
• MongoDB