Business Analytics with R | Get Hands-on with Business Analytics

Business Analytics with R Course Description

R is an open source programming language consists of several standard statistical tests, analyses and models.

Business Analytics enables the organizations to make better decisions and develop business insights using extensive use of data, statistical methods, predictive models, and fact-based management. Analytical skills are essential not only for data scientists but also for all decision makers. This program will provide an overview of the business analytics, data exploration techniques, and introduction level predictive modeling that are widely used in business. This program is an entirely hands-on approach to data analysis using R software.

Business Analytics with R Course Learning Outcomes

  • Optimize business situations that involve whole numbers, such as employees to deploy.
  • Optimize business decisions that take multiple input variables to predict between two possible outputs.
  • Model decisions under a variety of future uncertain states, depending on the decision maker’s proneness or aversion to risks.
  • Compute correlation where, at first glance, there seem to be none – correlation between data points in a time series.
  • Compute the regression model for time series data that has correlation within itself.
  • Optimize business situations where two variables do not move in a linear fashion.
  • Test hypothesis for experiments involving different treatments.
  • Model continuous outcomes that depend on more than one input variable.
  • Group data points dynamically based on the similarities among the members of each group.

Business Analytics with R Training - Suggested Audience

This training is aimed at professionals who want to learn data analytics skills. Suggested attendees based on our past programs are:
  • Software Engineers
  • Data analytics professionals
  • Data Analysts
  • Business Analysts
  • Research professionals

Business Analytics with R Training Duration

  • Open-House F2F (Public): 5 days
  • In-House F2F (Private): 4/5 days, for commercials please send us an email with group size to

Business Analytics with R Training - Prerequisites

Knowledge of programming concepts and familiarity with excel is desirable but not mandatory.

This Business Analytics with R training course outline includes:

1. Introduction to Business Analytics and R Programming
  • Get introduced to the basics, evolution, and scope of business analytics. 
  • Learn R and data manipulation, functions and data visualizations in R.

2. Statistics and Data Exploration
  • Understand the various types and applications of statistics as well as the types of data and statistics variables. 
  • Master the art of making informed decisions using summary statistics.

3. Random Variable and Normal Distribution and Hypothesis Testing
  • Learn random variables, expected value, probability distribution, standard deviation, variance and the types of distributions. 
  • Learn how to state null and alternative hypotheses, understanding Type-I and Type-II errors. Conduct one-sided hypothesis test for population.

4. Correlation and Regression
  • Apply correlation, strength of linear association, least-squares or regression line, linear regression model
  • Gain expertise in multiple regression, regression diagnostics and detection of collinearity: simple signs.

5. Model creation and selection
  • Learn about fitting of model, diagnostic plots, comparison of models, cross-validation, variable selection, relative importance and Box-Cox transformations.

6. Logistic Regression
  • Master binary response regression model and linear regression output of proposed model. 
  • Work on the various problems with linear probability model, logistic function, logistic regression & its interpretation and the various odds ratio, goodness of fit measures and confusion matrix.
Keny White


Keny White is Professor of the Department of Computer Science at Boston University, where he has been since 2004. He also currently serves as Chief Scientist of Guavus, Inc. During 2003-2004 he was a Visiting Associate Professor at the Laboratoire d'Infomatique de Paris VI (LIP6). He received a B.S. from Cornell University in 1992, and an M.S. from the State University of New York at Buffalo.


After working as a software developer and contractor for over 8 years for a whole bunch of companies including ABX, Proit, SACC and AT&T in the US, He decided to work full-time as a private software trainer. He received his Ph.D. in Computer Science from the University of Rochester in 2001. "What I teach varies from beginner to advanced and from what I have seen, anybody can learn and grow from my courses".


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