A BA's Introduction to the Methods of Business Analytics Using R

Online Course

This course is designed for business analysts who have an interest in the role of analytics within their work. The language and level of concepts taught within this workshop are designed for participants with no formal training in statistics or information technology, instead it is assumed that most participants will have a business background and who are making their first tentative steps in the field of analytics.

R is the most commonly used tool for business analytics, and has been chosen for this course as it is free and publicly available. It is assumed that most course participants will not have used R or similar software previously.

This workshop will be taught through two key activities:

  • A weekly 2 hour webinar (the first 1.5 hours will focus on the methods and be almost software independent, the last 0.5 hour will be a demonstration of how to apply these methods using the R software package)
  • A set of weekly exercises using the R package

More details about these activities is described below, and can be quickly accessed by the contents links to the left.

International Institute of Business Analytics

Insight Research Services Associated is now an Endorsed Education Provider with the International Institute of Business Analysis. This course is worth 20 points toward IIBA Personal Development hours or Continuing Development Units. You do not need to be an IIBA member to register for this course.

About R

R is the most commonly used tool for business analytics. It is a free software environment for scientific and statistical computing and graphics that runs on all common computing platforms. An active and highly skilled developer community works on its development and improvement. R has become an environment of choice for the implementation of new methodology and is attracting wide attention from statistical application area specialists. The powerful and innovative graphics abilities available in R include the provision of well-designed publication-quality plots.

Course Format

This course will be presented as a ten-week course, with a two-hour webinar each week. The webinars will be presented in two different timezones each week to accommodate participants in different geographic regions and with different times of availability. These webinars will also be recorded for later viewing, and will be available until two weeks following the end of the course.

Each webinar will consist of the presentation of a set of Powerpoint slides, as well as a real-time demonstration of the use of R (using the methods taught in that week).

In each week a set of exercises and solutions will also be shared with course participants for them to work on between webinars. These solutions also contain a step-by-step guide in how to perform each exercise in R.

A LinkedIn group will be set up specifically for the purpose of this course to discuss all questions arising while students work through the course exercises. Questions will also be encouraged during the webinar presentations and also through personal email with the Instructor.

Course Syllabus

Week 1:  Introduction to the field of Business Analytics. Introduction to the R programming environment.

Week 2:  Linear regression and regression diagnostics

Week 3:  Logistic and Poisson regression - Including odds ratios, incidence rate ratios

Week 4:  Analysis of Variance

Week 5:  Factor analysis – including factor rotations, uniqueness and commonality

Week 6:  Longitudinal data analysis - mixed effects models

Week 7:  Longitudinal data analysis - generalized estimating equations

Week 8:  Clustering techniques – k means clustering, cluster linkage, and dendrograms

Week 9:  Missing data and multiple imputation

Week 10:  Data visualization (using the ggplot package in R) – including radial plots, clustered and stacked bar charts, and donut plots. Creating web interfaces in R using Rshiny.

Recommended Background

While there may be a range in demographics of the people who will enrol in this course, this course is designed for Business Analysts who have no previous training (formal or informal) in business analytics, information technology or statistics. It is assumed that most course participants will not have used R or similar software previously. The exercises in this course will require participants to type commands into R at the command line (where the commands required for each exercise will be provided).

Recommended Texts

Shmueli G., Bruce P.C., Yahav I., Patel N.R., Lichtendahl Jr. K.C. (2017). Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. 1st edn, Wiley

Maindonald, J.H. and Braun, W.J. (2010). Data Analysis and Graphics Using R. An Example-Based Approach. 3rd edn, Cambridge University Press.


Is R really free and publicly available?

For sure. R has been / is being developed by an online community of statisticians and programmers around the world that have made all of their work available for the benefit of users.

Can I download and install R prior to the workshop?

Yes, please visit https://cran.r-project.org. It is assumed that most if not all participants will not have installed R prior to the workshop, though there may be a couple of eager participants who want to make a head start.


Mark Griffin.jpg

Dr Mark Griffin is the Founding Director of Insight Research Services Associated (www.insightrsa.com), and holds academic appointments at the University of Queensland and the University of Sydney. Mark is the Chair of the IIBA Business Analytics Special Interest Group and the IIBA Asia-Pacific Regional Director. Mark also serves on the Executive Committee for the Statistical Society of Australia, and is the Chair of their Section for Business Analytics. Mark has previously taught over 80 two-day workshops and 10 five-day workshops in the fields of Business Analytics and Statistics. Major analytics projects that Mark is or has been involved in include:

  • Mark leads a research group at the University of Queensland conducting analysis of incident reports collated by the Queensland Ambulance Service. The QAS visits approximately 700,000 incidents per year where QAS staff complete a report detailing each incident. This project uses R for text analytics, market segmentation, and spatial mapping (GIS) (2017 to present).
  • Mark is leading a research group at the University of Queensland that are creating an online sample size calculator in R. This software will be used by managers of medical trials who wish to know how many patients to enrol in their trials. This work is being conducted in partnership with research collaborators at Harvard University. This project uses R for developing a web interface and for the mathematical equations involved (2017 to present).
  • Mark has developed software in R for SeqWater (where SeqWater monitors the water quality of all 28 water reservoirs in South-East Queensland). This project uses R for developing a web interface and for statistical analysis using time-series data (2017).
  • Mark led a project team evaluating the delivery of the Positive Parenting Program for the Queensland Department of Communities, Child Safety and Disability Services. This included the collection and analysis of data from 140,000 parents and 1000 practitioners (psychologists) involved in the program. This project used R for statistical analysis and data visualization (2016-2017).