Business Analytics

This module will examine the strategic implications of using data in different areas of the business. We will look at new sources of information, such as social media, and how these can be used to gain a better understanding of our potential and current stakeholders, our business environment, and our own organisation. We will also examine some of the challenges of data management such as laws protecting individual’s data and moving data between different legal jurisdictions. Additionally, we will explore some of the technological advances that are helping organisations to collect and interrogate data, such as Artificial Intelligence.

By the end of this module the student should be able to:

1. Critically evaluate the business data requirements and source required data.
2. Demonstrate a critical understanding of traditional and new statistical methods to scrutinise data.
3. Interpret analysis results, gain insights into the interrelationships of different business aspects and their effect on organisational decision making and use results/insights to inform and justify decisions.

1 Data Visualization

In these sessions we discuss some general concepts related to data visualization to help analyse data and convey information to others. We cover specifics dealing with how to design tables and charts and present an overview of some more advanced charts. We use examples from Excel to generate tables and charts, and we discuss several software packages that can be used for data visualization.

2 Data Mining

The increase in the use of data-mining techniques in business has been caused largely by three events: the explosion in the amount of data being produced and electronically tracked, the ability to electronically warehouse these data, and the affordability of computer power to analyse the data. In these sessions, we discuss the analysis of large quantities of data to gain insight on customers and to uncover patterns to improve business processes.

3 Probability and Statistical Inference

In these sessions, we show how simple random sampling can be used to select a sample from a finite population and we describe how a random sample can be taken from an infinite population that is generated by an ongoing process. We then discuss how data obtained from a sample can be used to compute estimates of a population mean, a population standard deviation, and a population proportion. In addition, we introduce the important concept of a sampling distribution. Finally, we discuss how to formulate hypotheses and how to use sample data to conduct tests of a population mean and a population proportion.

4 Regression Analysis

In these sessions, we consider simple and multiple linear regression, in which the relationship between one dependent variable, and one, or more independent variables is approximated by a straight line. In addition, we extend our discussion by studying categorical independent variables and the use of regression as a tool for prediction.

5 Machine Learning

In these sessions, we introduce students to the fundamental concepts and tools used in machine learning. We then explore the origins and practical applications of machine learning in a business context.