Business intelligence and analytics have become one of the most powerful tools available to retailers in an increasingly competitive world, given the vast increase in the quantity of data. Analytics is a subset of business intelligence and has been defined as “the extensive use of data…and fact based management to drive decisions and actions” (Davenport and Harris 2007, pg. 7). This module is designed to introduce to introduce the student to the field of business intelligence and analytics. The learner will develop an understanding of its importance, the practical use of analytical tools and their analysis to aid business decisions.
Introduction to Data Analytics
Understanding data analytics; customer centric data analytics; the business case for data analytics; understanding how data analytics can solve business problems; pitfalls to avoid.
Data Analytics Lifecycle
The stages of a data analytics project; examples of lifecycle models; CRISP DM.
Data Preparation
Understanding data; data quality; data preparation.
Exploratory Analysis I
Descriptive analysis of individual variables.
Exploratory Analysis II
Correlation analysis, Cross tab & chi square analysis, Anova.
Predictive Data Analysis
Regression analysis, Forecasting, Classification analysis and Cluster analysis.
Business Decision Making
The types of decision data analysis can support; using data analysis to make business decisions; data analysis in practice.
Data Visualisations
Visualising data including histograms, boxplots, scatter plots, grouped bar charts.
The module will incorporate a range of teaching and learning methods including lectures, class discussion and computer lab work. The learning environment will be practical, integrative and hands on.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Formal Examination | 60 |
| Other Assessment(s) | 40 |