Analytics have become one of the most powerful tools available to decision makers. The module is designed to introduce students to a variety of predictive techniques using industry-standard (but highly accessible) purpose-designed software, geared to the special nature of ‘Big Data’. The purpose of the module is to develop student’s skills in the use of data and models to support decision making in business. Participants will also receive a thorough grounding in relevant underlying statistical /probability theory in a practical and user-friendly lab environment.
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 data analysis I
Descriptive analysis of individual variables.
Exploratory data analysis II
Correlation analysis, Cross tab & chi square analysis, Anova.
Predictive models
Regression analysis, Forecasting, Classification analysis and Cluster analysis.
Business Decisions
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 with a hands-on approach on using software to analyse and model data to find solutions to real-world problems.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Other Assessment(s) | 100 |