The context for the Statistical Foundations of Business Analytics module is management in complex, interconnected, data-driven environments.
The benefits that can be achieved through the appropriate analysis of data are significant. But companies face significant challenges dealing with large, complex data sets that are difficult to process and learn from. And it is all too easy to misunderstand the data and its structure or to apply inappropriate analytical techniques, which consequently draw flawed conclusions.
Managers need to understand the variety of statistical techniques and complex analytics that can extract value from complicated, multifaceted data. And know when and how these techniques can be applied.
Businesses are increasingly using application specific platforms and tools where the analytics are embedded into the software. However, understanding the underlying statistical techniques is important to appreciate the limitations of such tools.
This module introduces students to the range of statistical techniques that underpin business analytics and develops their understanding of the challenges of handling complex data through the study of selected techniques.
The aims of the Statistical Foundations of Business Analytics module are:
To provide students with an understanding of some of the key statistical concepts that underpin business analytics.
To expose students to the range of statistical techniques used to analyse large, complex data sets, such as probabilistic methods, Bayesian analysis, econometric models, supervised learning and unsupervised learning.
To introduce students to the principles of scientific experimentation, including hypothesis construction, experimental testing and design, and population selection and sampling, needed to evaluate the validity of data analyses.
To provide students with an understanding of selected statistical techniques and how they are used in practice.
To ensure that students have the necessary statistical skills to be able to make effective use of the latest business analytics platforms and tools.