Module Directory

The module covers two important aspects of managing data within any organisation. First, the fundamental concepts that underlie the techniques and algorithms deployed by data science teams to extract information from data. Second, the framework that aligns the understanding of three different types of teams that managers need to coordinate: business team, the technical/software development team, and the data science team.

While the module does not require a sophisticated mathematical background, the content of the module by definition being technical, we cannot exclude it. Nevertheless, the exposition involves significant data handling, software skills and associated data computations. Expect a hands-on and hands-full, experience. The focus, however, is on those conceptual aspects of data science with which managers increasingly need to be familiar.

The aims of this module are:

• To describe and explain the key principles employed in data science, and how they are applied in contemporary organisations.

Knowledge of the key principles employed in data science will:

• Equip managers with an awareness of, and the ability to critically assess alternative ways of envisioning the business problem from a data-centric perspective.


• Enable managers to select, from different data science techniques, those suitable and viable ones for the business problem at hand.


• Enable managers to distil insights from the results of the data mining techniques selected and identify those insights that can profitably be deployed.


• Enable managers to identify the need to iteratively address the business problem that was initially envisioned and finally to help decide when to stop the iteration cycles.

By the end of this module, students will be expected to be able to:

1. Demonstrate a comprehensive understanding of the organisational setting into which data science fits, including how to select, structure and retain data science teams, how to develop competitive advantage when employing data, and demonstrate awareness of tactical concepts for running data science projects.


2. Select and apply suitable lenses to identify the relevant data to invest in and the methods to acquire that data.


3. Explain data mining processes and understand how high-level data mining tasks need to be delegated.


4. Demonstrate a systematic understanding of the concepts underlying the vast array of algorithms for prospecting and mining the business-relevant knowledge from the vast data that is now increasingly flowing into organisations.

Indicative Lecture Programme

• Introduction – Data Analytic Thinking.


• Business Problems & Data Science Solutions.


• Prediction, Entropy & Information Gain.


• Segmentation & Strategy, Classification Trees.


• Fitting a model to data (Estimation), OLS, Support Vector Machines.


• Overfitting, Holdouts, Cross-Validation.


• Metric Spaces: Similarity, Neighbours, Clustering & Marketing.


• Data Analytic Thinking: Expected Value, Type I & II errors, and Cost Benefit Matrix.


• Evaluating Performance of Models: ROC, AUC, Lift Curves.


• Text Mining, Sparseness, TFIDF.

This module will be delivered via:

• Four days of lectures and computation slots in one week.