Analytics in Action II

Module overview

Having learnt the basic techniques and principles of business analytics in previous semester 1 modules, this module will introduce you to a number of advanced machine learning methods and their applications in practice. These include machine learning methods, big data solutions and technologies, and advanced models to extract complex non-linear patterns from large amounts of diverse data. The focus will be on the underlying principles, modelling methodologies, and implementation using appropriate software packages.

Linked modules

Pre-requisite: MANG3108

Aims and Objectives

Learning Outcomes

Transferable and Generic Skills

Having successfully completed this module you will be able to:

communicate effectively.
plan and control effectively for successful completion of a personal workload;
self-manage the development of learning and study skills;

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

identify the analytical models;
handle various decisions with complex/big data;
use basic heuristics to set booking limits;
assess the relevance of software packages outputs to the decisions being addressed;
appropriate for analysing the work with relevant software packages to develop credit scoring solutions;
work with current software packages to create models using complex data sources.

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

interpret the output of advanced analytics techniques used for complex data analytics applications.
solutions and technologies specifically designed for handling and extracting patterns from big data;
underlying theory of credit scoring;
solutions basic principles of pricing and revenue management;

Learning and Teaching

Teaching and learning methods

Teaching methods include: • Lectures • Interactive case studies • Problem-solving activities • Computer labs • Directed reading • Private/guided study Learning activities include: • Introductory lectures • Two assignments (individual written reports) • Case study / problem solving activities • In class debate and discussion • Private study • Use of video and online materials

Study time
Type	Hours
Follow-up work	40
Preparation for scheduled sessions	20
Supervised time in studio/workshop	10
Tutorial	8
Revision	10
Lecture	24
Completion of assessment task	46
Total study time	158

Resources & Reading list

Textbooks

Chollet, F. (2017). Deep Learning with Python. Manning Publications.

Talluri, K.T. and van Ryzin, G.J. (2005). The Theory and Practice of Revenue Management. Springer.

Gaurav, V. (2013). Getting started with NoSQL: Your guide to the world and technology of NoSQL. Packet Publishing Ltd.

Goodfellow, I., Bengio, Y. and Courville, A. (2017). Deep Learning. Available freely online at http://www.deeplearningbook.org/: MIT Press.

Hastie, T., Tibshirani, R. and Friedman, J. (2013). The Elements of Statistical Learning. Available freely online at https://statweb.stanford.edu/~tibs/ElemStatLearn/. NI, USA: Springer.

Thomas, L.C., Crook J.N. and Edelman. (2017). Credit Scoring and Its Applications. Philadelphia, PA, USA: SIAM Press.

Assessment

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

In-class activities

Assessment Type: Formative
Feedback: Feedback will arise from in-class activities such as problem-solving activities and discussions, and also from computer labs
Final Assessment: No
Group Work: No

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Report	40%
Report	60%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Report	100%

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Breakdown
Method	Percentage contribution
Report	100%

Repeat Information

Repeat type: Internal & External