Analytics in Action II

Learning Outcomes

Knowledge and Understanding

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

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

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

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

Transferable and Generic Skills

Having successfully completed this module you will be able to:

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

The topics covered in this module will include:

• Introduction and overview of various business analytics techniques;
• Sources of different types of data (e.g. unstructured, network, etc.)
• Using advanced analytics model (e.g. advanced generalized linear models, random forests, deep learning, etc.) to conduct analysis with various types of data to solve different business problem settings
• Big data solutions and technologies: the main challenges that drive the need to NoSQL, differences with relational databases, principles of cloud computing.
• Deep learning and non-linear models: basic principles, feature extraction, ensembles, modelling.

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

Resources & Reading list

Textbooks

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.

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

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

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

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

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

Formative

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

Summative

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

Referral

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

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.

Repeat Information

Repeat type: Internal & External