module specification

This module will enable students to understand the fundamental concepts of data science and appreciate key techniques of data science and its applications in a wide range of business context. Students will be exposed to data understanding, preparation, modelling, results evaluation and data visualisation techniques that can assist businesses in making effective data-driven decisions to improve productivity and consumer satisfaction. Students will be introduced to the practical application of tools and techniques required to perform data science projects in a modern business environment.

The main areas of the module syllabus include:

Introduction to key concepts in data science: data science tools, approaches, techniques and application scenarios. LO1
An overview of data mining process for business: business understanding, data understanding, data preparation, data modelling, results evaluation and deployment. LO1, LO4
Business data environment: operational database, data warehouse, web and big data platform. LO2, LO5
Data understanding and preparation: data measurement, statistical summaries, transformation, cleaning, and graphical visual exploration. LO3, LO4
Data science business applications: credit scoring, fraud detection, customer relationship management, associated products detection, correlation and identification. LO2, LO5
Web data analysis: Social network analysis, affiliation prediction and recommendation.
LO2, LO5

Topics will be introduced through the medium of formal lectures, supported by tutorial and workshop sessions, and blended learning as follows:
- Lecture (1 hour / week): Introduction of the major topics identified in the syllabus, plus for practical exercises, directed reading and other further study
- Tutorial/ Workshop (2 hour / week): Consolidating understanding of topics introduced in the lecture via class and group discussions, informal presentations and other activities in the tutorial sessions. Data analytic skills will be further developed through lab-based workshops.
- Blended learning: Using the University’s VLE and online tools to deliver content, assessment and feedback, to encourage active learning, and to enhance student engagement and learning experience.
Students will be expected and encouraged to produce reflective commentaries and an action plan for personal development on the learning activities and tasks that they carry out to complete their work, e.g. in the form of an assessed section of their coursework report.

On successful completion of this module, the student will be able to:
LO1: Understand fundamental concepts and techniques of data science.
LO2: Appreciate the business context in which the analysis of data can be fruitful and effective for decision-making and creating value.
LO3: Understand and compare the techniques and tools for analysing and visualising data.
LO4: Develop the practical skills in preparing, modelling and visualising data.
LO5: Gain exposure to the practice of formulating and structuring problems and identifying the relevant tools to aid problem-solving.

Students are assessed by two compulsory assessments [LO1-5].
The first compulsory assessment is an individual coursework assignment [1500 words + analytical output and evaluation] designed mainly to assess the practical aspects of the module. It will provide students with the opportunity to undertake research on current issues and practical techniques in data analytics and the effective application and deployment of these approaches [LO1, LO2]. It will also enable students to apply their knowledge to a practical business problem, demonstrating their skills for problem-solving and critical evaluation [LO3, LO4, LO5].
The second compulsory assessment is a 2-hour written exam designed to assess the understanding of fundamental concepts and their practical application [LO1, LO2, LO3, LO5]. 
Consistent with University policy, formative and summative feedback will be provided at various points throughout the teaching semester. Feedback will be delivered in lectures or in workshop sessions and/or via the VLE depending on the specific assessment instrument.

Where possible, the most current version of reading materials is used during the delivery of this module.  Comprehensive reading lists are provided to students in their handbooks.  Reading Lists will be updated annually.

Textbooks:

Core Text:
• Provost, F. & Fawcett, T., Data Science for Business: What you need to know about Data Mining and Data-analytic thinking, (2013), O’Reilly Media

Other Texts:
• Cady, F., The Data Science Handbook, (2017), Wiley-Blackwell
• EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, (2015), John Wiley & Sons
• Graham, A., Statistics – A Complete Introduction, e-book (2013), Hodder & Stoughton.
• Jeffrey M. Stanton, 2013. Introduction to data science – e-copy is freely available at (last accessed February 2018) https://ischool.syr.edu/media/documents/2012/3/DataScienceBook1_1.pdf

Journals:
• ACM Transactions on Knowledge Discovery from Data (TKDD), ACM New York, NY, USA, ISSN: 1556-4681.
• The Computer Journal of the British Computer Society, ISSN 1460-2067 (Electronic); Publisher: Oxford: Oxford Journals, Oxford, UK : Oxford University Press.
Websites:
• Data Science Central: http://www.datasciencecentral.com
• Safari Books Online
Electronic Databases (available from the University Library)
• ACM Digital Library
• IEEE Xplore/IET Digital Library
Other
• Lynda.com