COM6053 - Data Science for Business

Objectives:

On successful completion of the module, students will be able to:
Collect meaningful customer data to inform business goals and strategies while understanding the legal and ethical requirements surrounding its storage, transfer, processing and analysis;

Prepare raw data for analysis, including the transformation, interrogation and cleansing of data from multiple sources;
Apply computational techniques and statistical methods to the analysis of business data;
Produce information-rich data visualisations and derive meaningful business insights;
Understand basic concepts underpinning distributed data processing and utilise a range of big data tools.

Content:

This module teaches students the fundamental principles of data science while equipping them with the analytical thinking needed to extract business value from data. The module aims to prepare students for roles which combine data analysis with data-driven decision making. It recognises the role of big data analytics in business by introducing students to the basics of big data processing. Knowledge from previous modules will inform students' approaches and strengthen their business insights.

Topics covered on the module include:
- Collection, cleaning and storage of data, including the mining of relevant information from large and complex data sets
- Legal and ethical requirements concerning the storage and handling of customer data
- Analysis of data using statistical toolkits
- Creation of reports, dashboards and data visualisations to inform decision making

Learning and Teaching Information:

Through a mixture of tutorials and seminars students will develop an understanding of the role of data analytics in business and how it influences business decision making. The case study is used as a vehicle for content delivery through which tools and techniques for working with data are introduced. Case studies will be taken from local and large-scale businesses with input from local industry. Examples of case studies to illustrate the required level and scope, include:

- How have Yorkshire Water used open data sources to obtain real-time information and understand trends about its water and sewerage operations. Also exploring the impact of these insights on its business services.
- How ASDA is using technology and data to understand its customers' journeys across its digital channels and use these insights to optimise its large-scale marketing investment.
- How Spotify have analysed the music of different artists to create Spotify playlists. Also attempting to understand the value this has brought to the business and to quantify this value using data.
- How NHS Digital have leveraged big data analytics to save money and redesign services. Also considering how they engage with the Alan Turing Institute's Data Ethics Group to work towards an ethical approach to the use of data science in the health care sector.

Group work with on-going formative and developmental feedback is integral to the learning and teaching. Through workshops and hands-on tutorials students develop a portfolio of example analyses which will be the focus of student-led seminars.

Tutorials/workshops
Hours: 36
Intended Group Size: Cohort

Student-led seminars
Hours: 24
Intended Group Size: Cohort

Guided independent study
Hours: 240

Further details relating to assessment
Presentation: an analytical data report delivered as a presentation with on-screen visuals. Students will select or conceive of a scenario around which they will design their analysis. The presentation should last ~15 minutes allowing 5 minutes for questioning.

E-Portfolio: across the module students will build up a data science portfolio showcasing their skills. The portfolio will take the form of a website and/or git repository. Projects in the portfolio may centre on scenarios introduced in the tutorials or those defined by the student. Where a student-defined project relies on the collection or handling of sensitive data, ethical approval must first be obtained through the University's Ethical Approval Committee. In such cases the process of ethical approval will be considered in relation to Number 1 of the learning outcomes.

Other relevant matters
Students will develop the report over several weeks, receiving on-going formative feedback prior to final submission at the end of the module.

Assessment:

001 Presentation/oral exam; 20 mins 2000 word equiv; end semester 1 30%
002 E-Portfolio; 4000 word equiv; end semester 1 70%

Fact File

Module Coordinator - Yashar Baradaranshokouhi
Level - 6
Credit Value - 30
Pre-Requisites - NONE
Semester(s) Offered - 6S1

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