2019/20 Taught Postgraduate Module Catalogue

GEOG5917M Big Data and Consumer Analytics

15 creditsClass Size: 200

Module manager: Lex Comber
Email: a.comber@leeds.ac.uk

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2019/20

This module is not approved as an Elective

Module summary

This practical module explores the role of large volume and high-temporal frequency transactional, social media and digital trace big data in deriving powerful insight into consumer behaviours, retail systems, marketing and service delivery. The module content reflects topical developments in this field and draws closely on the work of the Leeds Institute for Data Analytics (LIDA) and the Consumer Data Research Centre (CDRC). Students are taught predominantly via computer practicals, currently undertaken in R, the open source statistical software that is increasingly important in academic and commercial sectors. Students will gain an understanding of the innovative ways through which a range of big data sources are used to reveal consumer behaviours, sentiment and preferences and to inform and evaluate service provision, marketing campaigns, and consumer flows to name a few examples. Students consider how industry derives value from both the location- based and textual (sentiment) content of social media data and explore flow data at a variety of spatial scales. The module includes a focus on ethical and privacy concerns and other benefits, opportunities and challenges that must be addressed when working with large-scale consumer data to derive commercial or policy insight.

Objectives

This module seeks to:
-Outline the role of data analytics in supporting business decision making, with particular reference to consumer data, consumer facing commercial sectors

-Introduce students to open source software and tools commonly used in academia and the commercial sector for handling and analysing transactional big data

-provide students with practical experience in deriving spatial insight from a range of transactional data sources using innovative analytic tools and case studies informed by research and commercial practice

-Enable students to consider fully the ethical and privacy concerns associated with analysis of consumer big data

Learning outcomes
On completion of this module students will:
1. Be able to explain the role and value of consumer data and big data analytics in supporting commercial decision making, using a variety of topical examples

2. Demonstrate competency in handling a range of datasets , using open source software and analytic tools to summarise, analyse and visualise trends within the data

3. Be able to clearly derive spatial insight from analysis of big data, using outputs suitable for a range of audiences and problem types

4. Have a critical appreciation of the opportunities and challenges, including ethical and privacy concerns, in using big data to derive commercial insights

Syllabus

What is Big Data?
Introduction to CDRC, LIDA and applied Big Data research at the University of Leeds
Why is Big Data important to business analytics?
Data linkage, data summaries
Making models (non-spatial, spatial, machine learning)
Reproducibility and Transparency
Big Data Privacy and Ethics
Big Hype? What is the future for big data in business analytics?

Teaching methods

Delivery type	Number	Length hours	Student hours
Practical (computer based)	5	3.00	15.00
Lecture	5	2.00	10.00
Practical	5	2.00	0.00
Private study hours			125.00
Total Contact hours			25.00
Total hours (100hr per 10 credits)			150.00

Private study

Private study comprises directed core reading to contextualise practical activities.
Research and preparation for assessed academic essay. Independent work on practical activities outside timetabled sessions.

Opportunities for Formative Feedback

Short question and answer sessions integrated into lectures provide an opportunity for formative assessment of student progress throughout the taught component of this module. Computer practicals and practical help sessions enable staff and postgraduate demonstrators to assess progress of the cohort and individual students and to provide additional support, clarification and feedback where required.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Project	Data analysis project 2,500 word (4,000 word equivalent)	100.00
Total percentage (Assessment Coursework)		100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

There is no reading list for this module

Last updated: 24/09/2019

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