MODULE SPECIFICATION

1.  Title of the module

Introduction to Big Data (CB554)

2.  School or partner institution which will be responsible for management of the module

Kent Business School

3.  The level of the module (e.g. Level 4, Level 5, Level 6 or Level 7)

Level 5

4.  The number of credits and the ECTS value which the module represents

15 credits (7.5 ECTS)

5.  Which term(s) the module is to be taught in (or other teaching pattern)

Spring

6.  Prerequisite and co-requisite modules

Prerequisites: At least one quantitative research module that covers the concept of statistical significance and basic statistical modelling (e.g. CB313 Introduction to Statistics for Business, SO410 An Introduction to Quantitative Research or equivalent) at the discretion of the module convenor.

7.  The programmes of study to which the module contributes

BA Business Administration with Business Analytics, BA Sociology with Quantitative Research, BA Social Policy with Quantitative Research, BA Criminology with Quantitative Research, BA Politics & International Relations with Quantitative Research, BA Law with Quantitative Research, and BSc Statistical Social Research. Also available as a wild module to those that can satisfy the prerequisites above.

8.  The intended subject specific learning outcomes.
On successfully completing the module students will be able to:

8.1 / Demonstrate knowledge and comprehension of different types of data (e.g. structured vs. unstructured data; static vs. streaming data).
8.2 / Conceptualise and design different types of data analysis tasks (e.g. supervised, semi-supervised and unsupervised learning tasks).
8.3 / Demonstrate knowledge of different types of tools for data collection, data cleaning and integration, data visualisation, text mining, social network analysis and parallel data mining (e.g. R, Hadoop).
8.4 / Analyse and synthesise big data related challenges (e.g., privacy issues, data storage) and data analysis processes.

9.  The intended generic learning outcomes.
On successfully completing the module students will be able to:

9.1 / Conduct data preparation, data modelling, and model evaluation;
9.2 / Conduct structured data analysis, text mining, social network analysis
9.3 / Identify, analyse, and address data analysis problems;
9.4 / Interpret the outputs of data analysis projects;

10.  A synopsis of the curriculum

This module aims to address these aspects and challenges of Big Data Analytics by introducing fundamental concepts and algorithms of big data analytics. It starts with introduction of methods and tools of data collection, and then followed by methods of dealing with dirty data such as inconsistent data, missing data and redundant data, on which techniques of data preparation including data cleaning, data transformation and integration are addressed. Having discussed those contents, the module will then be continued with methods for structured data and unstructured data, where techniques for structured data include data mining (in particular parallel data mining techniques) and those for unstructured data include social network analysis and text mining. A further aim of the unit is to introduce software systems used for Big Data Analytics such as Hadoop.

Below is the outline of the module.

·  Concept of big data

·  Data collection, cleaning, transformation, and integration

·  Streaming data analysis

·  Parallel data mining

·  Structured data analysis

·  Social network analysis

·  Text analysis

11.  Reading List (Indicative list, current at time of publication. Reading lists will be published annually)

Reading will be taken from a set of specified articles to be published in the module guide. These will be a mixture of academic and non-academic sources. Such reading will provide the intellectual platform for the module beyond the lecture series.

Recommended Text Books:

·  Marz, N., & Warren, J. (2015). Big Data: Principles and best practices of scalable realtime data systems. Manning Publications Co. (ISBN: 9781617290343)

·  Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt (ISBN 978-0-544-00269-2).

·  Zikopoulos, P., & Eaton, C. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media. (ISBN: 978-0-07-179053-6)

Journal articles from scientific Journals

·  Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. Knowledge and Data Engineering, IEEE Transactions on, 26(1), 97-107.

·  Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society, 15(5), 662-679.

·  Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36(4), 1165-1188.

12.  Learning and Teaching methods

There will be a one-hour lecture per week for the 11 teaching weeks of term, run in a workshop format where new concepts and requisite theory are introduced, illustrated by reference to practical experience. One-hour seminars will be run weekly to enable students to apply theories taught during the course to real examples. Students will be expected to contribute to seminars in an active sense.

13.  Assessment methods

The module will be assessed by 100% coursework, split as follows:

Exam (40%): An open book exam covering the content the module taught.

Assignment 1 (30%): an individual report (1500 words) involves a critical review of big data.

Assignment 2 (30%): Analysing big data: to design and execute an analysis of a large data set using the Amazon Elastic MapReduce (EMR) web service (or similar service).

14.  Map of Module Learning Outcomes (sections 8 & 9) to Learning and Teaching Methods (section12) and methods of Assessment (section 13)

Module learning outcome / 8.1 / 8.2 / 8.3 / 8.4 / 9.1 / 9.2 / 9.3 / 9.4
Learning/ teaching method / Hours allocated
Independent Study / 128 / X / X / X / X / X
Lectures / 11 / X / X / X / X / X / X
Seminars / 11 / X / X / X / X / X / X / X / X
Assessment method
Exam / X / X / X / X / X / X / X
Assignment 1 / X / X / X
Assignment 2 / X / X / X / X / X / X

15.  The School/Collaborative Partner (delete as applicable) recognises and has embedded the expectations of current disability equality legislation, and supports students with a declared disability or special educational need in its teaching. Within this module we will make reasonable adjustments wherever necessary, including additional or substitute materials, teaching modes or assessment methods for students who have declared and discussed their learning support needs. Arrangements for students with declared disabilities will be made on an individual basis, in consultation with the University’s/Collaborative Partner’s (delete as applicable) disability/dyslexiastudent support service, and specialist support will be provided where needed.

16.  Campus(es) or Centre(s) where module will be delivered:

Canterbury.

FACULTIES SUPPORT OFFICE USE ONLY

Revision record – all revisions must be recorded in the grid and full details of the change retained in the appropriate committee records.

Date approved / Major/minor revision / Start date of the delivery of revised version / Section revised / Impacts PLOs( Q6&7 cover sheet)

4

Module Specification Template (September 2015)