DATA ANALYTICS - 2017/8

Module code: MANM301

Module provider

Surrey Business School

Module Leader

GROVER V Dr (SBS)

Number of Credits

15

ECT Credits

15

Framework

FHEQ Level 7

JACs code

G300

Module cap (Maximum number of students)

N/A

Module Availability

Semester 1

Overall student workload

Independent Study Hours: 106

Lecture Hours: 22

Laboratory Hours: 20

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework INDIVIDUAL COURSEWORK 2500 WORDS 50%
Examination 2 HOUR EXAMINATION (CLOSED BOOK) 50%

Alternative Assessment

Not applicable

Prerequisites / Co-requisites

None

Module overview

This module is the science of examining raw data in order to support businesses and organisations in their decision making. On one hand this module looks at relationships of entities in databases using the Structured Query Language to extract relevant information efficiently. On the other hand it introduces unstructured data concepts. Special focus is given to Big Data providing knowledge, analysis and practical skills to gain additional business and customer insights. Fundamental statistical techniques to extract the essential management information are shown.  This module is the foundation for further investigations and a cornerstone of the programme.

Module aims

The course offers students an opportunity to analyse, manipulate and visualise data using state-of-the-art techniques. The module aims to extract knowledge using data mining techniques and statistical methods from structured and un-structured data.

Learning outcomes

Attributes Developed
Analyse the efficiency and productivity of business firms by using data analytics techniques
Apply state-of-the-art techniques to build analytical and predictive models
Use Big Data analytics and a methodical approach to analyse data
Communicate and provide results to the management and stake-holders for decision making and implementation

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Module content

The module will focus on a selected set of areas in Big Data. Please find below an indicative set of topics:


Big Data (Hadoop/MapReduce)
Unstructured data – social media information
Database design and ER
Statistical methods for data analysis
Data visualisation
SQL, OLAP

Methods of Teaching / Learning


The learning and teaching strategy is designed to:

The teaching and learning strategy is designed to: cultivate an understanding of the complex process of big data analysis and understanding of methodical approach of data analysis.

This module is delivered as a programme of lectures and lab classes. Web-based learning support and electronic resources will be provided.

The learning and teaching methods include:


2 hour lecture per week x 11 weeks

2 hour labs per week  x 11 weeks

Assessment Strategy


The assessment strategy is designed to provide students with the opportunity to demonstrate

In order to achieve the threshold standard for the award of credits for this module, the student must meet the following criteria (related to the learning outcomes):


analysis and manipulate data;

improve the quality of Big Data;

present knowledge in a suitable format for the target audience;

demonstrate evidence of background reading and research of the academic and practitioner literature relevant to big data and data analysis.


Thus, the summative assessment for this module consists of:


Two hours unseen exam paper

One piece of project based coursework


Formative assessment and feedback

Formative assessment will be provided during computer laboratory sessions.

Reading list

Reading list for DATA ANALYTICS : http://aspire.surrey.ac.uk/modules/manm301

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2017/8 academic year.