DATA ANALYTICS - 2021/2

Module code: MANM301

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 provider

Surrey Business School

Module Leader

GARN Wolfgang (SBS)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

JACs code: G300

Module cap (Maximum number of students): N/A

Overall student workload

Independent Learning Hours: 95

Lecture Hours: 22

Laboratory Hours: 20

Guided Learning: 2

Captured Content: 11

Module Availability

Semester 1

Prerequisites / Co-requisites

None

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


Assessment pattern

Assessment type Unit of assessment Weighting
Coursework COURSEWORK A 70
Coursework COURSEWORK B 30

Alternative Assessment

Not applicable

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.

Module aims

  • Explore data analytics tools to extract knowledge from datasets
  • Analyse, manipulate, and visualise data using state-of-the-art techniques
  • Explore data mining techniques to extract knowledge from datasets

Learning outcomes

Attributes Developed
001 Demonstrate the knowledge discovery process KP
002 Analyse and visualise data using a methodical analytical approach KP
003 Familiarity to be able to apply important data mining algorithms and techniques CPT
004 Apply state-of-the-art methods and tools to build classification and predictive models CPT
005 Demonstrate the ability to communicate and provide resulting information to the management for decision making KCPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

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


Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: MANM301

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Business Analytics MSc 1 Compulsory A weighted aggregate mark of 50% is required to pass the module
FinTech and Policy MSc 1 Optional A weighted aggregate mark of 50% is required to pass the module

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 2021/2 academic year.