Surrey University Stag

DATA ANALYTICS - 2023/4

Module code: MANM301

Module Overview

Data Analytics focuses on extracting insights from data. This module introduces the science of examining raw data in order to support businesses and organisations in their decision making. Conveniently, data is structured and stored in relational-databases. In this case, information can be extracted using the Structured Query Language. Often data is unstructured data and additional preparation is required. Methods – including machine learning techniques – are introduced to discover patterns, which can provide businesses with actionable data insights. This module is the foundation for further investigations and a cornerstone of the business analytics 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: 101

Lecture Hours: 18

Laboratory Hours: 18

Guided Learning: 2

Captured Content: 11

Module Availability

Semester 1

Prerequisites / Co-requisites

None

Module content

The module introduces knowledge, techniques, and tools relevant to the area of data analytics. Please find below an indicative set of topics:


  • Structure data – databases and SQL;

  • Business Intelligence tools;

  • Data Science Language R;

  • Knowledge Discovery in databases;

  • Unstructured data;

  • Statistical Learning and Machine Learning;

  • Artificial Intelligence.


Assessment pattern

Assessment type Unit of assessment Weighting
Coursework COURSEWORK A 50
Coursework COURSEWORK B 50

Alternative Assessment

Not applicable

Assessment Strategy

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


  • Value Data Analytics for decision-making;

  • Can gain actionable business insights from data;

  • Demonstrate abilities of presenting solutions in a business context.



The learning and teaching methods include:


  • Synthesising theories of relevant data analytics areas;

  • Hands-on-approach by evaluating several software tools relevant to data analytics;

  • Demonstrating evidence of background reading and research of the academic and practitioner literature relevant to data analytics.



The summative assessment for this module consists of two pieces of coursework.

Formative assessment will be provided during computer laboratory or feedback 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 teaching and learning strategy is designed to: cultivate an understanding of the data analytics process with the view of providing actionable data insights to businesses.

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 lectures and computer laboratory sessions.

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 2023/4 academic year.