Module code: MANM530

Module Overview

The primary purpose of this module is to teach students how to structure a business data analysis from end to end, from business question to communication of options and insights. Such a skill is fundamental to all Business Analytics, and will help students structure their analyses throughout the rest of the programme.

Students will learn how to apply data analysis within a general decision-making framework by practical first-hand experience, taking a business problem (and associated dataset) from start to finish, with each week teaching them how to progress through one step of the analytical process. Along the way they will learn key concepts that determine the quality of a data analysis, including how to generate a specific business question, how to generate reliable, clean data, how to differentiate signal from noise, such that they may identify useful business insights. At the end of the module, they will take their analyses and learn how to communicate data insights through visualisation and dashboarding.

Module provider

Surrey Business School

Module Leader

GARN Wolfgang (SBS)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

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

Overall student workload

Independent Learning Hours: 73

Lecture Hours: 22

Laboratory Hours: 22

Guided Learning: 11

Captured Content: 22

Module Availability

Semester 1

Prerequisites / Co-requisites


Module content

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

  • How to structure analysis projects using analytical and decision-making processes.

  • Analytics tools

  • Spreadsheets, coding and Business Intelligence (e.g. Excel, R and Tableau).

  • Foundational data analysis skills (e.g. manipulating, cleaning, joining and exploring data)

  • Foundational statistics and statistical concepts (e.g. uncertainty, variance, pattern identification).

  • Data dashboards.

  • Data visualisation.

  • What-if scenario analysis.

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework Coursework 50
Examination Exam (2 hours) 50

Alternative Assessment

Not applicable

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate all learning outcomes Thus, the summative assessment for this module consists of:

  • One piece of coursework - tests learning outcomes 1-5.

  • One exam - tests learning outcomes 2-5

The coursework allows us to test the student's ability to follow the analytical process from end-to-end, to take a real-world business problem and produce realistic, robust and understandable options and insights. This combines the technical skills above with the softer skills of process and communication that transform raw numbers into actionable, decision-making insights.

The exam enables us to test the student's technical skills through a series of mathematical and data questions (e.g. testing the student's ability to extract descriptive statistics and work out key metrics such as break-even point). These technical skills are arguably the primary objective of the programme and of employers, and so it is necessary to specifically test the student's mathematical capability within an exam setting.

Formative assessment

Students will be given real data to analyse in the computer labs. Additional tasks at the end of the lab sheets will form the basis of their independent study. The solutions provided to the lab sheets (including the additional tasks) will form the formative assessments, as students can assess their own work and can also use the labs and office hours for advice and feedback.


As discussed in the formative assessment section, students are expected to complete lab sheets including the additional independent study tasks and have various opportunities within lab sessions and in office hours to seek advice and feedback on their work. One of the final face-to-face sessions will be reserved to discuss assessment strategies and get feedback on the module as a whole.

Module aims

  • Teach students how to structure Business Analytics problems through practical application of general decision-making processes
  • Analyse, manipulate and visualize data using state-of-the-art techniques
  • Demonstrate key analytical concepts such as data cleansing, variance and uncertainty.

Learning outcomes

Attributes Developed
001 Apply the analytical process to a real-world business problem CKP
002 Extract value from data using a methodical analytical approach KP
003 Familiarity with common spreadsheet, coding and Business Intelligence tools CPT
004 Knowledge of key analytical concepts (e.g. uncertainty, variance) C
005 Communicate insights effectively to decision-makers using data visualisation and data dashboards KPT

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 Analytics process to provide actionable data insights to businesses. Students will be taught using business datasets, with each week allowing students to learn how to apply the next stage of the analytical process. 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:

  • Synthesising theories of relevant Business Analytics areas.

  • Hands-on-approach by evaluating several software tools relevant to Business Analytics;

  • Demonstrating evidence of background reading and research of the academic and practitioner literature relevant to Business Analytics.

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
Upon accessing the reading list, please search for the module using the module code: MANM530

Other information

Employability: The module is highly applied and directly teaches key skills needed for Business Analytics graduates going into analytical careers.

Digital capabilities: Students develop digital capabilities specifically by using problem-solving and analytical skills to use data to make decisions how to transform data into useful insights by understanding how data gets turned into knowledge and eventually into insight.

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 Compulsory 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 2024/5 academic year.