SHE Level 1
SCQF Credit Points 30.00
ECTS Credit Points 15.00
Module Code M1N025059
Module Leader Noorie Karimbocus
School Glasgow School for Business and Society
Subject GSBS - School Office
  • A (September start)
  • A (September start)-B (January start)
  • B (January start)
  • B (January start)-C (May start)
  • C (May start)

Summary of Content

The Data and Decisions course engages students with seeking and analysing different types of data to make informed decisions when addressing real world problems. Students will gain a solid foundation and exposure to how data shapes most major decisions in the world. In this module, students will engage with thinking through problems, and data visualisations.


Unit 1: Introduction to logical reasoning Guesstimation and market sizing Logical fallacies Problem-solving under pressure Unit 2: Data cleaning Highlighting information Advanced visualisations Stakeholder analysis Unit 3: Revisiting inferential statistics Hypothesis testing Simple and multiple linear regression Unit 4: Decision making Linear programming

Learning Outcomes

On successful completion of this module, students should be able to:1). Use logic to solve simple problem sets2). Use guesstimation to reach approximate answers3). Clean data sets4). Create different types of charts/graphs5). Apply stakeholder analysis from multiple perspectives6). Design studies, choose sample sizes and calculate confidence intervals7). Define a hypothesis and conduct a hypothesis test8). Understand independent and dependent variables9). Understand correlation coefficients10). Understand correlation and causation11). Understand linear programming (equations, inequalities and optimisation)12). Apply Excel to solve linear programming problems

Teaching / Learning Strategy

This module will provide students with a foundation in thinking through problems logically, and data visualisation and will provide students with a foundation in linear programming and hypothesis testing. A blended learning approach will be used to engage students in the module content. Students will be introduced to concepts and strengthen their knowledge of these concepts through facilitated classroom sessions. Students will engage in individual self-work content, published on the online learning environment. Students will further deepen their understanding of the module through peer learning. Students will receive further support during weekly Office Hours sessions with facilitators.

Indicative Reading

Overarching Resources D.R. Anderson, D. J. Sweeney, and T.A. Williams. Modern Business Statistics with Microsoft Excel. South-Western Publishers, 2004 [ISBN: 978-1305082182] Zelazny, Gene. Say It with Charts. The Executive's Guide to Visual Communication. New York: McGraw Hill, 2001. [ISBN: 978-0071501859] W. Mendenhall, J.E. Reinmuth, R. Beaver, D. Duhan. Statistics for Management and Economics. Belmont: Duxbury Press, 1996. [ISBN: 978-0534932992] J.E. Hanke, A.G. Reitsch. Business Forecasting. Boston: Allyn and Bacon, 1995. [ISBN: 978-0205160051] Supplementary Resources Saunders, Mark, Philip Lewis, and Adrian Thornhill. Research methods for business students. Harlow, Essex, England: Pearson Education Limited, 2014 Data Visualization 101: How to Design Charts and Graphs, PDF Trochim, William M. The Research Methods Knowledge Base, 2nd Edition Microsoft: DAT205x Introduction to Data Analysis Using Excel (edX Online Course) Swift, Louise, and Sally Piff. Quantitative Methods for Business, Management and Finance. Basingstoke: Palgrave Macmillan, 2014. Burton, Glyn, George Carrol, and Stuart Wall. Quantitative Methods for Business and Economics. Harlow: Financial Times/Prentice Hall, 2001. Matthews, Bob, and Liz Ross. Research Methods: A Practical Guide for the Social Sciences. Harlow: Pearson Education, 2010. (Hard copy available on Library Shelf 02_008) Newbold, Paul, William L. Carlson, and Betty M. Thorne. Statistics for Business and Economics: with MyMathLab Global XL. Boston: Pearson Education Ltd., 2013. Flick, Uwe. Designing Quantitative Research. Los Angeles: SAGE Publications, 2007. Data Sources Kaggle World DataBank Atlas

Transferrable Skills

Meta Skill: Quantitative reasoning Core Skills: Uncertainty and modelling the real world, Data-based decision making, Quantitative problem-solving approach Meta Skill: Managing complex tasks Core Skills: Structuring Meta Skill: Critical thinking Core Skills: Authentic inquiry, Research, Analysis, Synthesis

Module Structure

Activity Total Hours
Seminars (FT) 30.00
Independent Learning (FT) 180.00
Lectures (FT) 30.00
Assessment (FT) 60.00

Assessment Methods

Component Duration Weighting Threshold Description
Course Work 03 n/a 27.50 n/a Peer assignment
Course Work 01 n/a 20.00 n/a Individual assignment
Course Work 02 n/a 30.00 n/a Individual assignment
Course Work 04 n/a 22.50 n/a Peer assignment