SHE Level 1
SCQF Credit Points 15.00
ECTS Credit Points 7.50
Module Code M1N226967
Module Leader Mutsa Chinyamakobvu
School School of Health and Life Sciences
Subject SHLS - School Office
  • B (January start)
  • C (May start)
  • A (September start)

Summary of Content

The Data and Decisions modules engage 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 major decisions in the world. Students will learn how to contextualise data, use data to effectively construct arguments and communicate insights, model real world phenomena and uncertain outcomes, and become better decision-makers. In this module, students will engage with hypothesis testing and linear programming.


Unit 3: Data-based decision making -360? Revisiting inferential statistics ? Hypothesis formulation ? Hypothesis testing ? Simple and multiple linear regression Unit 4: Linear programming -360? Decision making ? Linear programming

Learning Outcomes

On succesful completion of this module, students should be able to:1). Design basic experimental and non-experimental studies2). Choose appropriate sample sizes and calculate confidence intervals3). Develop an empirically verifiable hypothesis and conduct a hypothesis test4). Understand and be able to identify independent and dependent variables5). Understand and explain correlation coefficients6). Understand and distinguish between correlation and causation, recognize real world examples of confusing these two concepts.7). Understand key elements of linear programming (including equations, inequalities and optimisation)8). Use Excel to solve linear programming problems

Teaching / Learning Strategy

This module 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 deepen their understanding of these concepts through facilitated classroom sessions. Students will also independently study content published on the online learning environment. Students will further deepen their understanding of all module content through peer learning. Students will receive further support during weekly Office Hours sessions with facilitators.

Indicative Reading

-360 Carter, M., Price C., Rabadi, G. (2018). Operations Research: A Practical Introduction. 2nd ed. Chapman and Hall. Doane, D., and Seward, L. (2018). Applied Statistics in Business and Economics. 6th ed. McGraw-Hill Education. Fox, J. (2015). Applied Regression Analysis and Generalized Linear Models. 3rd ed. SAGE Publications. -360 Knaflic, C. (2015). Storytelling with Data: A Data Visualisation Guide for Business Professionals. 1st ed. Wiley. Swift, L., and Piff, S. (2014). Quantitative methods for business, management and finance. 4th ed. Palgrave Macmillan. -360 Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing. 3rd ed. Academic Press. Online resources: Kaggle <> World DataBank <> Khan Academy <>

Transferrable Skills

Quantitative reasoning Uncertainty and modelling the real world Empirical research Data-based decision making Quantitative problem-solving approach Communicating for impact Organising for effective communication Storytelling and presentation Critical thinking Authentic inquiry Research Analysis Synthesis

Module Structure

Activity Total Hours
Independent Learning (FT) 76.00
Assessment (FT) 50.00
Seminars (FT) 24.00

Assessment Methods

Component Duration Weighting Threshold Description
Course Work 01 n/a 30.00 35% Weekly assessments
Course Work 03 n/a 30.00 35% Weekly assessments
Course Work 02 n/a 20.00 35% Peer presentations
Course Work 04 n/a 20.00 35% Linear programming questions and presentation