## DATA AND DECISIONS A

 SHE Level 1 SCQF Credit Points 15.00 ECTS Credit Points 7.50 Module Code M1N226963 Module Leader Mutsa Chinyamakobvu School School of Health and Life Sciences Subject SHLS - School Office Trimesters B (January start) A (September start) C (May 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 data contextualisation and visualisation.

### Syllabus

Unit 1: Contextualizing Data -360? Introduction to logical reasoning -360? Guesstimation and market sizing -360? Logical fallacies -360? Problem-solving under pressure Unit 2: Data Visualisation ? Data cleaning -360? Highlighting information -360? Advanced visualisations -360? Stakeholder analysis

### Learning Outcomes

On succesful completion of this module, students should be able to:1). Use logic to solve simple problem sets2). Use guesstimation to reach approximate answers 3). Clean data sets4). Create different types of charts/graphs5). Apply stakeholder analysis from multiple perspectives

### 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 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.

-360? Doane, D., and Seward, L. (2018). Applied Statistics in Business and Economics. 6th ed. McGraw-Hill Education. ? Knaflic, C. (2015). Storytelling with Data: A Data Visualisation Guide for Business Professionals. 1st ed. Wiley. ? Osborne, J. (2012). Best Practice in Data Cleaning: A Complete Guide to Everything You Need to Do Before and After Collecting Your Data. 1st ed. SAGE Publications. ? Wonnacott, T., and Wonnacott, R. (1990). Introductory Statistics for Business Economics. John Wiley & Sons. ? Zelazny, G. (2001). Say it With Charts: The Executive's Guide to Visual Communication. 4th ed. McGraw-Hill. ? Online resources: -10? Kaggle:<http://www.kaggle.com/>www.kaggle.com <http://www.kaggle.com/><http://www.kaggle.com/> ? World DataBank<http://data.worldbank.org/>data.worldbank.org <http://data.worldbank.org/><http://data.worldbank.org/> ? Khan Academy<https://www.khanacademy.org/math><https://www.khanacademy.org/math><https://www.khanacademy.org/math>

### 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
Seminars (FT) 24.00
Independent Learning (FT) 76.00
Assessment (FT) 50.00

### Assessment Methods

Component Duration Weighting Threshold Description
CW2 Course Work 02 n/a 35.00 35% Weekly assessments
CW3 Course Work 03 n/a 20.00 35% Database anaylsis and report
TST Exam (Dept) 01 n/a 20.00 35% In class test
CW1 Course Work 01 n/a 25.00 35% Weekly assessments