DATA AND DECISIONS 102

SHE Level 1
SCQF Credit Points 15.00
ECTS Credit Points 7.50
Module Code M1N025780
Module Leader Yetunde Odunsi
School Glasgow School for Business and Society
Subject GSBS - School Office
Trimesters
  • B (January start)
  • A (September start)
  • C (May start)

Summary of Content

The Data and Decisions course is a journey about seeking and analyzing different types of data to make informed decisions when addressing real world problems. All first year students embark on this journey together as they gain a solid foundation and exposure to how data shapes most major decisions in the world. It is an engaging, motivating and practical way for students to develop quantitative skills that are critical for any high-performing employee or leader of any industry. Data and Decisions students learn to contextualize numbers in their tangible meaning, effectively make arguments and communicate powerful insights by representing data, model real world phenomena and uncertain outcomes, and become better decision-makers by using this extensive toolkit the course helps them develop. In this module we cover critically and logically working through problem solving, as well as data preparation, analysis and visualisation.

Syllabus

Unit 3: Descriptive and inferential statistics, probability and sampling Hypothesis testing Simple and multiple linear regression Unit 4: Decision making and linear equations Linear programming Sensitivity analysis

Learning Outcomes

On successful completion of this module students should be able to:1). Design studies, choose sample sizes and calculate confidence intervals2). Define a hypothesis and conduct a hypothesis test3). Understand independent and dependent variables4). Understand correlation coefficients5). Understand correlation and causation6). Understand linear programming (equations, inequalities and optimisation)7). Apply 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 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 and formative assessments. 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 WHO Unicef Yelp DBpedia FiveThirtyEight

Transferrable Skills

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

Module Structure

Activity Total Hours
Lectures (FT) 15.00
Seminars (FT) 15.00
Assessment (FT) 30.00
Independent Learning (FT) 90.00

Assessment Methods

Component Duration Weighting Threshold Description
Course Work 01 n/a 26.00 n/a Unit 3 weekly assignments
Exam (Dept) 01 n/a 18.00 n/a Unit 3 final assignment - summative in-class test
Course Work 03 n/a 36.00 n/a Unit 4 final assignment - summative exercise
Course Work 02 n/a 20.00 n/a Unit 4 weekly assignments