module specification

Data analysis is a top business priority. It drives the opportunity for performance improvement and, with advances in technology and software, data are generated at an ever increasing rate. As such, it is not surprising business data analysis and software skills are among the top graduate skills sought by employers today. Understanding and Managing Data, responds to these market demands by providing the underpinning skills required to make effective use of quantitative and statistical analyses and develops students’ interpretation and reporting skills.

The module introduces data-based decision making and performance measurement and provides students with the practical experience of using Excel to transform data into meaningful information. It further introduces students to forecasting, target setting and project management. As such, it provides students with an understanding of the fundamentals of statistical methods for business decision making. In doing so, it provides the skills and knowledge required for levels 5 and 6 modules, including the dissertation and consultancy project, that develop and evaluate the quantitative aspects of business management. 
  
Overall, this module develops the analytical and communication skills relevant to understanding business information, with an emphasis on problem-solving techniques in the context of business management, decision making and performance measurement.

Standard University level 4 entry requirements

This module focuses on the processes by which data are transformed into information to provide insights and facilitate informed decision making.

Primary and secondary data sources are discussed and students are introduced to Excel spreadsheet facilities for data presentation and the descriptive analysis of ungrouped and grouped cross-sectional data. To examine relationships and future values, correlation, regression and time series analyses are introduced. Finally, the module covers critical path analysis to facilitate project planning and management, and introduces probability and decision analysis, investment appraisal and breakeven. Whilst the syllabus will be delivered using a combination of pen and paper exercises and Excel activities to familiarise students with the techniques, the emphasis is on interpreting output. Students will learn to describe consumer characteristics or to recognise trends, but also to understand how product and service decisions are made by companies, for example. LO1,LO2

Communication skills, in particular the ability to compile output and interpretation, are also developed. LO3

Learning and teaching are structured around three hours of class contact time per week. The weekly sessions take the form of a 1-hour lecture and a 2-hour interactive seminar in a computer lab.

The lectures introduce students to the theories and methods and adopt a problem-focused approach to learning. The interactive seminars are designed to facilitate learning through individual and collaborative practical activities, interpretation and reporting exercises, discussions and presentations, and feedback. 

Students will receive module material, including weekly session notes, presentation slides, exercises and computer activities via WebLearn. The computer-based tasks are blended into the weekly activities: students will be using Microsoft Excel to enter and analyse data and Microsoft Word to report the findings. A range of tasks are completed in class, though others are completed outside formal contact hours.

In addition to pen and paper exercises and Excel-, or Word-based activities, tasks take a variety of forms, including online research, working with statistical applets and participating in interactive activities.  It is expected that for every 3-hours spent in lectures and seminars, students spend a further 7 hours a week on independent study. A total of 30 hours is allocated for assessment preparation. 

Reflective learning is incorporated into the module and students are advised to keep a journal of experiences and personal development to analyse and reflect on the effectiveness of their learning.

 On successful completion of this module, students will be able to:

LO1.  Appreciate the role of quantitative methods for business analysis, planning and performance measurement;

LO2.   Use Excel facilities to transform data into information to facilitate problem solving and business decision making; 

LO3.  Demonstrate an understanding of basic statistics and modelling techniques and be able to interpret and communicate findings.

The assessment is designed to provide feedback opportunities and enable students to demonstrate the learning outcomes have been achieved. On MN4063 assessment takes the form of an individually-completed coursework with a reflective component embedded.

The module has one summative assessment consisting of two parts. The first part requires students to complete, and provide a 400-word commentary on, a set of short tasks. These tasks can cover a range of concepts, methods and models across a variety of business and management settings.

The second part is course-specific: the context, data set and decision problem will reflect the students' subject area. This part requires students to select appropriate data analysis and / or modelling techniques to analyse a specific business problem. It involves the preparation of a 600-word report to interpret the findings and to communicate recommendations to decision makers.

Both parts require the use of Microsoft Excel to summarise and analyse the data and Microsoft Word to report the findings and recommendations.

A short, reflective commentary is required as part of the assessment.

The coursework assesses the three learning outcomes, is due at the end of week 13 and contributes 100% to the overall module mark.

Coursework briefs and assessment criteria are uploaded to WebLearn and discussed in class to ensure requirements, and the basis on which academic judgements are made, are clear. In the weeks where coursework workshops are incorporated into the regular sessions, students are encouraged to show draft work for developmental feedback. To improve the quality of work, these sessions focus on interpretation and / or business report writing and developing good academic practice, for example in terms of consistently crediting sources.

Results are finalised following internal moderation and are published on E-vision at the end of the academic year.

Textbooks:

Oakshott, L. (2020) Essential Quantitative Methods for Business, Management and Finance. 7th edition. Red Globe Press

Evans, J.R. (2016) Business Analytics, Global Edition.  2nd edition. Pearson

Wisniewski, M. and Shafti, F. (2019) Quantitative Analysis for Decision Makers. 7th edition, Blackwells

HBR (2018) Guide to Data Analytics Basics for Managers. HBR Guide Series. Harvard Business Review Press 

Curwin, J. et al (2013) Quantitative Methods for Business Decisions. 7th edition. Cengage

Websites:

MN4063 WebLearn module

Bloomberg – European Edition, https://www.bloomberg.com/europe

Reuters. Edition: United Kingdom, https://uk.reuters.com/

Rice Virtual Lab in Statistics (simulation applets, interactive multimedia course, cases and more): http://onlinestatbook.com/rvls.html

Other:

Google’s Chief Economist, Hal Varian, on the Importance of Statistics and Data: https://www.mckinsey.com/industries/high-tech/our-insights/hal-varian-on-how-the-web-challenges-managers

Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/),
Project Leader: David M. Lane, Rice University. Free statistics Textbook (pdf version): http://onlinestatbook.com/Online_Statistics_Education.pdf

London Metropolitan University Harvard Style Referencing guide: http://student.londonmet.ac.uk/library/subject-guides-and-research-support/referencing-and-copyright/referencing/