module specification

Data analysis ranks as a top business priority that drives performance improvements across industries. Advances in technology and software have accelerated data generation, and employers now actively seek graduates with strong data analysis and software skills. Data Analysis for Business Decision Making meets these market demands by providing you with essential quantitative and statistical methods and by developing your interpretation and reporting skills.

In this module, you will learn data-based decision making and performance measurement, while gaining hands-on experience using Excel to transform raw data into meaningful information. You will also explore forecasting, target setting and project management. Our primary objective is to provide you with an understanding of the fundamentals of statistical methods for business decision making. This foundation will prepare you for modules at levels 5 and 6, such as Research Methods or The Practice of Consultancy, Project Management, and the Dissertation or the Consultancy Project, that further develop and evaluate the quantitative aspects of business management. 
  
Overall, this module develops your analytical and communication skills, enabling you to understand business information and apply effective problem-solving techniques to support business decision making and enhance performance measurement.

In summary, the module aims to equip you with:

1. An appreciation for the value of quantitative methods that underpin effective business analysis;
2. The skills to utilise Excel to transform raw data into actionable insights that inform decision making;
3. Experience in applying structured quantitative techniques to produce reliable outputs; and
4. An understanding of basic statistics and the ability to interpret and communicate analytical findings.

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. 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, LO3)

Communication skills, in particular the ability to compile output and interpretation, are also developed.
(LO4)

Learning and teaching are structured around three hours of class contact time per week. The weekly sessions take the form of a 2-hour lecture and a 1-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 with solutions, Excel worksheets, videos and self-tests 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 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 actively encouraged to discuss their learning experiences with peers and tutors during seminars. By engaging with the weekly feedforward opportunities, students can continually develop their skills, refine their work and enhance their learning effectiveness. Students will be working on individual Coursework – undertaking quantitative analysis based on a set of tasks and business objectives (up to 1500 words). The students are provided 1-2-1 support and feedback on their draft works.

Learning Trough Assessment

The module is assessed through one individual coursework, designed to test all four learning outcomes. Students complete six short tasks addressing business objectives, applying appropriate methods to quantitative problems drawn from business and management contexts, with interpretations totalling up to 1000 words.

The summative coursework contributes 100% to the overall module mark and is due at the end of week 12. The coursework brief and criteria are provided on WebLearn and discussed in class to ensure clarity of requirements. Workshops embedded in seminars in specific weeks allow students to share draft work for developmental feedback, with emphasis on interpretation and effective communication of findings. Results are subjects to internal moderation and results are published on Evision in February."

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.  Apply structured quantitative techniques to generate reliable output for business analysis;

LO4.      Demonstrate an understanding of basic statistics and modelling techniques and be able to interpret and communicate findings.

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