BEM2031 Introduction to Business Analytics

Module Description

This module will explore the role of information and analytics in supporting the development of strategies, and the practical techniques managers can use to design effective information flows. Information is the lifeblood of business. Companies that manage information effectively can improve efficiency,be more responsive to market opportunities,achieve competitive advantage and operate more sustainably. As businesses drive towards sustainable strategies,they are looking for better information to guide decisions. A critical next step is to build information systems and data analytics capabilities that will turn raw data into actionable insights.  This will enable companies to identify which actions more effectively are achieving their goals, detect risk or opportunity early, evaluate possible outcomes,allocate resources to achieve greatest returns, and measure the true impact of products. Internationalisation: the module will draw on recent scholarship in the areas of data and analytics published by researchers internationally (the UK, Europe, the United States) and case studies based on a variety of national contexts.

Employability: the module will offer an opportunity to acquire knowledge and develop analytical skills for those pursuing careers in planning and analytics.

Module Aims

The module aims to enhance your understanding of the application of data in organisations, and to start the process of building your capability in designing, structuring, and analysing data.

Specifically, we will consider:

•    How businesses use data to build, understand and report on their activities

•    How to apply current concepts in data and analytics to real examples

• The use of ‘Design Thinking’ to create information management systems

• The initial tools for analysing numbers and text

ILO: Module-specific skills

•   Critically  evaluate  current  approaches  used  for  collection,  management, communication and analysis of commercial, operational and sustainability data, and how this data is used to support decision-making.

•   Apply Design Thinking techniques to the analysis of a specific business challenge and use these to identify required information flows.

•    Use data visualisation techniques to share original content and insight with a general management audience .

•    Demonstrate  familiarity  with  analytical  tools  available for  the  analysis  of numerical  and  textual  data  and  use  these  to  find,  derive  and  evaluate information.

•    Discuss current developments and thinking in the information management industry,  specifically  around  big  data  management,  analytics,  cloud,  and visualisation techniques.

ILO: Discipline-specific skills

•    Describe key terms and concepts in data and information management and be able to apply these to a typical business situation.

ILO: Personal and key skills

• Critical and reflective thinking.

•    Demonstrate effective independent study and research skills.

General Support

•    Business School welfare team:[email protected]

•   Accessibility (e.g. ILPs):www.exeter.ac.uk/wellbeing/accessability/support/

•    Exams and ILPs:https://www.exeter.ac.uk/students/wellbeing/resources-and- services/exams-and-ilps/

•    Mitigation (extensions and deferrals):https://www.exeter.ac.uk/students/infopoints/yourinfopointservices/mitigation/

Module resources

•    Download and install R and RStudio:RStudio Desktop - Posit

•    Start learning withPosit Cloud PrimersandR cheatsheets

•    Module textbook:Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking Provost, Foster ; Fawcett, Tom (2013)

Hard copies available at Forum Library, or available online at:Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking - University of Exeter

• R for Data Scienceis an excellent free book by Wickham and Grolemund.

•      For GGPlot2 refer to theGGPlot2 bookby Wickham.

•      We will useTidy Text Mining with Rby Silge and Robinson.

•      AndInterpretable Machine Learningby Chistoph Molnar.

•     You can find more information about R Markdown and its options on the websiteR Markdown (rstudio.com)or the bookR Markdown: The Definitive Guide (bookdown.org).

Course overview 2024

Assessments

There are two assessments for this module:

(a) A formative assessment is intended to develop and practice analytic skills. It is an assignment worth 30% of your final grade. Outline for Critique Length:

300-500 words

Assignment Due: 16 February 2024 Time: 15:00 hours

(b) A summative assessment in the form of a single final project is worth 70% of your final grade. Analytics Report Critique Word Count: 3,000 words

Final Project Due: 28 March 2024 Time: 15:00 hours

(a) The assignment will be very similar to what was done in class but will use different datasets. There will be several sections which will be marked using the scale listed below for reference.

Fully correct answers that complete the task in the expected manner will be given a high distinction of 8/10. For a full 10/10 I have left some room for innovation and personal exploration. Students who go above the expected, integrate a new package, attempt a new plot, try a new analysis, can be rewarded here.

Score            Description

0                   The problem was not attempted.

2                   The problem was attempted but largely incomplete or incorrect.

4                   Concepts are understood, but not well explained in the context of the problem. Calculations yield the wrong answer due to minor or major  errors. Plots are incorrectly generated.

6                   The approach is generally correct. Calculations yield the wrong answer due to minor errors. Plots are roughly correct.

8                   The solution is correct, well-documented, and the writing is clear.

Reproducible code provides a correct step-by-step solution and is easy to follow. Plots are correct, detailed, and clearly explained.

10                  The solutions are exceptional, clear, and creative. The solutions provided innovate and expand on existing knowledge.

(b) For the final project, you will be given a report similar to what may be provided in a business setting along with a dataset.

Your task is to critique the report and provide your own report. You will provide additional  or  corrected  visualizations  and  analyses,  and  recommendations  and conclusions to top management regarding the most prudent course of action based on the data.

The full details are in the separate assignment brief.

Additional Information

Late Submissions:

There are significant penalties for submitting work late.

For coursework:

•     Work submitted up to one hour late will receive a 5% reduction in marks, down to a minimum score of the module pass mark

•     Work submitted between 1 hour and 24 hours late will be capped at the pass mark

•     Work submitted more than 24 hours late will receive a mark of zero

(NOTE: Where an exceptional three-week extension has been granted, work submitted at any point beyond the extended submission deadline will receive a mark of zero. Any students requiring additional time should submit a further application for mitigation within 24 hours of the extended deadline in order to be granted a deferral.)

Please always check you’re submitting the right piece of work to the right place. A Late Submission of Coursework FAQs is also available within theTQA Manual: section 2.11.

Further information: FAQ | Student hubs | University of Exeter

Mitigation:

Mitigation works by giving you extra time to complete your assignment.

Two types of mitigation are possible:

(i) For coursework assignments, you can have an evidence-free extension of 72 hours (3 days). This option is available once per assessment. You can use it up to four

times during the academic year; any further extensions required after this must be applied for through the evidence-based process detailed below.