APPLIED ANALYTICS IN BUSINESS - 2022/3
Module code: MAN3201
In light of the Covid-19 pandemic the University has revised its courses to incorporate the ‘Hybrid Learning Experience’ in a departure from previous academic years and previously published information. The University has changed the delivery (and in some cases the content) of its programmes. Further information on the general principles of hybrid learning can be found at: Hybrid learning experience | University of Surrey.
We have updated key module information regarding the pattern of assessment and overall student workload to inform student module choices. We are currently working on bringing remaining published information up to date to reflect current practice in time for the start of the academic year 2021/22.
This means that some information within the programme and module catalogue will be subject to change. Current students are invited to contact their Programme Leader or Academic Hive with any questions relating to the information available.
The module provides advanced topics in business analytics. It uses real life cases to explore topics and how Business Analytics methods (such as Data Mining, Machine Learning and Pattern Recognition) used in business using real-life cases in context (for example, healthcare analytics and social media analytics, marketing etc.). Students will gain knowledge on how to think about developing an analytics project and what the possible challenges are in a real-life context with less depth in the theoretical foundations of the techniques.
Surrey Business School
HILL Andrew (SBS)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 6
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 117
Lecture Hours: 22
Laboratory Hours: 11
Prerequisites / Co-requisites
1. Introduction to the course
2. Types of business problems and their common data solutions
3. Recent case studies in applying Business Analytics
4. The decision-making process supported by Business Analytics
5. Using data to identify questions
6. Using data to solve problems
7. Constructing useful data dashboards
8. Tailoring the message for business leaders
9. Future trends in Business Analytics
10. Putting it all together
11. Module review
|Assessment type||Unit of assessment||Weighting|
|Project (Group/Individual/Dissertation)||Applied Analytics Project||50|
|Examination||Applied Analytics Examination||50|
An alternative to the group project is a defined individual project using a given dataset and problem description (1000 words for the individual).
The assessment strategy is designed to provide students with the opportunity to demonstrate:
· Knowledge of practical business analytics processes
· Appreciation of the need to deliver fit-for-purpose solutions that aid decision-making
· Ability to apply key techniques that enable robust insights, including not only analytical but process methods
Thus, the summative assessment for this module consists of:
· Applied Analytics Project (50%)
3000 words group coursework to be submitted at the end of the semester
· Applied Analytics Examination (50%)
2-hour closed book examination taken at the end of the semester.
· Online assessments tied to lab sessions Students will be given the opportunity to receive formative assessment and feedback relevant to the assignments. Formative assessment will also be relevant to the closed book exam may be provided on SurreyLearn discussion forums.
· Group feedback on lab session results – common errors, examples of good practice.
· Individual feedback after online assessment covering key concepts.
- · Introduce students to the practical methods of data analytics in business that turn theory into applied decision support
- · Teach students how to work with real, messy data and real, messy problems.
- · How to use analytics techniques to improve Return on Investment
|001||Students will learn how analytics is applied in everyday business practice||KP|
|002||Students will learn the key methods and processes for delivering fit- for-purpose analytics||KPT|
|003||Students will learn to critically appraise business questions and which analytical techniques are most suited to deliver insights||CPT|
|004||Students will learn how to manage and analyse real, messy data||CKPT|
|005||Students will apply state-of-the-art techniques and tools to novel business problems||CKPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
encourage a critical understanding of the importance of the practical application of Business Analytics, where proponents will have to deal with messy data sources, unclear business questions and integrate them all to develop useful insights for decision-making. The course aims are to introduce students to the data-driven decision-making process and to teach them valuable practical skills to solve common data problems.
The course will be very practical, encouraging students to get hands-on with real-life business questions and messy data, and teach them how to solve business problems with robust processes and methods that can handle diverse data sources and business leaders who don¿t know what they want!
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: MAN3201
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2022/3 academic year.