- UCAS course code
- UCAS institution code
BSc Management (Marketing) with Industrial/Professional Experience
Year of entry: 2023
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Course unit details:
Business Data Analytics
|Unit level||Level 2|
|Teaching period(s)||Semester 1|
|Available as a free choice unit?||No|
The course covers a variety of data analytics techniques, including data management and preparation, data preliminary analysis and preprocessing, feature selection and engineering, predictive modelling, clustering, ensemble learning, association analysis, etc.
|Unit title||Unit code||Requirement type||Description|
|Quantitative Methods for Business and Management||BMAN10960||Pre-Requisite||Compulsory|
|Fundamentals of Data Analytics||BMAN11060||Pre-Requisite||Compulsory|
This course requires analytical thinking, the use and interpretation of mathematical & statistical concepts, as well as rapid familiarization with a range of specialist software tools. As such, students are expected to bring basic competency and confidence in all the above three areas, including a willingness for extensive independent study in line with the requirements for a 20-credit course unit.
For students progressing from BMAN10960 Quantitative Methods for Business & Management, it is strongly suggested that a mark of 60% or more should have been achieved.
To provide students with an understanding of data analytics for business and management.
To help develop skills in the use of industry-leading software tools, mainly SAS packages.
At the end of the course students should be able to:
• Understand the fundamentals of data analytics and its applications to real life business problems,
• Understand a variety of data analytics techniques, including data pre-processing, feature selection, predictive modelling, unsupervised learning, etc., and,
• Demonstrate the ability to use specialised software tools to analyse large sets of data in different business contexts.
• Introduction to business data analytics
• Data management and preparation,
• Data preliminary analysis,
• Data preprocessing,
• Predictive modelling
• Clustering analysis
• Ensemble learning
• Association analysis
• Text analytics
• Visual analytics and big data analytics
Teaching and learning methods
Two-hour lecture and two-hour lab per week (see detailed schedule below) for 11 weeks, directed reading and computer based support.
100% individually assessed coursework
• Informal advice and discussion during lectures or seminars.
• Responses to student emails and questions from a member of staff including feedback provided to a group via an online discussion forum.
• Written and/or verbal comments on assessed or non-assessed work.
• Generic feedback posted on Blackboard regarding overall examination performance.
In addition to the central unit evaluation questionnaire, student are encouraged to give feedback through emails and conversations at anytime, and questionnaire near the end of the semester
Galit Shmueli, et al.; Data Mining for Business Analytics: Concepts, Techniques, and Applications - in R (e-book available from the university library) or in Python, John Wiley & Sons, 2018.
Max Bramer, Principles of Data Mining, Springer, 2013.
Other reading materials will be shared via Blackboard.
|Scheduled activity hours|
|Practical classes & workshops||22|
|Independent study hours|
|Yu-Wang Chen||Unit coordinator|
Pre-requisites: BMAN11060 Fundamentals of Data Analytics for BSc ITMB, and BMAN10960 Quants for Business and Management (except BSc Mathematics and Management & Maths Stats & OR.) or equivalent for other BSc programmes
Dependent courses: None
- BSc Information Technology Management for Business
- BSc Management and Management (Specialisms),
- BSc International Management with American Business Studies,
- BSc International Management,
- BSc Mathematics and Management, Maths, Stats & OR
For Academic Year 2023/24
Updated: March 2023
Approved by: March UG Committee