Module BUSI4X915: Data Science for Business

Department: Management and Marketing

BUSI4X915: Data Science for Business

Prerequisites

• None.

Corequisites

• None.

Excluded Combination of Modules

• None.

Aims

• To provide an appreciation of how analytics can address problems and provide value to business
• To provide understanding of descriptive, predictive and prescriptive business-analytics techniques.
• To provide knowledge of, and ability to apply, a range of business-analytics techniques.
• To explore ethical issues with managing data

Content

• Types of data, data collection and data quality checking
• Ethical use of data
• Data structures
• Data visualisation
• Descriptive techniques e.g. data visualisation, data analysis, and descriptive statistics.
• Multivariate Statistics and Hypothesis testing.
• Predictive techniques e.g. classification and clustering for data segmentation
• Prescriptive techniques e.g. linear optimisation models
• Analytics to improve customer service

Learning Outcomes

• Develop in-depth knowledge of a range of business-analytics techniques and be able to choose the most appropriate ones and apply those critically to business problems.
• Develop critical understanding of the applicability and limitations of business-analytics techniques.
• Develop specialised knowledge of the relevant analytical techniques to plan and manage supply chains

• Upon successful completion of the module, the students should be able to:
• analyse data, and develop data-driven recommendations to improve supply chain and business performance
• interpret the results of business analytics models and their relevance for companies

• Upon successful completion of the module, the students should demonstrate
• Effective written communication skills
• Planning, organising and time-management skills
• Problem solving and analytical skills
• Data presentation, visualisation and interpretation

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• On-site teaching will typically include a mix of taught input, group work and discussion, use of case studies to emphasize real-world applications, and industry-informed sessions. Online learning will be divided into study weeks and will typically include activities facilitated by the teaching team and specially produced resources. Facilitated activities will make use of a range of educational technologies to include digital collaboration spaces and live online sessions.
• Learning resources vary according to the learning outcomes but typically include: video content, directed reading, reflective activities and opportunities for self-assessment.
• The summative assessment comprises online assessments and a written assignment which will test students’ theoretical understanding, their knowledge of relevant techniques and types of analysis and their ability to apply these to a particular, real-world context.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

The formative assessment serves to encourage students to study regularly and to monitor their learning progress. Students will undertake a series of activities aligned to the module content, receiving ongoing feedback on analytical tools and how those can be applied for addressing business problems. These will typically include classroom and online exercises involving individual and group analyses and presentation on application of analytical tools and techniques relevant to the learning outcomes of the module. Tutors provide written feedback on formative work through and are available for individual consultation as necessary (usually by email and video call).

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University