Module BUSI4AT10: Business Analytics

Department: Management and Marketing

BUSI4AT10: Business Analytics

Prerequisites

• None

Corequisites

• None

Excluded Combination of Modules

• None

Aims

• To equip students with an in-depth understanding of key principles of the decision making process in business and management.
• To develop students; skills in undertaking data analytics (descriptive, predictive and presecriptive).
• To provide real experience in analysing real-world problems.
• To enable students to be able to inspire business actions and influence business leaders using powerful data visualisations and storytelling.

Content

• Descriptive techniques e.g. data visualisation, data analysis, and descriptive statistics.
• Predictive techniques e.g. regressions, classifications problems, clustering.
• Prescriptive techniques e.g. linear optimisation.

Learning Outcomes

• By the end of the module students should:
• Understand the role that data plays in organisations and the technical infrastructure, governance and data management policies, practices and culture that supports ethical use of data.
• Have in-depth knowledge of a range of descriptive and predictive and prescriptive business-analytics techniques and be able to apply them critically to management problems.
• Have an understanding of the applicability and limitations of these descriptive and predictive and prescriptive business-analytics techniques.
• Grasp the principles of data storytelling and how to use narratives to present data insights effectively.

• By the end of the module students should be able to:
• Confidently use appropriate computer software to manipulate and anlayse data.
• Formulate a data science project / problem from business problem or context.
• Be able to use data, data visualisations and data story-telling to create compelling narratives for driving evidence-based business decisions.
• Implement predictive and prescriptive business analytics models using appropriate software packages.
• Interpret the results of predictive and prescriptive business analytics models and their relevance for companies.

• Effective verbal communication
• Planning, organising and time-management
• Problem solving and analysis
• Interpreting and using data
• Making effective use of analytical software
• Data visualisation and storytelling

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

• The module is taught using a blended approach with online asynchronous theoretical content and practice-based backed up by synchronous face to face lectures and practical workshops.
• Online asynchronous lecture sessions will cover both theoretical content and practice-based demonstrations using computer software. During these sessions, students will gain foundational knowledge in descriptive, predictive abd prescriptive analytics and understand the role of data in organistions.
• Lectures will primarily include a brief re-cap of the online asynchronous sessions and have structured time for disucssion (e.g. questions and answers and mini-exercises on case studies).
• Classroom-based practical workshops will involve students working in groups on case studies. These workshops will be focused on performing the data analysis, building and executing the analytical models and making inferences based upon the results. Students are expected to have engaged with online asynchronous and face to face lectures before attending the workshops.
• The summative assessment is split into three components, each assessing the module's descriptive, predictive and prescriptive aspects. These assessments emphasize the practical nature of business analytics and data science, requiring students to undertake their analysis independently with the help of computer software.
• The formative assessments consists of classroom-based exercises involving individual and group analytical work on a business problem.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

For the descriptive analytics assessment, the student will receive individualised feedback on the suitability of their chosen business problem and data set. For both the predictive and prescriptive analytics components, students will work on a personalised data set and receive individualise feedback.

■ 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