Data Driven Decision Making Course Descriptor

Course Summary

This course is designed to provide an in-depth focus on data-driven decision-making in organisations. It examines the models, tools, techniques, and theory of data-driven decision making that can improve the quality of business leadership decisions through solution-based case studies. The course will also address the barriers that exist to effective data analysis between analysts and their stakeholders and how to avoid or resolve these; how to raise awareness of and mitigate biases and prejudice in business analytics; how ethics and compliance affect Data and Business Analytics work; and the impact of international regulations (e.g.,General Data Protection Regulation, Data Protection Act 2018).

This course provides learners with an understanding of the theory behind data-driven decision-making. It introduces effective communication techniques using data through scenario-based assignments, catering to diverse stakeholders with varying needs. The course also underscores the significance of leadership in the decision-making process and encourages the enhancement of presentation skills in professional contexts.

Learning Outcomes

On successful completion of the course, learners will be able to:

Knowledge and Understanding

Subject Specific Skills

Transferable and Professional Skills

Teaching and Learning

This is an e-learning course, taught throughout the year.

This course can be offered as a standalone short course.

Teaching and learning strategies for this course will include:

• On-line learning
• On-line discussion groups
• On-line assessment

Course information and supplementary materials will be available on the University’s Virtual Learning Environment (VLE).

Learners are required to attend and participate in all the formal and timetabled sessions for this course. Learners are also expected to manage their self-directed learning and independent study in support of the course.

The course learning and teaching hours will be structured as follows:

• Off-the-job learning and teaching (12 days x 7 hours) = 84 hours
• On-the-job learning (24 days x 7 hours) = 168 hours (e.g. 2 days per week for 12 weeks)
• Private study (4 hours per week) = 48 hours

Total = 300 hours

Workplace assignments (see below) will be completed as part of on-the-job learning.

Assessment

Formative

Learners will be formatively assessed during the course by means of set assignments. These will not count towards the final degree but will provide learners with developmental feedback.

Summative

Assessment will be in two forms:

Feedback

Learners will receive formal feedback in a variety of ways: written (via email or VLE correspondence) and indirectly through online discussion groups. Learners will also attend a formal meeting with their Academic Mentor (and for apprentices, including their Line Manager). These bi- or tri-partite reviews will monitor and evaluate the learner’s progress.

Feedback is provided on summatively assessed assignments and through generic internal examiners’ reports, both of which are posted on the VLE.

Indicative Reading

Note: Comprehensive and current reading lists for courses are produced annually in the Course Guide or other documentation provided to learners; the indicative reading list provided below is used as part of the approval/modification process only.

Books

• Bartlett, R., (2013), A Practitioner’s Guide to Business Analytics, McGraw-Hill Education

Journals

Learners are encouraged to consult relevant journals on data driven decision making.

Electronic Resources

Learners are encouraged to consult relevant electronic resources on data driven decision making.

Indicative Topics

• Decision Making
• Defining Metrics
• Leadership

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