| Unit name | Data-Driven Business Decision Making |
|---|---|
| Unit code | MGRC20002 |
| Credit points | 20 |
| Level of study | I/5 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Professor. Wang |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Management - Business School |
| Faculty | Faculty of Social Sciences and Law |
Why is this unit important?
This mandatory core second year TB1 (Teaching Block 1) unit will teach students how to use business analytics to have a positive impact on businesses and organisations. Through learning how decisions are made in organisations and how business analytics can support and influence decision-making, students will be able to identify their own pathway to impact and maximise the value they can bring to an organisation. Students will also develop the knowledge and skills required to implement and evaluate models that are fundamental to state-of-the-art machine learning and artificial intelligence methods and will learn when and how the outputs of such models can be used in data-driven business decision-making.
How does this unit fit into your programme of study
This unit builds on the fundamental business analytics and mathematical & statistical knowledge and programming skills developed in the first year Introduction to Business Analytics unit and will complement the second year Management Science unit through its focus on predictive, rather than prescriptive, analytics and machine learning methods.
An overview of content
The unit will introduce students to different models of decision-making and how business analytics can support effective and responsible decision-making in businesses and organisations. Students will master the social and technical aspects of decision-making and how predictive analytics can be used, learn the technical knowledge and skills required to develop predictive models to meet business needs, and gain hands-on experience working as team to turn predictive models into actionable recommendations.
How will students, personally, be different as a result of the unit
Students will be able to identify and communicate the benefits and risks of data-driven decision making and
create viable plans for integrating business analytics into decision-making processes. Students will be able to move beyond simple visualisations and analyses of data to incorporate domain knowledge into sophisticated models to predict the future and guide decision-making. Such advanced business analytics skills will give students a competitive advantage in the marketplace.
Learning Outcomes
On completion of this unit, students will be able to:
ILO1: Demonstrate an understanding of how decisions are made in organisations, how sustainable development influences business decision-making, and how business analytics can support decision-making.
ILO2: Use mathematical tools to formulate decision-making problems, develop solutions, and provide recommendations based on analytics.
ILO3: Design and develop suitable predictive analytics solutions to business decision-making problems within the limits of time and resources available.
ILO4: Implement and evaluate a variety of predictive models and improve on their design to meet business needs and requirements.
ILO5: Work effectively in a team to develop data-driven recommendations with the potential for a positive impact in practice.
Students may learn through interactive lectures, data analysis tasks, and industry-relevant case studies and simulations.
Pre-recorded (asynchronous) lectures may be made interactive through online quizzes; on-campus / online (synchronous) lectures will be made interactive though polls, Q&A sessions, and group discussion. Both types of interactive lectures aid learning through listening, reading, memorisation, thinking, and action. Interactive lectures enable students to learn essential business analytics and mathematical knowledge, while pre-recorded walk-through and live coding lectures enable students to learn the essential programming knowledge required to implement predictive analytics in practice.
Data analysis tasks enable students to learn through practical application of their business analytics, mathematical, and programming knowledge to solve business decision-making problems. Data analysis tasks may follow a project-based approach to learning where tasks are clearly positioned in decision-making problems and require students to integrate domain knowledge and practical considerations into their analysis.
Industry-relevant case studies emphasise context-specific learning and application of business analytics knowledge and skills. They require contextual application of essential mathematical and statistical knowledge and programming skills and helps students to relate abstract concepts and technical skills to the real world. Case studies enable students to see business challenges and subsequent business analytics decision-making solutions and results, and to learn through collaborative application of their knowledge and skills.
Industry-relevant simulations emulate real-life situations experienced in professional settings and provide a synthetic practice environment where students can improve their knowledge, skills and attitude through teamwork and collaboration. Simulations enable students to put their knowledge and skills into practice in safe, risk-free and controlled environments emulating the undefined and unpredictable nature of reality. The collaborative and competitive nature of simulations engage and motivate students in their own learning and peer-teaching and prepare them for employability.
Tasks which help you learn and prepare you for summative tasks (formative):
Students may complete regular practice questions and data analysis tasks, individually or collectively, and be able to access answers and solutions for self-assessment. These tasks help students learn towards ILO2 – ILO4 and prepare students for the individual coursework assignment task. Students may read, analyse and discuss case studies to help test their understanding relating to ILO1, and formative feedback would be provided to prepare students for their individual essay assignment task. Finally, group-based data analytics tasks may help students learn how to work effectively in a team (ILO5), and students would be given the opportunity to reflect on their experience to prepare themselves for the group report assignment task.
Tasks which count towards your unit mark (summative):
Individual Coursework (70% of the overall unit mark): Students will demonstrate their knowledge and skill in the use of mathematical and computational tools (ILO2) to meet business needs (ILO3 and ILO4) through a written report documenting the design and development of a solution to a business decision-making problem.
Group Report (30% of the overall unit mark): Students will demonstrate their understanding of how decisions are made in organisations (ILO1) and their ability to work effectively in a team (ILO5) through a written group report analysing and solving a business decision-making problem.
Note: A proportion of your final unit mark is based on a group assignment. So that student contributions are reflected in their marks, an equity share approach to peer assessment will be used which aims to encourage successful productive teamwork and reflect an individual’s contribution to the team.
When assessment does not go to plan
The re-assessment weightings on this unit will not be the same as the original assessment. This means if you do not pass the unit overall, then you will be reassessed with a single piece of assessment weighted at 100%, covering all Learning Outcomes for the unit. Please note, if you passed some components but did not reach the overall unit pass mark, those passed components will be disregarded and not included in the reassessment mark. Your overall mark in the unit will then be solely based on the reassessment work done in the summer reassessment period.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. MGRC20002).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
