| Unit name | Data Analytics and Artificial Intelligence for Business |
|---|---|
| Unit code | EFIM30051 |
| Credit points | 20 |
| Level of study | H/6 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Dr. Cheng |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
Quantitative Analysis in Management (EFIM10014) or Mathematical and Statistical Methods (EFIM10008) |
| 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?
With the rise of data analytics and Artificial Intelligence (AI) applications in business, many companies are increasingly looking for digitally savvy graduates with skills in data analytics. This unit provides you with business analytics skills as well as a theoretical and practical understanding of the significance of data analytics for AI. The unit provides you with a practical understanding of how statistical techniques apply to data analytics and AI together with the necessary critical understanding of broader ethical and societal issues around AI in an industry.
How does this unit fit into your programme of study?
The unit complements what you have learned in international business, human resource management, innovation, economics and sustainability across BSc Management degrees by teaching you how data analytics can support decision making in organisations, and how AI can be employed to improve a company's strategic positioning or innovate service operations, and the impact that AI may have on the workforce and consumers. The unit will equip you with the needed analytical skills on the job market to boost your employability as well as prepare you to become a digitally savvy manager who cares about the ethical and responsible use of AI.
An overview of content
The unit’s content is divided into two parts. The first part teaches statistical techniques that are employed in data analytics (e.g. linear/logistic regressions and cluster analysis). In this first part of the unit, you will learn how to apply these statistical techniques to solve business problems. The second part of the unit teaches AI in business. It provides you with an understanding of how the statistical techniques you have learned in the first part of the unit apply to AI. You will learn about various areas of application of AI in a managerial context, including its strategic and ethics implications.
How will students, personally be different as a result of the unit
The aim of this course is to provide you with a basic practical understanding of data analytics and its significance for AI. It will also equip you with a critical understanding of how to interpret data to make sound business decisions and how to integrate AI to innovate products and services. The specific aims of this unit are:
Learning Outcomes
On completion of this unit, you will be able to:
ILO1: explain how data analytics can be used to automate a business process or decision making with Artificial Intelligence;
ILO2: analyse business problems and identify and apply appropriate data analytics techniques to provide solutions;
ILO3: provide reasoned analyses and critically evaluate the impact of AI on businesses and workplaces, and the main ethical and societal implications of AI;
ILO4: critically evaluate cases for the adoption of AI to solve a business problem.
The unit will be taught in one hour lecture and 2-hour seminars. The first five seminars will be taught in computer rooms and will focus on data analytics exercises and business problem-based discussions. The next five seminars will require students to discuss thought-provoking academic papers and practice-oriented cases about the use of AI in organizations. Students will be required to practice some exercises and do some reading before each session. Students will be able to access and revise the unit contents on Blackboard.
Tasks which help you learn and prepare you for summative tasks (formative)
The seminars will be highly interactive sessions where students will have the opportunity to discuss the problems they are trying to solve and get verbal feedback from lecturers/tutors as well as peer-to-peer feedback. Students will practice and be given feedback on computer lab exercises on data analytics in the seminars. In the seminars about AI, feedback about students’ understanding of the unit contents will be based on students’ discussions of academic and practice-oriented reading that students will have to prepare before each session. Students will have to discuss ethical and societal issues about AI. The tasks that students will have to perform in class will be similar to what is expected from them in the assessment. In this way, students can receive formative feedback that will help their preparation of the assessment.
Tasks which count towards your unit mark (summative)
Individual data analytics report (1,000 words, 50%): students will have to analyse a business problem and identify and perform a data analytics exercise to provide a solution and explain how the proposed data/model may be suitable to automate a business process or decision making with AI (ILO1 and ILO2).
Preparation for four AI seminars (10%): students will be required to complete a set of online activities (e.g. forum discussions on Blackboard) based on the lecture material and seminar reading before each seminar. Students will be awarded up to a maximum of 10 marks for completing all seminars’ activities (ILO3).
Individual AI report (1,500 words, 40%): students will draw on a number of theories taught in the course and provide a reasoned analysis of key ethical and organisational issues in order to develop a set of recommendations around the adoption of a proposed AI solution to solve a business problem (ILO3 and ILO4).
Both the Individual data analytics report (50%) and the Individual AI report (40%) must be passed to pass the unit.
When assessment does not go to plan
In the event reassessment is needed, the re-assessment components on this unit will remain the same weighting as the original assessment. This means that if you do not achieve the pass mark overall in the unit, you will only re-sit the components you have not passed, in the summer reassessment period. Your marks from the components you have passed will be “carried forward” to be included in your overall unit mark.
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. EFIM30051).
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.
