| 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. Bernardi |
| 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 |
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. By teaching simple data analytics techniques and the necessary critical understanding of broader ethical and societal issues around AI, the unit complements what you have learned in other subjects. It provides you with a practical understanding of how statistical techniques you have learned apply to data analytics in the industry. It complements what you have learned in international business, human resource management, innovation, and sustainability across BSc Management degrees by teaching you how AI can be used 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.
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:
1. to provide an insight into the main data analytics techniques and their relevance for AI;
2. to provide an in-depth understanding of how data analytics should be used in making sound business decisions;
3. to explore areas of applications of AI in various business contexts and provide a critical understanding of its benefits and risks;
4. to provide a critical understanding of the ethical implications of AI for workers and consumers.
On completion of this unit, you will be able to:
ILO1: demonstrate a systematic and critical understanding of the areas of application of data analytics in
organisations and their use in 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 asynchronous/synchronous online lecture and 2-hour lectorials. The first five lectorials will be taught in computer rooms and will focus on data analytics exercises and business problem-based discussions. The next five lectorials 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 lectorials 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 lectorials. In the lectorials 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).
Individual AI report (2,000 words, 50%): students will draw on a number of theories taught in the course and provide a reasoned analysis of key ethical and societal 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).
When assessment does not go to plan
Reassessment on final year units is only permitted where students are unable to sit the original assessment due to Extenuating Circumstances. 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 Faculty 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. If you have self-certificated your absence from an
assessment, you will normally be required to complete it the next time it runs (this is usually in the next assessment period).
The Board of Examiners will take into account any extenuating circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
