| 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 |
| Pre-requisites |
Quantitative Analysis in Management (EFIM10014) or Mathematical and Statistical Methods (EFIM10008) |
| Co-requisites |
None |
| School/department | School of Management - Business School |
| Faculty | Faculty of Social Sciences and Law |
Unit Directors: Dr Roberta Bernardi and Andrew Rogers
With the rise of data analytics and Artificial Intelligence (AI) applications in the company, many companies are increasingly looking for digitally savvy graduates with analytic skills. The aim of this course is to provide students with a basic practical understanding of predictive models and their application in AI systems. It will also equip students with a critical understanding of how to interpret data to make sound business decisions and how to integrate AI to increase productivity in the workplace. The specific aims of this unit are:
1. to provide an insight into the main predictive analytics techniques and their business applications in relation to AI;
2. to teach basic predictive analytic techniques (e.g., linear modelling);
3. to provide an in-depth understanding of how predictive modelling should be used in making sound business decisions;
4. to explore areas of applications of AI in various business contexts and provide a critical understanding of its benefits and risks;
5. to provide a critical understanding of the ethical and societal issues concerning the use of AI in the workplace.
On completion of this unit, students will be able to:
1. demonstrate a systematic and critical understanding of the areas of application of predictive models in organisations and their use in Artificial Intelligence;
2. analyse a business problem and identify and apply appropriate predictive analytic techniques to provide solutions;
3. critically evaluate the impact of AI on businesses and in the workplace;
4. provide a reasoned analysis and evaluation of the main ethical and societal implications of AI;
5. critically evaluate a case for the adoption of AI to solve a business problem.
Teaching will be delivered through a combination of synchronous and asynchronous sessions including lectures, tutorials, drop-in sessions, discussion boards and other online learning opportunities.
Summative (TB1): 3,000-word report (100%) split into two parts: A) 1,000-word numerical/problem solving exercise; B) 2,000 word essay.
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.
