| 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.
Essential/recommended reading:
A number of academic articles and practice-based cases will be assigned to each session. Students will be encouraged to read a selection of articles. Details will be made available on Blackboard.
Recommended textbooks:
Brynjolfsson, Erik and McAfee, Andrew. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. WW Norton & Co, 2014.
Finlay, S. (2018). Artificial Intelligence and Machine Learning for Business, 3rd Ed., Relativistic.
Finlay, S., (2014), Predictive Analytics, Data Mining, and Big Data: Myths, Misconceptions and Methods, Basingstoke: Palgrave Macmillan.
Foreman, J. W., (2014), Data Smart: Using Data Science to Transform Information into Insight, Indiana: John Wiley & Sons.
Rose, D. (2018), Artificial Intelligence for Business: What you Need to Know about Machine Learning and Neural Networks, Beaverton: Chicago Lakeshore Press.
Siegel, Eric, (2013), Predictive Analytics, Hoboken, New Jersey: John Wiley & Sons.
Zuboff, S. (2019). The Rise of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. Profile Books.
