BUU33803 Business Analytics

(5 ECTS)

Lecturer: Baidyanath Biswas                        

E-mail: biswasb@tcd.ie                    

Office Hours: By appointment (Room 415 or online)                      

Pre- Requisite        

A high level of quantitative ability is needed for this module.

Available to Exchange students 

Module Description

Businesses rely on insights generated through careful and scientific analysis of data. This module focuses on the applications of statistical, data-oriented techniques and software applications to offer actionable insights to businesses and guide them in decision-making. We will learn what happened previously, then build upon it to predict and make the most favourable business decisions.

We intend to learn the following techniques: Linear Regression, Logistic Regression, Classification, Clustering, Frequent itemset mining, and Time-Series Forecasting. For each technique – we will first learn its working principle, then apply it to numerical problems and finally, business implementations. Our primary software will be any/all of EXCEL, SPSS, R, and Python. For installation of licensed software such as SPSS or Microsoft 365 Excel, please see: https://www.tcd.ie/itservices/

Learning and Teaching Approach

This module will be delivered via six lectures, each of which will be three hours long. A typical lecture session will involve learning the theoretical underpinnings of a statistical technique and its application in practice using some/all of the above software platforms. We will investigate real-world business examples, followed by exercises solved in class. No prior programming knowledge is required, but students must be willing to learn new techniques in programming. We will also share supplementary reading materials such as news articles, business cases, and videos to assist with and complement the topics taught in class.

Learning Outcomes

(NOTE: 5 Learning Outcomes are recommended – with a minimum of 4 and a maximum of 7.)

• Appreciate the role of data-driven insights generation in real-world situations.
• Learn the working principle of each prescribed technique.
• Compute numerical problems/simulations of each prescribed technique.
• Apply each technique to business cases using relevant software.
• Evaluate and compare results from each technique to generate business insights.

Relation to Degree

Workload

Textbooks and Resources

Required core course textbook

• TB1 – “Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking” by Foster Provost and Tom Fawcett, Publisher(s): O'Reilly Media, Inc., ISBN: 9781449361327
• TB2 – “Introduction to Machine Learning with Python: A Guide for Data Scientists” by Andreas C. Mueller and Sarah Guido, Publisher(s): O'Reilly Media, Inc., ISBN: 9781449369415.
• TB3 – “Forecasting: Principles and Practice” by Rob J Hyndman and George Athanasopoulos, Online Edition freely available at: https://otexts.com/fpp2/
• TB4 – “Complete Business Statistics”, by Amir Aczel and Jayavel Sounderpandian, Publisher(s): McGraw-Hill Higher Education, ISBN: 9780071284936

General Supplemental Readings

• Healey, J.F. (2015). Statistics: A Tool for Social Research. 10th Edition. Stanford: Cengage ISBN: 978-1-285.45885-4.
• Grolemund, G. and Wickham, H. (2016). R for Data Science. O’Reilly. http://r4ds.had.co.nz/ Creative Commons.
• Data Mining: Concepts and Techniques by Jiawei Han, (3rd Edition) Micheline Kamber and Jian Pei. Morgan Kaufman.
• HBR Case articles and research papers will be assigned in the course outline and classroom lectures.

Student Preparation for the Module

Attendance Policy

Students are expected to attend all the classes. Medical absences should be communicated to the instructors at the earliest.

Preparation

Students should come to the class well-prepared. You are expected to come to the class after reading any assigned material for the particular class. Students are also expected to spend sufficient time beyond class hours (as indicated by the course load for the module) to revise and prepare for the classes and assignments. 

Course Communication

Please note that all course-related email communication must be sent from your official TCD email address. Emails sent from other addresses will not be attended to.

Assessment

Students will have two assignments to develop their skills in this area. All submissions must be via ‘Turnitin’ (referencing / originality-checking software) on  Blackboard.

Group Assignment (50%) Deadline – End of Module 2024

Students are asked to complete a group-based project. This assignment has a word limit of 1500 words. More details will be given in Lecture 1.

Individual Assignment (50%) – After five sessions 2024

The individual assignment will be given after five sessions. Details of the individual assignment, including sample questions and answers, will be provided during the lectures.

Re-Assessment

Students who fail the exam will be allowed to sit for a supplemental examination.

Biographical Note

Dr Baidyanath Biswas is an Assistant Professor in Business Analytics at Trinity Business School. Before joining Trinity, he was an Assistant Professor at the DCU Business School in Ireland. Baidyanath received his PhD in Information Systems (cybersecurity) from the Indian Institute of Management Lucknow (2019) and his Bachelors in Electronics Engineering from the Bengal Engineering and Science University in India (2005). Baidyanath’s research focuses on business analytics, cybersecurity and IT risk management. His work has appeared in several reputed business and management journals. Baidyanath is a passionate teacher with experience at the Undergraduate and Masters levels. Baidyanath was nominated for the 2023 President’s Award for Teaching Excellence at DCU. He has previously taught various modules, including Business Analytics, Total Quality Management, E-commerce, Digital Ecosystems and Management of Information Systems. Before joining academics, Baidyanath worked with Infosys and IBM for nine years as a Mainframe and DB2 specialist.