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BAN601- Quantitative Methods for Business Analytics

MSc Business Analytics

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Core Course

BAN601- Quantitative Methods for Business Analytics

Course Unit Code: BAN601

Type Of Unit: Core

Level of Course Unit: Graduate

Year of Study: 1

Semester: Semester 1

Number of ECTS Credits: 10

Class Contact Hours: 12

Mode of Delivery

Distance Learning

Prerequisites

None

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The course discusses a number of quantitative methods from Discrete Mathematics and Statistics which is considered core knowledge for the students studying Business Analytics and Artificial Intelligence at the MSc level.
From the Discrete Mathematics perspective the course will start from the very basic contents, propositional logic, introduction to proofs, Boolean Algebra and set theory. Then we will move into counting techniques, permutations and combinations and we will introduce some basic aspects of Discrete probability. Finally we will introduce more advanced topics, like algorithms and their complexity, induction and recursion algorithms, tress and graphs.

From the Statistics perspective we will introduce basic definitions of exploratory data analysis (measures of location and measures of spread) and we will discuss important type of plots that can be used for data discussion and inference. Also building on the introduction for Set theory and Discrete probability in the Discrete Mathematics part we will discuss Discrete and Continuous distributions, Conditional and joint distributions, Sampling distributions and then we will move to inferential Statistics, that is, point and interval estimation, hypothesis testing and Regression.

The course will close with a brief introduction to Multivariate Statistics which will help the students move into the Modern data analysis, machine learning courses which will follow.

Throughout the course, the students will see the theoretical and the practical aspects of quantitative methodology. In addition to the toy (small) dataset example we will use for the in class learning, we will also have the opportunity to apply these methods in larger (real) datasets by using the R programming language.

By the end of the course we expect the students to be able to discuss the theoretical aspects of the topics discussed as well as being able to answer basic data analytics and statistical inference research questions to real data.

Learning Outcomes

1: Understand the basics of exploratory data analysis and the use of statistical graphs
2: Gain familiarity with R Statistical programming language and be able to use it to analyse big datasets
3: Understand the basics of Probability theory
4: Ability to apply appropriate methodology to analyze real data and make statistical inference.
5: Ability to apply appropriate methodology to analyze real data and make statistical inference.

6:  Understand the basics of propositional logic and be able to construct basic mathematical sentences and proofs.

7: Understand the basics of propositional logic and be able to construct basic mathematical sentences and proofs

1st week: Introductory Statistics (mean, variance, histograms, boxplots) and Introduction logic and proof
2nd week: Introduction to Set theory and Introduction to Probability and introduction to functions/sequences/sums and matrices.
3rd week: Discrete and Continuous Distributions
4th week: Conditional probability and Joint probability/ Bayes theorem and counting techniques
5th week: Sampling distributions., point and interval estimation
6th week: Introduction to hypothesis testing – Hypothesis testing on one sample
7th week: Hypothesis testing on 2 samples
8th week: Hypothesis testing on 3 or more samples (ANOVA – Contingency Tables – Goodness of fit tests)
9th week: Regression/Correlation
10th week: Induction and Recursion
11th week: Algorithms and their complexity
12th week: Introduction to Graphs

Course Features

Weekly self-assessment activities :

On a weekly basis, students will have the possibility to engage in self-assessment activities to judge their own level of understanding of the concepts covered so far. The weekly self-assessment activities provide immediate feedback.

Weekly interactive activities (20%)

Weekly interactive activities account for 20% of the grade, and will be graded almost instantly by the instructor with appropriate feedback.

Project (30%) :

Students will be provided with a real dataset and a number of research questions and they will be asked to provide a solution.  Deliverable will be the code and a report written as a provision from a consultant to a client.

Readings

Rosen (2018) Discrete Mathematics and it’s applications, McGraw Hill, 7th Edition

Devore and Berk (2012) Modern Mathematical Statistics with Applications, Springer

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Informed Consent Form

Dear Participant,

You are invited to participate in a survey as part of the DiFiCy – Digital Transformation and FinTech: Shaping Cyprus’s Economy research project which is implemented by the University of Limassol, PwC Cyprus and Unity Growth Limited.

The project is co-funded by the European Union within the framework of the Cohesion Policy Programme “THALIA 2021-2027” through the Research and Innovation Foundation (RIF) in Cyprus [Pr. number: EXCELLENCE/0524/0568].

The study has been approved by the National Bioethics Committee of Cyprus (Approval no.: EEBK EΠ 2025.01.19 )

This project explores digital transformation and FinTech adoption in Cyprus, with a particular focus on SMEs and the broader business ecosystem. It uses a mixed-methods research design, combining survey data with interviews and case study evidence to generate evidence-based insights for policymakers and businesses.

Before deciding whether to participate, please read the following information carefully.

Purpose of Participation

The purpose of this research is to better understand the drivers, challenges, and impacts of digital transformation and FinTech adoption in Cyprus. The findings aim to support evidence-based policymaking, strengthen business practices, and contribute to innovation and economic development.

Your participation involves the following:

Completion of this survey, which takes approximately 15 minutes.

Invitation to take part in an optional interview.

Contribution to a case study or discussion session at a later stage.

Your participation is entirely voluntary, and you are free to decline or withdraw at any time without any consequences.

If you wish to withdraw from the study after submitting your responses, you may do so by contacting the Project Coordinator.

Data Confidentiality and Usage

All data collected will be handled in strict accordance with the General Data Protection Regulation (GDPR) and Cyprus Data Protection Law (125(I)/2018).

Your data will be treated confidentially, pseudonymised and anonymised, and no personally identifiable information will be disclosed. Data will be securely stored and accessed only by authorised members of the research team. In some cases, qualitative data may be processed using machine learning techniques, such as keyword identification or pattern detection, strictly to support analysis; however, all interpretation and conclusions will be made by the researchers.

Aggregated and anonymised findings derived from the data (e.g., statistical summaries, graphs, and thematic insights) may be used for dissemination activities, including scientific publications, policy reports, infographics, and training materials. These outputs will not contain any information that could directly or indirectly identify participants or their organisations.

Anonymised data may be shared publicly after a 24-month embargo period, while raw or sensitive data will not be publicly available. Access to raw or sensitive data will be granted only upon justified request and consortium approval.

Your participation in this study does not involve any risks.

On the contrary, your contribution may help improve digital and FinTech policies, support SME development and innovation strategies, and enhance the overall business environment in Cyprus.

Contact Information

If you have any questions, concerns, withdraw requests or complaints, please contact the Project Coordinator, Dr. Snezana Eminidou. Please include “DiFiCy Survey” in the subject line.

Email: snezana@uol.ac.cy

Phone number: +357 22462228

Partnership:

Thank you for your valuable contribution to this research.

The project is co-funded by the EU within the framework of the Cohesion Policy Programme “THALIA 2021-2027” through the Research and Innovation Foundation. [Pr. no. EXCELLENCE/0524/0568]. The content reflects the views of the author(s) only. The European Commission and the Managing Authority of THALIA 2021–2027 cannot be held responsible for any use which may be made of the information contained therein.