Module: Statistical Models and Methods for Business Analytics

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Statistical Models and Methods for Business Analytics
Module Code	BAA1089 (ITS: SB5002)
Faculty	DCU Business School	School	DCU Business School
NFQ level	9	Credit Rating	10

Description

The module will teach students the key statistical methods required for Business Analytics, and crucially, will develop the students’ ability to interpret the result provided by a statistical model and evaluate its performance. They will also work on communicating insights to inform management decisions. Students will be initially learning how to work with data; understanding data, working with graphs and performing descriptive statistics and correlations. Before moving onto more advanced techniques including statistical tests (A/B testing), linear regression, etc., working with excel for statistics, correlation vs causation, hypothesis testing, transition toward machine learning, and data visualisation and communication. Students will use Power BI for creating interactive reports, dashboards, and meaningful data visualisations. They will learn how to present data in a user-friendly way, using visual storytelling to communicate insights effectively. The course will be delivered with a practical approach, with the material embedded within realistic business-related examples , case studies that will advance their understanding of decent work and economic growth as per SDG 8. Additionally, students will use the VR lab to improve their presentation skills. They will also have the opportunity to benefit from online training on related topics via Kubicle, such as: Data Literacy, Excel for Data Analytics, Data Presentation Skills, and Power BI.

Learning Outcomes

1. Understand the importance of statistical analysis in current business environment and how organisations can make use of it.
2. Understand the characteristics of different types of data and the differences between structured and unstructured data.
3. Identify and apply appropriate statistical techniques for gathering valuable business insights.
4. Interpret statistical output to drive business decisions.
5. Evaluate the fit-for-purpose of a statistical model and its analytical and predictive performance.
6. Implement statistical analysis and create interactive dashboards and reports that effectively visualise business data.
7. Develop skills in data storytelling and presentation, learning how to design Power BI reports that clearly communicate key insights and drive data-driven decision-making among stakeholders.
8. To manage collaborative projects effectively by communicating professionally within a group, resolving conflicts constructively, and contributing equitably to achieve shared goals.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	44	Class lectures
Independent Study	110	Preparation for class and tutorials
Group work	20	Working on group assignment
Tutorial	50	Completion of tutorials or online learning activities
Assignment Completion	26	Individual assignment design and completion.
Total Workload: 250

Section Breakdown
CRN	12268	Part of Term	Semester 1
Coursework	100%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	N
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Babu Veeresh Thummadi	Module Teacher

Type	Description	% of total	Assessment Date
Assessment Breakdown
In Class Test	Statistics in-class test	30%	Week 8
Group assignment	This project is based on a CBL approach in which student groups choose an existing company, gather live data and build a business solution. This work will require comprehensive data cleaning of live data, data analysis, visualisation and statistical tests based on the concepts discussed in class and provide recommendations for business on topics such as AI regulation . At the end, they will use Power BI to present their output to a mock company board (composed of other students and the lecturer). This role-playing exercise will train them in an important aspect of business analytics: communicating insights from data.	30%	Week 10
Assignment	Write a report and develop a presentation on how to use business statistics for solving a real-world problem on emerging technologies.	30%	Week 12
Completion of online activity	Completion of Kubicle online training	10%	Week 12

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Introduction to Business Analytics
Data-driven decision making, Predictive analytics, Business intelligence, Data visualization, Big data, Key performance indicators (KPIs), Data mining, Machine learning in business.

Descriptive statistics and visualisation
Mean, median, mode, Standard deviation, Variance, Histogram, Scatter plot, Boxplot, Correlation, Data distribution, Outliers

Probability
Random variables, Probability distributions, Conditional probability, Normal distribution

Statistical techniques
Hypothesis testing, P-values, Confidence intervals, T-test, Chi-square test, Sampling techniques.

Regression modelling
Linear regression, Multiple regression, Logistic regression, Regression coefficients, Residual analysis, Multicollinearity, Predictive modelling

Introduction to Power BI
Introduction to Power BI, Building your First Dashboard, Advanced Visualisations. Choosing the type of visualisations. Effective Presentation of Analytics Findings

Case studies
Real-world applications, Business process optimization, Retail analytics, Healthcare analytics, Financial risk assessment, Supply chain analytics, Marketing campaign analysis, Fraud detection

Indicative Reading List

Books:

Groebner, D., Shannon, P., Fry, P.,: 2024, Business Statistics: A Decision Making Approach, 11,
O’Connor, E.,: 2018, Microsoft Power BI Dashboards Step by Step. Microsoft, Pearson,
Provost, F., Fawcett, T: 2013, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking,
Sharda, R., Delen, D., Turban, E.: 2020, Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support, Pearson,
Sharda, R., Delen, D., Turban, E.: 2024, Business Intelligence, Analytics, Data Science, and AI: A Managerial Perspective, Fifth,
Sherman, R.: 2015, Business Intelligence Guidebook From Data Integration to Analytics,

Articles:
None

Other Resources

1: https://kubicle.com/, Data Presentation Skills,
419873: 1, https://kubicle.com/, Foundations of generative AI,