Module Code - Title:

MA6101 - STATISTICS FOR DATA ANALYTICS

Year Last Offered:

2025/6

Hours Per Week:

Lecture

2

Lab

1

Tutorial

0

Other

3

Private

4

Credits

6

Grading Type:

N

Prerequisite Modules:

Rationale and Purpose of the Module:

This module is a core module for the new MSc in Business Analytics (from KBS).

Syllabus:

1. Types of structured data. Data collection using passive, causal and survey design approaches. 2. Validity and reliability of collected data, sources of bias within the context of analytics. 3. Data visualisation and descriptive analytics. Data cleaning and presentation of key features of data sets. 4. Conditional probability and probability distributions with applications. Bayes theorem. 5. Inference and hypothesis testing. Issues surrounding statistical analysis in large datasets. 6. Correlation and association for categorical and numerical data. Simpson's paradox and confounding. 7. Regression models with emphasis on applications in predictive analytics.

Learning Outcomes:

Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis)

1. Understand and implement data collection procedures using current best practices. 2. Carry out exploratory data analysis using appropriate visualisation and summary methods to identify key data features. 3. Describe the most commonly used probability distributions and identify real-world applications for the use of each. Use distributions to calculate probabilities. 4. Understand estimation, construct confidence intervals and carry out hypothesis testing to derive new knowledge for decision-making.. 5. Investigate correlation between variables, fit predictive models and perform model-checking. 6. Carry out analyses in statistical software.

Affective (Attitudes and Values)

1. Discuss what is meant by 'statistical thinking'. 2. Differentiate between data types and identify the appropriate data analytics tools. 3. Relate the role of probability and probability distributions to real-world problems. 4. Discuss the merits and pit-falls of hypothesis testing in Big Data. 5. Discuss the impact of confounding in predictive modelling. 6. Demonstrate the ability to analyse real-world data and communicate the results to stakeholders.

Psychomotor (Physical Skills)

N/A

How the Module will be Taught and what will be the Learning Experiences of the Students:

This module will be delivered using standard lectures and computer laboratories. Lectures will focus on teaching through real-world problems, relating theory and practice. Hands-on computer laboratories will develop the skills required to carry out data analytics in industry.

Research Findings Incorporated in to the Syllabus (If Relevant):

Prime Texts:

Jason Abrevaya (2025) Probability and Statistics for Economics and Business: An Introduction Using R , The MIT Press

Bruce L. Bowerman (2025) Business Statistics and Analytics in Practice , McGraw-Hill

Other Relevant Texts:

David Doane (2024) Applied Statistics in Business and Economics , McGraw-Hill

Programme(s) in which this Module is Offered:

Semester(s) Module is Offered:

Autumn

Module Leader:

shirin.moghaddam@ul.ie