Module Code - Title:
MS4037
-
STATISTICAL DATA SCIENCE PROJECT 1
Year Last Offered:
2025/6
Hours Per Week:
Grading Type:
N
Prerequisite Modules:
MS4222
MS4215
Rationale and Purpose of the Module:
The aim of this module is to bridge the gap between theory and application of statistical data science methodology in real-world scenarios. Through targeted research projects, students will interrogate, analyse and present data using a variety of advanced statistical data science methods. Emphasis will be placed on the challenges and opportunities presented by such analyses in industry and science including ethics, integrity, data protection and storage. Literature search strategies, report writing, and presentation skills will be covered in specifically designed workshops. This module will synthesise many of the major concepts and ideas encountered in earlier taught modules. This module is Part 1 of a longer module. Part 2, STATISTICAL DATA SCIENCE PROJECT 2 will be undertaken in the Spring term.
Syllabus:
Working in teams and individually, students will learn how to conduct a literature search and synthesise the knowledge in published material to inform their work. Students will then formulate and conduct in-depth statistical data science analyses of real-world datasets. Students will develop a dissemination strategy and give oral and written group and individual reports suitable for a variety of stakeholders.
Learning Outcomes:
Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis)
On successful completion of this module, students will be able to:
1. Conduct a comprehensive literature search to solve a given research question.
2. For a given research question formulate, conduct, interpret, and evaluate an analysis of real-world data.
3. Perform appropriate statistical data science analyses using industry standard software.
4. Develop data management, statistical computing, and oral and written communication skills.
Affective (Attitudes and Values)
On successful completion of this module, students will be able to:
1. Appreciate the importance of working effectively as part of a team and as an individual.
2. Justify and defend a statistical data science analysis plan.
3. Actively display a commitment to ethical practice when working with data.
4. Recognise the need for, and demonstrate, critical thinking skills.
Psychomotor (Physical Skills)
NA
How the Module will be Taught and what will be the Learning Experiences of the Students:
This module will be taught using Team Based Learning (Vance, 2021) and employ small-group collaborative learning to actively engage students in doing statistical data science. Students will experience a modern and dynamic learning environment with an emphasis on teamwork, critical reflection, and feedback. This module will contribute towards students who are KNOWLEDGEABLE and able to bridge the gap between theory and practice for real-world problems, COLLABORATIVE with an ability to work in teams to solve complex problems, RESPONSIBLE with a commitment to collecting and using data ethically in scientific and industry research, PROACTIVE with an ability to make active use of data to drive improvements and positive change, ARTICULATE by being able to communicate to other team members and report their findings to a variety of stakeholders.
Research Findings Incorporated in to the Syllabus (If Relevant):
Prime Texts:
James, G., Witten, D., Hastie, T., Tibshirani, R. (2021)
An Introduction to Statistical Learning with Applications in R
, Springer
Hastie, T., Tibshirani, R., Friedman, J. (2008)
The Elements of Statistical Learning
, Springer
Other Relevant Texts:
Programme(s) in which this Module is Offered:
BSMSCIUFA - MATHEMATICAL SCIENCES
BSFIMAUFA - FINANCIAL MATHEMATICS
BSECMAUFA - ECONOMICS AND MATHEMATICS
Semester(s) Module is Offered:
Autumn
Module Leader:
Ailish.Hannigan@ul.ie