Module Code - Title:
MS4038
-
STATISTICAL DATA SCIENCE PROJECT 2
Year Last Offered:
2025/6
Hours Per Week:
Grading Type:
N
Prerequisite Modules:
MS4037
Rationale and Purpose of the Module:
Being a modern data scientist requires a wide-range of technical and transversal skills, including computational methods, statistical modelling, communication and presentation skills. The aim of this module is to build on the skillsets developed in STATISTICAL DATA SCIENCE PROJECT 1. This module will place a larger emphasis on students working independently on a statistical data science project. Students will further develop their skills in data visualisation, data organisation and wrangling, statistical modelling, inference, and statistical learning, and research dissemination via report writing and presentations. Module ID 7476 is a prerequisite for this module (form would not allow it be selected)
Syllabus:
Students will apply the knowledge and skills gained throughout the programme in statistical data science. They will work independently and carry out a thorough and reproducible analysis on a real-world problem. They will analyse big data and make data-driven predictions through probabilistic modelling and statistical inference, identify and deploy appropriate modelling methodologies in order to extract meaningful information for decision making, and disseminate the results via dash-boards, report writing and oral presentations.
Learning Outcomes:
Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis)
On successful completion of this module, students will be able to:
1. Formulate a research question and identify suitable statistical data science methodologies to employ to answer that question.
2. Develop proficient programming skills and execute advanced analyses with professional statistical software.
3. Demonstrate proficiency in the statistical analysis of data, with the ability to design, implement and evaluate statistical data science models.
4. Explain the analysis pipeline, the models implemented and the final results to a variety of stakeholders.
Affective (Attitudes and Values)
On successful completion of this module, students will be able to:
1. Appreciate the complexities of a statistical data science workflow.
2. Uses an objective approach to justify a statistical data science analysis plan.
3. Recognise the value of inputs from all stakeholders in a data science team.
4. Recognise the balance of working both independently and collaboratively in data science roles.
Psychomotor (Physical Skills)
NA
How the Module will be Taught and what will be the Learning Experiences of the Students:
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:
Hastie, T., Tibshirani, R., Friedman, J. (2008)
The Elements of Statistical Learning
, Springer
James, G., Witten, D., Hastie, T., Tibshirani, R. (2021)
An Introduction to Statistical Learning with Applications in R
, Springer
Efron, B., Hastie, T. (2016)
Computer Age Statistical Inference
, Cambridge University Press
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:
Spring
Module Leader:
Ailish.Hannigan@ul.ie