MSc in Data Science
Solve real-world problems with analytics, applied AI and advanced machine learning across the full data science lifecycle, through a blend of core modules and an Applied Data Science Practicum.
- Credits
- 90 ECTS
- Duration
- 18–24 months part-time
- Delivery
- Fully online, cohort-based
- Award
- MSc in Data Science
- Starts
- Monthly
Course Overview
The MSc in Data Science is an advanced 90 ECTS programme that equips students with key skills in programming, data management, and applied analytics. Through a blend of core modules and an Applied Data Science Project, learners apply advanced techniques to real-world challenges across business and research contexts.
The programme includes live workshops, coding sessions, and case studies that develop strong problem-solving and technical capabilities. Students may enter directly into the MSc or progress from a completed Postgraduate Diploma, gaining the knowledge and experience needed to advance in data-centric roles across industries.
Course modules
Module 1 — Exploratory Data Analysis
Most industry analysis starts with exploratory data analysis and a thorough study of this will help you to perform data health checks and provide initial business insights. You will gain a sound understanding of Python and R programming, descriptive statistics, data management and data visualisation. You will also learn SQL for big data pre-processing and prepare data for big data analytics. The module serves as an essential foundation for advanced analytics taught later in the course.
- Programming in Python, R and SQL
- Data management
- Measures of central tendency and variation
- Data visualisation
Module 2 — Statistical Inference
Statistical inference is the process of drawing inferences or conclusions from data using statistical techniques. This is at the core of data science, and a strong understanding of statistics from the beginning is the prime ingredient for a competent data scientist. In this module, you will cover sampling, statistical distribution, hypothesis testing, and variance analysis and use R code to carry out various statistical tests and draw inferences from their output.
- Principles of statistical inference
- Parametric tests
- Non-parametric tests
- Analysis of variance (ANOVA)
Module 3 — Fundamentals of Predictive Modelling
Solutions to many business problems are related to successfully predicting future outcomes. This module introduces predictive modelling and provides a foundation for more advanced methods and machine learning. You'll gain an understanding of the general approach to predictive modelling and then build simple and multiple linear regression models in Python and R and apply these in a range of contexts.
- Predictive modelling principles
- Linear regression models
- Model validation
- Python and R packages for predictive modelling
Module 4 — Advanced Predictive Modelling
In this module, you are introduced to model development for categorical dependent variables. Binary dependent variables are encountered in many domains such as risk management, marketing, and clinical research, and this module covers detailed model building processes. Multinomial and ordinal logistic regression are also covered.
- Logistic regression models
- Survival analysis
- Cox regression
- Poisson regression
Module 5 — Time Series Analysis
In this module, time series forecasting methods are introduced and explored. You will analyse and forecast macroeconomic variables such as GDP and inflation, as well as look at complex financial models using ARCH and GARCH, ARIMA, time series regression, exponential smoothing, and other models.
- Time series concepts
- Assessing stationarity
- ARIMA, ARCH, GARCH modelling
- Panel Data Regression
Module 6 — Unsupervised Multivariate Methods
Data reduction is a key process in data science, and you will learn to apply data reduction methods such as principal component analysis, factor analysis, and multidimensional scaling. You will also learn to segment and analyse large data sets using clustering methods, another key analytical technique that brings out rich business insight if carried out skillfully.
- Principal Component Analysis
- Factor Analysis
- Multidimensional Scaling
- Cluster Analysis
Module 7 — Machine Learning 1
Machine learning algorithms are new generation algorithms used in conjunction with classical predictive modelling methods. In this Machine Learning 1 module, you will understand applications of the support vector machine, K-nearest neighbours, and naive bayes algorithms for classification and regression problems using case studies from a range of industries and sectors.
- Naive Bayes Method
- Support Vector Machine Algorithm
- K-nearest neighbours
Module 8 — Machine Learning 2
The Machine Learning 2 module continues developing your machine learning knowledge, and you will cover decision tree, random forest, and neural network algorithms for regression and classification, again drawing on case studies from real-world data. You will have the opportunity to compare the performance of machine learning algorithms against classical statistical models and learn to assess which are most appropriate for specific scenarios.
- Decision Tree
- Random Forest
- Association Rules
- Neural Networks
Module 9 — Text Mining & NLP
This module looks at analysing unstructured data such as that found in social media, newspaper articles, videos, and more. In particular, you will look at methods for text mining and natural language processing using R and Python code to produce graphical representations of unstructured data and carry out sentiment analysis.
- Structured vs unstructured data
- Text mining in R and Python
- Sentiment analysis using R and Python
Module 10 — Data Science in Practice
The Data Science in Practice module provides you with an opportunity to apply your knowledge through project work. You will select a project from a specific domain and appropriately apply exploratory data analysis, statistical methods, and select appropriate advanced modelling techniques. This module also develops your scientific communication skills through the preparation of project reports and presentations.
- Presentation and communication skills
- Synthesis of data science knowledge
- Application to real world data and scenarios
Module 11 — Applied Data Science Practicum
This Postgraduate Major Project completes the MSc Data Science and students choose a problem from a particular business or social domain. They have the option of working on a real-world problem from their own organisation and work with a mentor in conjunction with their course supervisor. Students are required to solve a research problem that involves carrying out exploratory data analysis, hypothesis testing, research design and use a range of classical and/or modern machine learning modelling methods to predict outcomes and provide actionable insights and recommendations. In doing so they will apply technical capabilities together with research skills and critical thinking. A key part of the project is to communicate the output of the student's research to both technical and non-technical audiences through written, verbal and visual means.
- Critical and creative thinking
- Application of technical expertise
- Scientific communication
Ready to start
Apply now, or download the brochure to explore the programme in detail.
Delivery
Fully online, cohort-based, with live teaching and one-to-one support throughout.
Start any month
Monthly starts, no fixed intake. Every student gets an induction at the start covering the platform, the study plan and assessment requirements.
Live classes, recorded
Regular live concept classes with the DSI teaching team, built around practical coding and modelling exercises. Every session is recorded, so you can study around work.
One-to-one tutor support
Individual guidance throughout the programme, with daily live support for questions on material, assessments and progress.
Applied projects
Individual and group work on authentic case studies, leading to the Applied Data Science Practicum.
Student community
The DSI Discord channel, where you can raise questions and discuss material with other students and the teaching team.
One platform for everything
Recorded sessions, structured course materials and supplementary resources, available any time.
Internationally accredited
Our accreditation
The Data Science Institute is a full member college of Woolf. Woolf is an accredited, degree-granting Higher Education Institution and is recognised within the European Qualifications Framework (EQF). All diploma and degree programmes adhere to rigorous European Standards and Guidelines, ensuring international academic excellence and credibility.
ECTS
Our curricula are accredited through the European Credit Transfer and Accumulation System (ECTS), a recognised international standard and the world's largest academic accreditation system. ECTS certification ensures widespread acceptance, facilitating both mobility and career development.
Why it matters
Qualifications are internationally portable, recognised by employers, institutions and government agencies, and open pathways for further academic progression and career development.
Admission
The programme welcomes applicants from a range of backgrounds and offers two routes: an academic route for those with a suitable qualification, and a non-academic route for candidates with relevant professional experience.
Academic entry route
For applicants with a suitable academic qualification.
- Honours degree
A 2.1 honours degree (or international equivalent) in a numerate or analytical discipline: Mathematics, Statistics, Computer Science, Engineering, Physics, Sciences, Economics or Business Studies with a quantitative focus.
- Alternative qualifications
Alternative qualifications demonstrating sufficient quantitative or analytical skills are also considered.
Non-academic entry route
For candidates with relevant professional experience or non-degree qualifications.
- Professional experience
At least four years of relevant professional experience, recognised as prior learning (RPL).
- Provisional admission
Join the study track with provisional status under performance-based admission (PBA).
- Full admission
Achieve a GPA of 90% or higher in the first two modules.
- Documents
CV, educational certificates and employer references; supplementary registration fees apply.
English language. IELTS 6.5 or higher, or equivalent, for non-native speakers.
Admissions
Complete the online application form, receive an invitation to enrol, register with Woolf, and finalise your admission through document verification and fee payment. Step one takes about ten minutes. You'll need your qualifications, a CV, and — if you're applying through the non-academic route — employer references.
Online application
Submit your application online with your qualifications and background.
Invitation to enrol
Suitable applicants are invited to enrol on the programme.
Registration
Register with Woolf, DSI's accrediting Higher Education Institution.
Decision
Your application is reviewed and a decision confirmed.
Fee payment
Complete fee payment to secure your place.
Questions
What roles or career paths can I pursue after completing the MSc in Data Science?
The programme prepares you to advance in data-centric roles across industries, applying programming, data management, machine learning and advanced analytics to real-world challenges in business and research.
Is this MSc suitable for those already in data-related roles?
Yes. It applies advanced techniques to real-world challenges across business and research, and it is studied part-time over 18 to 24 months with recorded live classes, so it fits around work.
How is the MSc in Data Science structured and delivered?
Eleven modules, 90 ECTS: ten 6 ECTS modules from exploratory data analysis and statistical inference to machine learning and text mining, plus the 30 ECTS Applied Data Science Practicum. Delivery is fully online and part-time over 18 to 24 months, with monthly starts, live teaching and one-to-one tutor support.
What if I already have a Postgraduate Diploma in Data Science?
You can top up to the MSc by completing the 30 ECTS Applied Data Science Practicum.
What kinds of assessments or projects can I expect?
Individual and group work on authentic case studies throughout, leading to the Applied Data Science Practicum, a 30 ECTS applied project. The assessment requirements are covered at your induction.
Is the MSc in Data Science accredited or recognised?
The Data Science Institute is a full member college of Woolf, an accredited degree-granting higher education institution recognised within the European Qualifications Framework. The programme carries 90 ECTS credits at EQF Level 7.
Can these credits be applied to further study or research?
The MSc carries 90 ECTS credits at EQF Level 7, the level of a master’s degree. ECTS is the European standard for credit transfer; recognition by another institution is at that institution’s discretion.
What academic qualifications do I need?
A 2.1 honours degree, or international equivalent, in a numerate or analytical discipline such as Mathematics, Statistics, Computer Science, Engineering, Physics or the Sciences, or Economics or Business Studies with a quantitative focus. Alternative qualifications demonstrating sufficient quantitative or analytical skills are also considered.
Can I apply if I don't meet the 2.1 requirement?
Yes, through the non-academic entry route. It requires at least four years of relevant professional experience, recognised as prior learning. You join with provisional status and move to full admission by achieving a GPA of 90% or higher in the first two modules. Alternative qualifications demonstrating sufficient quantitative or analytical skills are also considered on the academic route.
What about English language proficiency?
English language skills equivalent to IELTS 6.5 or higher are required for non-native speakers.
How do I apply for the MSc in Data Science?
Complete the online application form, which takes about ten minutes. Suitable applicants are invited to enrol, register with Woolf, DSI’s accrediting higher education institution, and secure their place through document verification and fee payment.
Where can I find more information or assistance?
Email info@datascienceinstitute.net or call +353 21 204 0519.
Apply for the MSc in Data Science
Applications are reviewed on a rolling basis with monthly starts. If you'd rather talk it through first, an admissions advisor can call you.