MSc in Data Science

The MSc in Data Science enables entry into the data science profession for high quality, numerate graduates and  a pathway to leadership for those already in the field
Full Time/Part Time Online
24 - 40 weeks
90 ECTS credits

What skills and knowledge will I gain?

The MSc in Data Science has been designed to develop skills and knowledge in maths, statistics and programming and provide a career path for those who wish to develop their academic, practitioner, research and critical thinking capabilities within the field of data science. As a result, students develop the full set of skills required for data scientists by organisations across multiple industries and sectors globally. 
The MSc is credit-rated at postgraduate level on the European Qualifications framework and carries 90 ECTS credits.

Key Skills and Knowledge

  • Gain the mathematical and statistical knowledge and understanding required to carry out simple to complex data analysis. 
  • Develop skills in the R, Python and SQL programming languages to use them to successfully carry out data analysis to an advanced level. 
  •  Develop a strong understanding of data management, including evaluation, structuring and cleaning of data for analysis. 
  • Become familiar with and use the tools and techniques used in data visualisation.


  • Develop a comprehensive knowledge of classical data analytics, including statistical inference, predictive modelling, time series analysis and data reduction. 
  • Become familiar with and apply new generation machine learning techniques to business and other problems in order to uncover options and solutions for them. 
  • Work with unstructured data and develop an understanding of text mining and natural language processing.
  • Understand, evaluate and apply data science and analytics within business and organisational contexts. 

What you will learn on the course

You are provided with highly structured and detailed course content. The full MSc Data Science consists of ten modules covering core skills and knowledge, culminating in a 30 ECTS credit Postgraduate Major Project. The course combines self paced learning with scheduled live online lectures, tutorial support for exam preparation. All students are assigned a supervisor for their major project.
The MSc is credit-rated at postgraduate level on the European Qualifications framework and carries 90 ECTS credits.
Masters Stage
Postgraduate Diploma Stage

Postgraduate Major Project

The 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 usie 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
  • Research skills

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 basics in Python and R
  • Data management
  • Measures of central tendency and variation
  • Bivariate relationships
  • Data visuallsation

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)

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

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 logisitic regression are also covered.
  • Logistic regression models
  • Survival analysis
  • Cox regression
  • Poisson regression

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

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

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

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 regressionand 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

Text Mining and Natural Language processing

This module looks at analysing unstructured data such as that found 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
  • Text mining using ggplot2
  • Sentiment analysis using R and Python

Data Science in Practice

The Data Science in Practice module provides you with an opportunity to yor apply 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

Who is the course for?

The MSc in Data Science is aimed at graduates, experienced data science professionals, managers and leaders. It is suitable for those wishing to move into data science from other fields or those wishing to enhance and confirm their existing skills and knowledge.
Advanced Degree Holders
Master’s and PhD graduates and researchers from other disciplines
Experienced Data Analysts
Data Analysts looking to move into a data scientist role
Data Scientists
Data science professionals looking to confirm and extend their skills
Graduates from numerate disciplines including business and finance, computing, economics, the sciences, social sciences 
Experienced managers
Experienced managers and leaders seeking to gain insight into data science and its role in business and organisations
Career advancement
Data science professionals looking to confirm and extend their skills and move into leadership roles

How do I progress with the Masters Degree in Data Science?

  • Graduates of the MSc in Data Science enhance their employability through the technical, research, critical thinking and communication skills that they develop on the MSc. 

  •  For those already working in data science and analytics roles, the MSc is a path to leadership and management roles. 

  • For those moving into the field from other careers and disciplines, the MSc in Data Science equips them with technical expertise and research skills that are applicable within a wide range of industries and sectors. 

  • MSc Data Science graduates can also combine their professional experience to achieve certified professional data scientist (CPDS) or Fellow of the Data Science Institute (FDSI).
eiuropean accredited programme (ECTS)


Data Science Institute is a full member college of Woolf, offering accredited degrees under the European Standards and Guidelines (Brussels 2015). Woolf is a global collegiate Higher Education Institution licensed in Europe (license 2019-015), and the operative Policy of Quality Assurance allows member colleges to share the same standards of accreditation while independently managing their own students and faculty. Courses with ECTS credits are specifically designated as such.