Master of Science in Data Science

Introduction

Master of Science in Data Science

The MSc in Data Science is designed for individuals who wish to deepen their knowledge of and skills in data science, and its use in various areas of employment. It is designed for those who will use data in sophisticated ways to support organisations and their managers in improved decision making.

Entry requirements

Education Requirements

Candidates must hold an EQF Level 6 degree involving technical skills, such as Mathematics, Computer Science, or Engineering; or have at least 7 years of relevant experience (such as IT or Engineering) with an EQF Level 5 qualification. In either case, applicants must demonstrate some familiarity with programming. Students with relevant experience may apply for Recognition of Prior Learning (RPL) at the time of admission.

Language Requirements

English language competency at IELTS 6.5 (or equivalent) is required of all applicants.

Instructional design

Teaching: The programme combines asynchronous components (lecture videos, readings, and assignments) and synchronous sessions attended by students and an instructor via video call. Asynchronous components support students from diverse work-life situations, while synchronous meetings provide accountability and motivation. Students have direct access to instructors and peers through messaging, group chat, and video calls at all times.

Assessment: Each module includes regular assignments (typically 1–4 per unit) submitted prior to synchronous sessions, and a summative final examination. Final assessments take the form of exams, essays, reports, presentations, or projects.

Degree structure

The degree requires 90 ECTS: 18 ECTS of compulsory foundational modules (Tier 1), up to 42 ECTS of elective specialist modules from Specialisation A (Data Analytics in Business) or Specialisation B (Machine Learning), and a 30 ECTS compulsory capstone project (Tier 3).

Module Descriptions

1. Exploratory Data Analysis & Data Management

Most industry analysis starts with exploratory data analysis and a thorough study of this will help learners to perform data health checks and provide initial business insights. The module helps the learner to understand and perform descriptive statistics and present the data using appropriate graphs/diagrams, and serves as a foundation for advanced analytics. This module also introduces the basics of programming in R and Python, the most commonly used languages in data science. The module culminates in practices related to data management, focusing on SQL as a highly practical language for data preprocessing, and addresses ways to connect SQL with R and Python tools.

Learning Outcomes

1. Independently work in R, Python, and SQL development environments.
2. Import and export datasets and create data frames within R and Python, and connect these to SQL for preprocessing.
3. Manage data sets using a variety of functions, including acting autonomously to identify problems and relevant solutions for data wrangling.
4. Troubleshoot problems and be prepared to make leadership decisions related to industry methods and principles of data analysis and management.

2. Statistical Inference

This module provides learners with an in-depth understanding of statistical distribution and hypothesis testing in a practical approach for getting things done. Statistical distributions include Binomial, Poisson, Normal, Log Normal, Exponential, t, F and Chi Square. Parametric and non-parametric tests used in research problems are covered in this unit.

Learning Outcomes

1. Evaluate standard types of distributions.
2. Demonstrate self-direction and industry practices in developing solutions for hypothesis testing.
3. Efficiently analyse the concept of variance through a variety of models.

3. Fundamentals Of Predictive Modelling

This module provides a strong foundation for predictive modelling. Its objective is to define the entire modelling process with the help of real life case studies. Many concepts in predictive modelling methods are common and therefore these concepts will be covered in detail in this module. Students will learn how to carry out exploratory data analysis to gain insights and prepare data for predictive modelling, an essential skill valued in many industries.

Learning Outcomes

1. Apply a professional and scholarly approach to real-world problems pertaining to the estimation of model parameters.
2. Efficiently manage troubleshooting issues that arise in connection to data not explained by a model.
3. Demonstrate self-direction in calculating inflation factors.
4. Solve problems and be prepared to take leadership decisions related to the methods and correlation of variables.

4. Business Intelligence

PowerBI and Excel are fundamental parts of the data analytics toolkit. In this module, learners will gain experience in collecting, processing, analysing, and communicating with data using Excel. Data visualisation is a powerful way to communicate meaning in data and support business decision-making. This module covers the main commercial tools used in data visualisation such as Tableau and Power BI, enabling learners to create a wide range of graphs, charts, and dashboards.

Learning Outcomes

1. Apply a professional and scholarly approach to data analytics within a business context.
2. Demonstrate self-direction in research and originality in addressing the availability of data for business operations.
3. Act autonomously in identifying research problems and solutions related to applications of Excel and PowerBI for analytics.
4. Solve problems related to the use of dashboards and visualisations for business management.

5. Data Science In Practice

This module provides learners with an opportunity to apply key knowledge and skills through project work. They will be able to select a project from a specific domain and will be required to carry out various data management, exploratory data analysis, data visualisation, and predictive modelling tasks.

Learning Outcomes

1. Apply a professional and scholarly approach to data analytics within a real-world context.
2. Demonstrate self-direction in research and originality in addressing statistical analysis and predictive modelling.
3. Act autonomously in identifying research problems and solutions related to data visualisation and analytics.
4. Solve problems related to the use of programming and data modelling in real-world applications.

6. Data Visualisation

The ability to render large data sets intelligible, especially through visual means and to potentially non-expert audiences, is a core part of data science. Building on Exploratory Data Analysis and Data Management, Data Visualisation grounds students in the theory and practice of modern data visualisation, drawing expertise from graphic design, cognitive psychology, user experience, and related fields.

Learning Outcomes

1. Apply a professional and scholarly approach to research problems pertaining to data visualisation.
2. Demonstrate self-direction in research and originality in solutions developed for visualising large data sets.
3. Act autonomously in identifying research problems and solutions related to making raw data intelligible.
4. Solve problems related to the visual presentation of statistical evidence in a variety of contemporary tools.

7. Big Data And Its Applications

Big Data involves the computational treatment of massively large data sets in order to gain actionable insights. Big Data and Its Applications introduces some of the algorithms, statistical models, and computing tools most common in data science today. Tools covered include Hadoop and Apache Spark, as well as platforms such as Amazon AWS, Microsoft Azure, and Google Cloud.

Learning Outcomes

1. Apply a professional and scholarly approach to research problems pertaining to massive data sets.
2. Demonstrate self-direction in research and originality in solutions developed for analysing problems in big data.
3. Act autonomously in identifying research problems and solutions related to reducing data to computationally-manageable amounts.
4. Solve problems related to the analysis of identifying massive data sets relative to a particular problem, as well as in tool and algorithm selection.

8. Optimization

This module is a practical introduction to optimization — a set of mathematical models and processes that help produce decisions in a wide variety of contexts. To prepare students for subsequent work in Machine Learning, this module introduces the fundamental theories and methods for optimization problems. The most common kinds of optimization problems are covered, along with software packages for solving them.

Learning Outcomes

1. Apply a professional and scholarly approach to research problems pertaining to linear and nonlinear optimizations.
2. Demonstrate self-direction in research and originality in solutions developed for analysing decision problems in a variety of domain-specific contexts.
3. Act autonomously in identifying research problems and solutions related to local and global optimality, valid inequalities, weak and strong duality, and convexity.
4. Solve problems related to common optimization problems and developing approximate solutions for these.

9. Privacy And Ethics In Data Science

The technologies and modes of analysis behind big data yield great promise in addressing social problems in various domains such as healthcare and public safety. At the same time, massive data sets that promise anonymity are routinely found to allow individual identification. This module addresses the ethical, legal, and regulatory dimensions of data science practice, including data protection frameworks and responsible use of data.

Learning Outcomes

1. Apply a professional and scholarly approach to research problems pertaining to data mining, anonymity, and privacy.
2. Demonstrate self-direction in research and originality in addressing ethical concerns in a variety of domain-specific contexts.
3. Act autonomously in identifying research problems and solutions related to applications of data science and ethical considerations.
4. Solve problems related to the regulatory contexts for data mining and privacy.

10. Advanced Predictive Modelling

This module builds on the concepts introduced in the module Fundamentals of Predictive Modelling. In this module, learners 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 unit covers detailed model building processes for binary dependent variables. Additionally, a primary goal of the module is for students to be able to select and successfully apply appropriate advanced regression models in applied settings. The module will culminate with multinomial models and ordinal scaled variables.

Learning Outcomes

1. Efficiently estimate model parameters.
2. Demonstrate self-direction in global hypothesis testing.
3. Act autonomously in developing estimates of unknown population parameters.
4. Solve problems related to generalised linear models through link function.

11. Unsupervised Multivariate Methods

Data reduction is a key process in business analytics projects. In this module, learners will learn data reduction methods such as PCA, factor analysis and MDS. Students will develop skills related to the formation of segments using cluster analysis methods. Additionally, students will analyse segments, the process of which is a key technique for large groups of data as intrinsic information appears in detail once segmented thoughtfully.

Learning Outcomes

1. Apply industry best practices for resolving issues pertaining to factor analysis.
2. Demonstrate self-direction in research and originality in developing scoring models.
3. Act autonomously in the estimation of loading matrices and interpreting factor solutions.
4. Solve problems and be prepared to take leadership decisions related to the methods and principles of visualising the level of similarity of individual cases of a dataset.

12. Time Series Analysis

In this module, time series forecasting methods are introduced and explored. Students will gain a working knowledge of the nature and processes used in relation to time series data and confidently recognize and understand trends that exist within that data. This information will be used to make predictions or forecasts. Students will analyse and forecast macroeconomic variables such as GDP and inflation. Additionally, students will work with complex financial models using ARCH and GARCH, ARIMA, time series regression, exponential smoothing and other models.

Learning Outcomes

1. Create synthetic contextualised discussions of key issues related to components of time series.
2. Efficiently manage industry-level issues in connection to trend analysis.
3. Demonstrate self-direction in developing real-world applications for serial correlation.
4. Solve problems and be prepared to take leadership decisions related to the methods and principles of residual analysis.

13. Machine Learning I

Machine learning algorithms are new generation algorithms used in conjunction with classical predictive modelling methods. In this Machine Learning 1 module, learners will understand applications of the Support vector machine, K Nearest Neighbours and Naive Bayes algorithms for classification and regression problems. Additionally, students will develop practical machine learning and data science skills including theoretical basics of a broad range of machine learning concepts and methods with practical applications to sample datasets.

Learning Outcomes

1. Apply a professional and scholarly approach to Bayes theorem and its applications.
2. Efficiently manage issues in connection to machine algorithms.
3. Demonstrate self-direction in bootstrapping and aggregation.
4. Act autonomously in identifying neutral networks for classification problems.

14. Machine Learning II

Machine learning algorithms are new generation algorithms used in conjunction with classical predictive modelling methods. In this Machine Learning 2 module, learners will understand applications of decision tree and random forest algorithms and neural networks for classification and regression problems. Additionally, students will develop practical machine learning and data science skills including theoretical basics of a broad range of machine learning concepts and methods with practical applications to sample datasets.

Learning Outcomes

1. Apply a professional and scholarly approach to Binary Logistic Regression and its applications.
2. Efficiently manage issues in connection to decision tree and random forest machine learning algorithms.
3. Demonstrate self-direction in bootstrapping and aggregation.
4. Act autonomously in identifying neutral networks for classification problems.

15. Text Mining And Natural Language Processing

In this module, students will look at analysing unstructured data such as that found on social media, newspaper articles, videos and more. Specifically, students will look at text techniques for text mining and natural language processing using R and Python code to produce graphical representations of unstructured data and carry out sentiment analysis. This module focuses on learning key concepts, tools and methodologies for natural language processing and emphasises hands-on learning through guided tutorials and real-world examples.

Learning Outcomes

1. Apply a professional and scholarly approach to research problems pertaining to natural language processing.
2. Efficiently manage issues that arise in connection to text mining.
3. Demonstrate self-direction in applying solutions related to text mining.

16. Topics In Domain-Specific Modelling And Analytics

This advanced graduate class addresses a unique topic on a rotating basis in order to keep the program at the forefront of scholarly research and industry practice. Every year the academic staff member will approve of a new topic to be covered. The bibliography will contain not less than 8 peer-reviewed articles or scholarly publications reflecting the current topic. Predictive Analytics and Data Science for Insurance: This course addresses the use of data science across the full extent of functions within insurance, from risk assessment, pricing, fraud detection, customer segmentation, product development, and more. The course focuses on recent developments in AI for modelling complex insurance problems in an increasingly unstable system, and creating more value to companies.

Learning Outcomes

1. Apply a professional and scholarly approach to predictive analytics within an insurance context.
2. Demonstrate self-direction in research and originality in addressing the legal/regulatory and ethical implications of predictive analytics in insurance.
3. Act autonomously in identifying research problems and solutions related to applications of artificial intelligence to insurance problems.
4. Solve problems related to the use of big data in an insurance context.

17. Topics In Data Mining

This advanced graduate class addresses a unique topic on a rotating basis in order to keep the program at the forefront of scholarly research and industry practice. Every year the academic staff member will approve of a new topic to be covered. The bibliography will contain not less than 8 peer-reviewed articles or scholarly publications reflecting the current topic. Data Mining and Social Media: Using the analytic and inferential tools of social media data mining, we are now able to learn a great deal about the individuals who participate online, how they participate, and the different ways that the networks they're a part of are activated by that participation. This course will focus on practical methods for scraping and analyzing social media data, as well as some theoretical implications of these practices.

Learning Outcomes

1. Apply a professional and scholarly approach to data mining within a social media context.
2. Demonstrate self-direction in research and originality in addressing the legal/regulatory and ethical implications of data mining social media.
3. Act autonomously in identifying research problems and solutions related to applications of data mining to social media.
4. Solve problems related to the use of metadata in the context of massively popular platforms.

18. Applied Data Science Practicum

The Applied Data Science Practicum requires learners to investigate a real-world problem in the last phase of the MSc Data Science course. Its objective is to help students appropriately apply the concepts, techniques and tools learned from the Postgraduate Certificate and Diploma parts of the course to a real-world scenario. Students typically choose a problem from a particular business or social domain after discussing it with the course instructor(s). 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 an analytically complex research problem, conduct a literature review, 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.

Learning Outcomes

1. Create synthetic contextualised discussions of key issues related to real-world problems in data science.
2. Apply a professional and scholarly approach to research problems pertaining to data science and machine learning.
3. Efficiently manage interdisciplinary issues that arise in connection to statistical methods and data visualisations.
4. Demonstrate self-direction in research and originality in solutions developed for classical and machine learning algorithms and other modelling methods.

Internships policy

Internships must be a genuine extension of the student’s academic programme, providing opportunity to apply theoretical knowledge to substantive projects directly related to their field of study. Internships consisting primarily of administrative or routine tasks will not be approved.

Every internship must have a defined start date, end date, and formal learning plan with objectives agreed in advance by the student, the host organisation, and the relevant college. Responsibilities and task complexity should increase over time. Each student must be assigned a named supervisor within the host organisation who holds relevant expertise and is responsible for providing regular guidance and feedback.

Woolf prioritises paid internships to ensure equitable access regardless of socioeconomic background. Unpaid internships may only be approved where they constitute a genuine learning opportunity and do not displace the work of a paid employee.

Programmatic standards

Day-to-day management sits with the relevant college. Each college must have a designated Woolf contact responsible for vetting and approving all host organisations and placements before any internship may proceed. Colleges are responsible for matching students to approved positions.

Students must complete pre-internship preparation before commencing a placement, which may include CV writing, interview support, and other instruction as necessary. Virtual internships are encouraged to widen access beyond geographical constraints; support systems must address the challenges of remote work, including cross-timezone communication and fostering professional belonging.

Programme effectiveness must be evaluated on an ongoing basis. Formal evaluations will be collected from students, host supervisors, and academic advisors, and will inform curriculum design and programme improvement.

Grading Scheme

General Marking Criteria and Classification

Marking of student work keeps in view the scale of work that the student can reasonably be expected to have undertaken in order to complete the task.

The assessment of work for the course is defined according to the following rubric of general criteria:

1. Engagement:
   • Directness of engagement with the question or task
   • Range of issues addressed or problems solved
   • Depth, complexity, and sophistication of comprehension of issues and implications of the questions or task
   • Effective and appropriate use of imagination and intellectual curiosity
2. Argument or solution:
   • Coherence, mastery, control, and independence of work
   • Conceptual and analytical precision
   • Flexibility, i.e., discussion of a variety of views, ability to navigate through challenges in creative ways
   • Completion leading to a conclusion or outcome
   • Performance and success of the solution, where relevant
3. Evidence (as relevant):
   • Depth, precision, detail, range and relevance of evidence cited
   • Accuracy of facts
   • Knowledge of first principles and demonstrated ability to reason from them
   • Understanding of theoretical principles and/or historical debate
   • Critical engagement with primary and/or secondary sources
4. Organisation & Presentation:
   • Clarity and coherence of structure
   • Clarity and fluency of writing, code, prose, or presentation (as relevant)
   • Correctness of conformity to conventions (code, grammar, spelling, punctuation, or similar relevant conventions)

Definition of marks

Indicative equivalence table

Synchronous Adjustments Template

Synch discussions may affect the mark on submitted assignments: written work is submitted in advance, and a discussion follows. This provides students an opportunity to clarify and explain their written claims, and it also tests whether the work is a product of the student’s own research or has been plagiarised.

The synchronous discussion acts to shift the recorded mark on the submitted assignment according to the following rubric:

+3

Up to three points are added for excellent performance; the student displays a high degree of competence across a range of questions, and excels in at least one group of criteria. Relative weaknesses in some areas may be compensated by conspicuous strengths in others.

+/- 0

The marked assignment is unchanged for fair performance. Answers to questions must show evidence of some solid competence in expounding evidence and analysis. But they will be marred by weakness under one or more criteria: failure to discuss the question directly; appeal to irrelevant information; factual error; narrowness in the range of issues addressed or evidence adduced; shortage of detailed evidence; or poor organisation and presentation, including consistently incorrect grammar. Answers may be characterised by unsubstantiated assertion rather than argument, or by unresolved contradictions in the argument.

• 3 (up to three points)

Up to three are subtracted points for an inability to answer multiple basic questions about themes in the written work. Answers to questions will fall down on a number of criteria, but will exhibit some vestiges of the qualities required, such as the ability to see the point of the question, to deploy information, or to offer some coherent analysis towards an argument. Such qualities will not be displayed at a high level or consistently, and will be marred by irrelevance, incoherence, error and poor organisation and presentation.

0 (fail)

Written work and the oral examination will both be failed if the oral examination clearly demonstrates that the work was plagiarised. The student is unfamiliar with the arguments of the assignment or the sources used for those arguments.

Plagiarism

Plagiarism is the use of someone else’s work without correct referencing. The consequence of plagiarism is the presentation of someone else’s work as your own work. Plagiarism violates Woolf policy and will result in disciplinary action, but the context and seriousness of plagiarism varies widely. Intentional or reckless plagiarism will result in a penalty grade of zero, and may also entail disciplinary penalties.

Plagiarism can be avoided by citing the works that inform or that are quoted in a written submission. Many students find that it is essential to keep their notes organised in relation to the sources which they summarise or quote. Course instructors will help you to cultivate professional scholarly habits in your academic writing.

Depending on the course, short assignment essays may not require students to submit a bibliography or to use extensive footnotes, and students are encouraged to write their assignments entirely in their own words. However, all essays must acknowledge the sources on which they rely and must provide quotation marks and citation information for verbatim quotes.

There are several forms of plagiarism. They all result in the presentation of someone’s prior work as your new creation. Examples include:

• Cutting and pasting (verbatim copying)
• Paraphrasing or rewording
• Unauthorised Collaboration
• Collaboration with other students can result in pervasive similarities – it is important to determine in advance whether group collaboration is allowed, and to acknowledge the contributions or influence of the group members.
• False Authorship (Essay Mills, Friends, and Language Help)
• Paying an essay writing service, or allowing a generous friend to compose your essay, is cheating. Assistance that contributes substantially to the ideas or content of your work must be acknowledged.

Complaints and appeals

Students and faculty should always seek an amicable resolution to matters arising by addressing the issue with the person immediately related to the issue. Students should handle minor misunderstandings or disagreements within a regular teaching session or by direct message, or with their College. If a simple resolution is not possible, or the matter remains unresolved for one party, the steps outlined in this section apply to all groups, colleges, and units of Woolf.

The Red Flag system

An issue with a red flag should be submitted in the case that a member of Woolf seeks to make an allegation of serious misconduct about another member, including matters of cheating, plagiarism, and unfair discrimination or intolerance.

Any member of Woolf, seeking to raise a matter of serious concern, should submit a red flag by emailing redflag@woolf.education. Provide a short, clear description of the issue.

If a student submits an issue with a red flag, or if a faculty member submits an issue about a student, it will trigger a meeting with the student’s College Advisor. If the issue is not resolved, the matter will be escalated to the College Dean, or to a committee designated by the College Dean, which will have the power to clear the flag.

If an issue is submitted with a red flag by a faculty member about another faculty member, then the issue is reported directly to the College Dean.

For both students and faculty members, after the Dean’s decision, the one who submits the complaint is provided the opportunity to accept or appeal the decision; if the one submitting the issue appeals the decision, it will be assigned to the Quality Assurance, Enhancement, and Technology Alignment Committee, which is a subcommittee of the Faculty Council.

Mitigating circumstances

When serious circumstances (‘Mitigating Circumstances’), beyond the control of a student or faculty member, adversely affect academic performance or teaching support, a Mitigating Circumstances report must be submitted using Woolf’s red flagging system. Mitigating Circumstances may include but are not limited to serious medical problems, domestic and personal circumstances, major accidents or interruptions of public services, disturbances during examination, or serious administrative or procedural errors with a material effect on outcomes.

Mitigating circumstances do not normally include a member’s personal technology problems, including software, hardware, or personal internet connection failures; employment obligations or changes in employment obligations; permanent or sustained medical conditions (unless there is a sudden change of condition); or circumstances where no official evidence has been submitted.

Mitigating circumstances are normally only considered when a red flag has been submitted for the issue before the deadline of an affected written project or assignment, or within one week of a cumulative examination. Proof of mitigating circumstances may result in an extended deadline or examination period, or the possibility to retake an examination; it will not result in any regrading of existing submissions or exams.

Grade appeals

Students who dissent from the grades they have received should follow the normal procedure for submitting a red flag.