Master of Science in Data Science
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Master of Science in Data Science
Downloads
About this degree
The course of study teaches students comprehensive and specialised subjects in data science; it develops sophisticated skills in statistics, mathematical modelling, and the ability to code in support of such analyses. It further grounds students in the disciplinary history and methodology of data science, preparing them for either further study or to work as a practitioner in the field. The programme prominently features a major capstone project, requiring students to identify a real-world problem that would benefit from a data-driven approach; to collect and prepare the data to address the problem; and to build visualisations in support of their arguments. The combination of rigorous mathematical training with practical approaches gives learners the ability to autonomously further develop their skills after graduation, turning them into lifelong learners of data science methods.
Target Audience
Ages 19-30, 31-65, 65+
Target Group
This course is designed for individuals who wish to enhance their knowledge of computer science and its various applications used in different fields of employment. It is designed for those that will have responsibility for planning, organizing, and directing technological operations. In all cases, the target group should be prepared to pursue substantial academic studies. Students must qualify for the course of study by entrance application. A prior computer science degree is not required; however the course does assume technical aptitude; and it targets students with finance, engineering, or STEM training or professional experience.
Mode of attendance
Online/Blended Learning
Structure of the programme
Please note that this structure may be subject to change based on faculty expertise and evolving academic best practices. This flexibility ensures we can provide the most up-to-date and effective learning experience for our students.The Master of Science in Computer Science combines asynchronous components (lecture videos, readings, and assignments) and synchronous meetings attended by students and a teacher during a video call. Asynchronous components support the schedule of students from diverse work-life situations, and synchronous meetings provide accountability and motivation for students. Students have direct access to their teacher and their peers at all times through the use of direct message and group chat; teachers are also able to initiate voice and video calls with students outside the regularly scheduled synchronous sessions. Modules are offered continuously on a publicly advertised schedule consisting of cohort sequences designed to accommodate adult students at different paces. Although there are few formal prerequisites identified throughout the programme, enrollment in courses depends on advisement from Woolf faculty and staff. The degree has 3 tiers. The first tier is required for all students, who must take 15 ECTS. In the second tier, students must select 45 ECTS from elective tiers. Tier Three may be completed in two different ways: a) by completing a 30ECTS Advanced Applied Computer Science capstone project, or b) by completing a 10 ECTS Applied Computer Science project and 20 ECTS of electives from the program.
Grading System
Scale: 0-100 points
Components: 60% of the mark derives from the average of the assignments, and 40% of the mark derives from the cumulative examination
Passing requirement: minimum of 60% overall
Dates of Next Intake
Rolling admission
Pass rates
2023 pass rates will be publicised in the next cycle, contingent upon ensuring sufficient student data for anonymization.
Identity Malta’s VISA requirement for third country nationals: https://www.identitymalta.com/unit/central-visa-unit/
Passing requirement: minimum of 60% overall
Dates of Next Intake
Rolling admission
Pass rates
2023 pass rates will be publicised in the next cycle, contingent upon ensuring sufficient student data for anonymization. Identity Malta’s VISA requirement for third country nationals: https://www.identitymalta.com/unit/central-visa-unit/
Master of Science in Data Science
Downloads
What you'll learn
- Students will develop advanced, innovative, and multi-disciplinary problem-solving skills,
- Students will communicate data science clearly and unambiguously to specialised and non-specialised audiences
- Students will develop advanced abilities related to data analytics operational procedures and the ability to implement them in response to changing environments.
- Students will critically evaluate alternative approaches to data science on the basis of academic scholarship and case studies, demonstrating reflection on social and ethical responsibilities.
- Students will formulate data-driven analytical judgments and plans despite incomplete information by integrating knowledge and approaches from diverse domains including statistical inference, machine learning, big data, computer vision, deep learning, and natural language processing.
- Students will produce work driven by research at the forefront of the domain of Data Science.
- Students will enquire critically into the theoretical strategies for applying data science and analytics within business and organizational contexts.
- Students will gain facility with modern tools for data analysis, from data visualisation tools such as Tableau through platforms for analysing massive amounts of unstructured data, such as Hadoop or MongoDB, and cloud architectures for data analysis.
- Students will develop new skills in response to emerging knowledge and techniques and demonstrate leadership skills and innovation in complex and unpredictable contexts
- Students will gain experience in working collaboratively on data science teams, including such skills as peer review, understanding contributor roles, and team dynamics.
Master of Science in Data Science
Downloads
Course Structure
About
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.
The module also builds on information covered in the module Exploratory Data Analysis to include hands-on applications of the summarization and visualisation of datasets through plots to present results in compelling and meaningful ways.
Teachers
Intended learning outcomes
- Industry applications of normality tests.
- Key strategies and best practices related to assessing the goodness of fit of a model.
- The step-by-step construction of regression models.
- Autonomously carry out global and individual testing of parameters used in defining predictive models.
- Test value assumptions using multiple predictors.
- Evaluate machine learning models on a limited data sample.
- Demonstrate self-direction in calculating inflation factors.
- Efficiently manage troubleshooting issues that arise in connection to data not explained by a model.
- Apply a professional and scholarly approach to real-world problems pertaining to the estimation of model parameters.
- Solve problems and be prepared to take leadership decisions related to the methods and correlation of variables.
About
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 will help 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 used for data science. The module culminates in practices related to data management, which is essential for both exploratory data analysis and advanced analytics. In particular, the module focuses on SQL as a highly practical language for data preprocessing, and addresses ways to connect SQL with R and Python tools, as well as learning the skills required to prepare data for machine learning and efficient data modelling.
Teachers
Intended learning outcomes
- Methods of distribution.
- Best practices used to visually display data.
- Key strategies related to the most appropriate measures of central tendency.
- Best practices related to data analysis and management, especially for large data sets.
- Autonomously gather material, including from large data sets, and organise it into effective visualisations for analysis.
- Accurately visualise and analyse data relationships. Autonomously connect SQL to R and Python to efficiently demonstrate data modelling processes through industry application.
- Assess symmetry of data using measures of skewness.
- Manage data sets using a variety of functions, including acting autonomously to identify problems and relevant solutions for data wrangling.
- Troubleshoot problems and be prepared to make leadership decisions related to industry methods and principles of data analysis and management.
- Import and export datasets and create data frames within R and Python, and connect these to SQL for preprocessing.
- Independently work in R, Python, and SQL development environments.
About
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.
The module will help learners to formulate research hypotheses, select appropriate tests of hypotheses, write primarily R programs to perform hypothesis testing and to draw inferences using the output generated. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analysing data.
Teachers
Intended learning outcomes
- Discrete and continuous random variables.
- Select topics for the advanced management of parametric and non-parametric tests.
- The relevance of R to calculate probabilities.
- Key strategies related to distributions of observed data.
- Understand and use statistical hypothesis testing concepts and terminology.
- Autonomously perform tests for normality and common distribution.
- Analyse data relationships using covariance.
- Evaluate standard types of distributions.
- Efficiently analyse the concept of variance through a variety of models.
- Demonstrate self-direction and industry practices in developing solutions for hypothesis testing.
About
The ability to render large data sets intelligible, especially in visual means and to potentially nonexpert audiences, is a core part of data science. Building from the introduction provided in 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.
At the end of Data visualisation, students will have developed strategies for making visible both subtle details and large patterns, and for telling visual stories with data.
Teachers
Intended learning outcomes
- Key strategies related to designing to facilitate human cognition.
- Select topics related to domain-specific data analytic tasks.
- Theories and contemporary practices in data visualisation.
- Theories of the ethical visual presentation of statistical evidence.
- Autonomously create a variety of visual representations of statistical evidence, ranging from simple tables and charts to complex maps, clouds, and networks.
- Employ the standard modern tools to perform data visualisation.
- Autonomously solve problems in the understanding of complex statistical information.
- Solve problems related to the visual presentation of statistical evidence in a variety of contemporary tools.
- Act autonomously in identifying research problems and solutions related to making raw data intelligible.
- Apply a professional and scholarly approach to research problems pertaining to data visualisation.
- Demonstrate self-direction in research and originality in solutions developed for visualizing large data sets.
About
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.
Teachers
Intended learning outcomes
- Comparing data to a known distribution.
- The implementation of binomial regression in real world settings.
- Determining if a sample follows a normal distribution.
- Develop models using one or more predictor variables to predict the target variable classes.
- Develop applications using more than two categories of dependent, outcome, or explanatory variables.
- Critically assess the effect of several variables upon the time a specified result takes to occur.
- Efficiently estimate model parameters.
- Demonstrate self-direction in global hypothesis testing.
- Act autonomously in developing estimates of unknown population parameters.
- Solve problems related to generalised linear models through link function.
About
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.
Teachers
Intended learning outcomes
- The industry relevance of the apriori algorithm.
- Decision boundaries that help classify data points.
- Regression models with binary target variables.
- Models intended to predict the value of a target variable.
- Appraise classification methods and the support vector machine algorithm.
- Use algorithims to make predictions and apply neutral networks to classification problems.
- Apply decision tree and random forest algorithms to classification and regression problems.
- Demonstrate self-direction in bootstrapping and aggregation.
- Act autonomously in identifying neutral networks for classification problems.
- Apply a professional and scholarly approach to Bayes theorem and its applications.
- Efficiently manage issues in connection to machine algorithms.
About
This unit 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.
If a student is pursuing either specialisation A or B, the Data Science in Practice work should deepen their engagement with this material.
Teachers
Intended learning outcomes
- Key strategies related to statistical modelling and predictive analytics.
- Theories and contemporary practices in data analytics.
- Select topics related to industry-specific uses of programming in R, Python, and MySQL, as well as data visualisation tools.
- Employ statistical modelling and predictive analytics within real-world business contexts
- Autonomously identify opportunities for the use of Python, R, MySQL, and data visualisation tools.
- Autonomously solve problems in the domain of data analytics.
- Demonstrate self-direction in research and originality in addressing statistical analysis and predictive modelling.
- Apply a professional and scholarly approach to data analytics within a real-world context.
- Solve problems related to the use of programming and data modelling in real-world applications
- Act autonomously in identifying research problems and solutions related to data visualisation and analytics.
About
This course introduces learners to the fundamentals of Generative Artificial Intelligence (AI) and its growing role in shaping modern technology and innovation. It covers the basic concepts, processes, and structures that form the Generative AI stack, including data, models, and tools. Learners will explore a range of applications and use cases across different fields such as business, education, healthcare, and creative industries. The course also examines adoption strategies, challenges, and opportunities for organisations and individuals. Ethical and social considerations are highlighted to encourage responsible development and usage of AI systems. Through interactive discussions and practical examples, students will develop an understanding of current trends and future directions in Generative AI. By the end of the course, learners will be able to evaluate and apply Generative AI concepts in diverse settings. This serves as a foundation for further study and exploration in the field of AI.
Teachers
Intended learning outcomes
- Demonstrates specialised or multi-disciplinary knowledge of Generative AI by reflecting on social, legal, and ethical responsibilities linked to its development, deployment, and outcomes.
- Has comprehensive knowledge and understanding of Generative AI concepts, tools, and applications that is founded upon and/or enhances knowledge typically associated with Bachelor’s level.
- Uses specialised or multi-disciplinary theoretical and practical knowledge of Generative AI, including its stack, enterprise solutions, and emerging innovations, some of which are at the forefront of the field.
- Develops new skills in response to emerging Generative AI models, frameworks, and techniques, demonstrating leadership and innovation in complex, rapidly evolving environments for work and study.
- Can communicate to specialist and non-specialist audiences clearly and unambiguously, presenting insights, analyses, and conclusions on Generative AI applications, whether derived from research, self-study, or practical experience.
- Performs critical evaluations and analysis of Generative AI systems and their impacts using incomplete or limited information, solving problems in new or unfamiliar domains and contributing to original research or innovation.
- Demonstrates autonomy in the direction of learning and a high level of understanding in the domain of Generative AI.
- Creates a research-based diagnosis of problems by integrating knowledge of Generative AI with other interdisciplinary domains and makes judgements in situations with incomplete or limited data.
- Demonstrates autonomy in directing learning related to Generative AI and exhibits a deep understanding of advanced learning processes to stay current in this dynamic field.
- Manages Generative AI projects and teams, demonstrating the ability to adapt and respond effectively to the fast-changing AI-driven business and technology landscape.
About
PowerBI and Excel are fundamental parts of the data analytics toolkit. A strong understanding in these also provides a basis for more advanced data analytics with other techniques and technologies. In this unit, learners will gain experience in collecting, processing, analysing, and communicating with data using Excel. In addition, data visualisation is a powerful way to communicate meaning in data and support business decision-making. This unit will cover 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 and use them appropriately in context.
Teachers
Intended learning outcomes
- Select topics related to industry-specific uses of PowerBI
- Key strategies related to deploying data in business operations and management
- Theories and contemporary practices in business analytics.
- Employ Excel–including tools such as pivot tables, and basic visualisations–and PowerBI to surface insights about business operations.
- Autonomously identify opportunities for the use of Excel and PowerBI in business contexts.
- Autonomously solve problems in the domain of visualizing business data.
- Act autonomously in identifying research problems and solutions related to applications of Excel and PowerBI for analytics.
- Demonstrate self-direction in research and originality in addressing the availability of data for business operations.
- Apply a professional and scholarly approach to data analytics within a business context.
- Solve problems related to the use of dashboards and visualisations for business management
About
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.
Current Topic
Data Mining and Social Media
Thirty years ago, people used to say “on the internet, no one knows you’re a dog.” Using the analytic and inferential tools of social media data mining, however, 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. A wide variety of organizations, from law enforcement to advertisers to academic researchers and public policy makers, apply data mining techniques to social media to learn more about the public.
This course will focus on practical methods for scraping and analyzing social media data, as well as some theoretical implications of these practices.
Teachers
Intended learning outcomes
- Theories and contemporary practices in data mining.
- Select topics related to industry-specific efforts to mine social media.
- Theories of data mining and social media.
- Key strategies related to deploying data mining to social media.
- Employ Python code, Jupyter notebooks, and Docker containers to mine social media networks for actionable insights.
- Autonomously solve problems in the domain of social media data mining.
- Autonomously identify key ethical concerns that can arise with data mining social media.
- Apply a professional and scholarly approach to data mining within a social media context.
- Demonstrate self-direction in research and originality in addressing the legal/regulatory and ethical implications of data mining social media.
- Act autonomously in identifying research problems and solutions related to applications of data mining to social media.
- Solve problems related to the use of metadata in the context of massively popular platforms.
About
Machine learning algorithms are new generation algorithms used in conjunction with classical predictive modelling methods.
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.
Teachers
Intended learning outcomes
- Decision boundaries that help classify data points.
- Regression models with binary target variables.
- The industry relevance of the apriori algorithm.
- Models intended to predict the value of a target variable.
- Appraise classification methods and the support vector machine algorithm.
- Use algorithms to make predictions and apply neutral networks to classification problems.
- Apply decision tree and random forest algorithms to classification and regression problems.
- Act autonomously in identifying neutral networks for classification problems.
- Demonstrate self-direction in bootstrapping and aggregation.
- Efficiently manage issues in connection to decision tree and random forest machine learning algorithms.
- Apply a professional and scholarly approach to Binary Logistic Regression and its applications.
About
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.
Teachers
Intended learning outcomes
- Key strategies related to structured data versus unstructured data and the features of each.
- Principles and applications of sentiment analysis.
- Principles and applications of text analysis.
- Industry applications in the domain of language processing.
- Perform sentiment analysis on unstructured data.
- Process text data to generate insights.
- Process text data and strings, and perform pattern matching with expressions in R and Python.
- Demonstrate self-direction in applying solutions related to text mining.
- Efficiently manage issues that arise in connection to text mining.
- Apply a professional and scholarly approach to research problems pertaining to natural language processing.
About
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. All external expert-supervisors and projects need to be approved by the instructor(s) to ensure that the analytic question is appropriately scoped and technically challenging, and that the solutions are rigorous and of high quality.
Students are required to solve an analytically complex research problem. Once the problem has been approved by the instructor(s), the student conducts a literature review of prior work in the field. Then, they conduct an 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. Depending on the problem, the students may build dashboards or other artifacts as part of this work.
A key part of the project is to communicate the output of the learner’s research to technical and non-technical audiences through written, verbal and visual means.
Teachers
Intended learning outcomes
- Propose appropriate solutions to complex and changing problems pertaining to data analytics.
- Assess, analyse, and criticise the various strategies for handling matters arising in the context of real-world data analytics problems.
- Compare and evaluate the different methodologies recommended in scholarly sources pertaining to how industry leaders should address issues of data analytics and machine learning.
About
This unit 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.
If a student is pursuing either specialisation A or B, the Data Science in Practice work should deepen their engagement with this material.
Teachers
Intended learning outcomes
- Key strategies related to statistical modelling and predictive analytics.
- Theories and contemporary practices in data analytics.
- Select topics related to industry-specific uses of programming in R, Python, and MySQL, as well as data visualisation tools.
- Employ statistical modelling and predictive analytics within real-world business contexts
- Autonomously identify opportunities for the use of Python, R, MySQL, and data visualisation tools.
- Autonomously solve problems in the domain of data analytics.
- Demonstrate self-direction in research and originality in addressing statistical analysis and predictive modelling.
- Apply a professional and scholarly approach to data analytics within a real-world context.
- Solve problems related to the use of programming and data modelling in real-world applications
- Act autonomously in identifying research problems and solutions related to data visualisation and analytics.
About
PowerBI and Excel are fundamental parts of the data analytics toolkit. A strong understanding in these also provides a basis for more advanced data analytics with other techniques and technologies. In this unit, learners will gain experience in collecting, processing, analysing, and communicating with data using Excel. In addition, data visualisation is a powerful way to communicate meaning in data and support business decision-making. This unit will cover 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 and use them appropriately in context.
Teachers
Intended learning outcomes
- Select topics related to industry-specific uses of PowerBI
- Key strategies related to deploying data in business operations and management
- Theories and contemporary practices in business analytics.
- Employ Excel–including tools such as pivot tables, and basic visualisations–and PowerBI to surface insights about business operations.
- Autonomously identify opportunities for the use of Excel and PowerBI in business contexts.
- Autonomously solve problems in the domain of visualizing business data.
- Act autonomously in identifying research problems and solutions related to applications of Excel and PowerBI for analytics.
- Demonstrate self-direction in research and originality in addressing the availability of data for business operations.
- Apply a professional and scholarly approach to data analytics within a business context.
- Solve problems related to the use of dashboards and visualisations for business management
About
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.
Current Topic
Data Mining and Social Media
Thirty years ago, people used to say “on the internet, no one knows you’re a dog.” Using the analytic and inferential tools of social media data mining, however, 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. A wide variety of organizations, from law enforcement to advertisers to academic researchers and public policy makers, apply data mining techniques to social media to learn more about the public.
This course will focus on practical methods for scraping and analyzing social media data, as well as some theoretical implications of these practices.
Teachers
Intended learning outcomes
- Theories and contemporary practices in data mining.
- Select topics related to industry-specific efforts to mine social media.
- Theories of data mining and social media.
- Key strategies related to deploying data mining to social media.
- Employ Python code, Jupyter notebooks, and Docker containers to mine social media networks for actionable insights.
- Autonomously solve problems in the domain of social media data mining.
- Autonomously identify key ethical concerns that can arise with data mining social media.
- Apply a professional and scholarly approach to data mining within a social media context.
- Demonstrate self-direction in research and originality in addressing the legal/regulatory and ethical implications of data mining social media.
- Act autonomously in identifying research problems and solutions related to applications of data mining to social media.
- Solve problems related to the use of metadata in the context of massively popular platforms.
About
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.
Teachers
Intended learning outcomes
- Comparing data to a known distribution.
- The implementation of binomial regression in real world settings.
- Determining if a sample follows a normal distribution.
- Develop models using one or more predictor variables to predict the target variable classes.
- Develop applications using more than two categories of dependent, outcome, or explanatory variables.
- Critically assess the effect of several variables upon the time a specified result takes to occur.
- Efficiently estimate model parameters.
- Demonstrate self-direction in global hypothesis testing.
- Act autonomously in developing estimates of unknown population parameters.
- Solve problems related to generalised linear models through link function.
About
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.
Teachers
Intended learning outcomes
- The industry relevance of the apriori algorithm.
- Decision boundaries that help classify data points.
- Regression models with binary target variables.
- Models intended to predict the value of a target variable.
- Appraise classification methods and the support vector machine algorithm.
- Use algorithims to make predictions and apply neutral networks to classification problems.
- Apply decision tree and random forest algorithms to classification and regression problems.
- Demonstrate self-direction in bootstrapping and aggregation.
- Act autonomously in identifying neutral networks for classification problems.
- Apply a professional and scholarly approach to Bayes theorem and its applications.
- Efficiently manage issues in connection to machine algorithms.
About
The ability to render large data sets intelligible, especially in visual means and to potentially nonexpert audiences, is a core part of data science. Building from the introduction provided in 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.
At the end of Data visualisation, students will have developed strategies for making visible both subtle details and large patterns, and for telling visual stories with data.
Teachers
Intended learning outcomes
- Key strategies related to designing to facilitate human cognition.
- Select topics related to domain-specific data analytic tasks.
- Theories and contemporary practices in data visualisation.
- Theories of the ethical visual presentation of statistical evidence.
- Autonomously create a variety of visual representations of statistical evidence, ranging from simple tables and charts to complex maps, clouds, and networks.
- Employ the standard modern tools to perform data visualisation.
- Autonomously solve problems in the understanding of complex statistical information.
- Solve problems related to the visual presentation of statistical evidence in a variety of contemporary tools.
- Act autonomously in identifying research problems and solutions related to making raw data intelligible.
- Apply a professional and scholarly approach to research problems pertaining to data visualisation.
- Demonstrate self-direction in research and originality in solutions developed for visualizing large data sets.
About
This course introduces learners to the fundamentals of Generative Artificial Intelligence (AI) and its growing role in shaping modern technology and innovation. It covers the basic concepts, processes, and structures that form the Generative AI stack, including data, models, and tools. Learners will explore a range of applications and use cases across different fields such as business, education, healthcare, and creative industries. The course also examines adoption strategies, challenges, and opportunities for organisations and individuals. Ethical and social considerations are highlighted to encourage responsible development and usage of AI systems. Through interactive discussions and practical examples, students will develop an understanding of current trends and future directions in Generative AI. By the end of the course, learners will be able to evaluate and apply Generative AI concepts in diverse settings. This serves as a foundation for further study and exploration in the field of AI.
Teachers
Intended learning outcomes
- Demonstrates specialised or multi-disciplinary knowledge of Generative AI by reflecting on social, legal, and ethical responsibilities linked to its development, deployment, and outcomes.
- Has comprehensive knowledge and understanding of Generative AI concepts, tools, and applications that is founded upon and/or enhances knowledge typically associated with Bachelor’s level.
- Uses specialised or multi-disciplinary theoretical and practical knowledge of Generative AI, including its stack, enterprise solutions, and emerging innovations, some of which are at the forefront of the field.
- Develops new skills in response to emerging Generative AI models, frameworks, and techniques, demonstrating leadership and innovation in complex, rapidly evolving environments for work and study.
- Can communicate to specialist and non-specialist audiences clearly and unambiguously, presenting insights, analyses, and conclusions on Generative AI applications, whether derived from research, self-study, or practical experience.
- Performs critical evaluations and analysis of Generative AI systems and their impacts using incomplete or limited information, solving problems in new or unfamiliar domains and contributing to original research or innovation.
- Demonstrates autonomy in the direction of learning and a high level of understanding in the domain of Generative AI.
- Creates a research-based diagnosis of problems by integrating knowledge of Generative AI with other interdisciplinary domains and makes judgements in situations with incomplete or limited data.
- Demonstrates autonomy in directing learning related to Generative AI and exhibits a deep understanding of advanced learning processes to stay current in this dynamic field.
- Manages Generative AI projects and teams, demonstrating the ability to adapt and respond effectively to the fast-changing AI-driven business and technology landscape.
About
This unit 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.
If a student is pursuing either specialisation A or B, the Data Science in Practice work should deepen their engagement with this material.
Teachers
Intended learning outcomes
- Key strategies related to statistical modelling and predictive analytics.
- Theories and contemporary practices in data analytics.
- Select topics related to industry-specific uses of programming in R, Python, and MySQL, as well as data visualisation tools.
- Employ statistical modelling and predictive analytics within real-world business contexts
- Autonomously identify opportunities for the use of Python, R, MySQL, and data visualisation tools.
- Autonomously solve problems in the domain of data analytics.
- Demonstrate self-direction in research and originality in addressing statistical analysis and predictive modelling.
- Apply a professional and scholarly approach to data analytics within a real-world context.
- Solve problems related to the use of programming and data modelling in real-world applications
- Act autonomously in identifying research problems and solutions related to data visualisation and analytics.
About
Machine learning algorithms are new generation algorithms used in conjunction with classical predictive modelling methods.
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.
Teachers
Intended learning outcomes
- Decision boundaries that help classify data points.
- Regression models with binary target variables.
- The industry relevance of the apriori algorithm.
- Models intended to predict the value of a target variable.
- Appraise classification methods and the support vector machine algorithm.
- Use algorithms to make predictions and apply neutral networks to classification problems.
- Apply decision tree and random forest algorithms to classification and regression problems.
- Act autonomously in identifying neutral networks for classification problems.
- Demonstrate self-direction in bootstrapping and aggregation.
- Efficiently manage issues in connection to decision tree and random forest machine learning algorithms.
- Apply a professional and scholarly approach to Binary Logistic Regression and its applications.
About
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.
Teachers
Intended learning outcomes
- Key strategies related to structured data versus unstructured data and the features of each.
- Principles and applications of sentiment analysis.
- Principles and applications of text analysis.
- Industry applications in the domain of language processing.
- Perform sentiment analysis on unstructured data.
- Process text data to generate insights.
- Process text data and strings, and perform pattern matching with expressions in R and Python.
- Demonstrate self-direction in applying solutions related to text mining.
- Efficiently manage issues that arise in connection to text mining.
- Apply a professional and scholarly approach to research problems pertaining to natural language processing.
About
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.
Teachers
Intended learning outcomes
- Comparing data to a known distribution.
- The implementation of binomial regression in real world settings.
- Determining if a sample follows a normal distribution.
- Develop models using one or more predictor variables to predict the target variable classes.
- Develop applications using more than two categories of dependent, outcome, or explanatory variables.
- Critically assess the effect of several variables upon the time a specified result takes to occur.
- Efficiently estimate model parameters.
- Demonstrate self-direction in global hypothesis testing.
- Act autonomously in developing estimates of unknown population parameters.
- Solve problems related to generalised linear models through link function.
About
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.
Teachers
Intended learning outcomes
- The industry relevance of the apriori algorithm.
- Decision boundaries that help classify data points.
- Regression models with binary target variables.
- Models intended to predict the value of a target variable.
- Appraise classification methods and the support vector machine algorithm.
- Use algorithims to make predictions and apply neutral networks to classification problems.
- Apply decision tree and random forest algorithms to classification and regression problems.
- Demonstrate self-direction in bootstrapping and aggregation.
- Act autonomously in identifying neutral networks for classification problems.
- Apply a professional and scholarly approach to Bayes theorem and its applications.
- Efficiently manage issues in connection to machine algorithms.
About
The ability to render large data sets intelligible, especially in visual means and to potentially nonexpert audiences, is a core part of data science. Building from the introduction provided in 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.
At the end of Data visualisation, students will have developed strategies for making visible both subtle details and large patterns, and for telling visual stories with data.
Teachers
Intended learning outcomes
- Key strategies related to designing to facilitate human cognition.
- Select topics related to domain-specific data analytic tasks.
- Theories and contemporary practices in data visualisation.
- Theories of the ethical visual presentation of statistical evidence.
- Autonomously create a variety of visual representations of statistical evidence, ranging from simple tables and charts to complex maps, clouds, and networks.
- Employ the standard modern tools to perform data visualisation.
- Autonomously solve problems in the understanding of complex statistical information.
- Solve problems related to the visual presentation of statistical evidence in a variety of contemporary tools.
- Act autonomously in identifying research problems and solutions related to making raw data intelligible.
- Apply a professional and scholarly approach to research problems pertaining to data visualisation.
- Demonstrate self-direction in research and originality in solutions developed for visualizing large data sets.
About
This course introduces learners to the fundamentals of Generative Artificial Intelligence (AI) and its growing role in shaping modern technology and innovation. It covers the basic concepts, processes, and structures that form the Generative AI stack, including data, models, and tools. Learners will explore a range of applications and use cases across different fields such as business, education, healthcare, and creative industries. The course also examines adoption strategies, challenges, and opportunities for organisations and individuals. Ethical and social considerations are highlighted to encourage responsible development and usage of AI systems. Through interactive discussions and practical examples, students will develop an understanding of current trends and future directions in Generative AI. By the end of the course, learners will be able to evaluate and apply Generative AI concepts in diverse settings. This serves as a foundation for further study and exploration in the field of AI.
Teachers
Intended learning outcomes
- Demonstrates specialised or multi-disciplinary knowledge of Generative AI by reflecting on social, legal, and ethical responsibilities linked to its development, deployment, and outcomes.
- Has comprehensive knowledge and understanding of Generative AI concepts, tools, and applications that is founded upon and/or enhances knowledge typically associated with Bachelor’s level.
- Uses specialised or multi-disciplinary theoretical and practical knowledge of Generative AI, including its stack, enterprise solutions, and emerging innovations, some of which are at the forefront of the field.
- Develops new skills in response to emerging Generative AI models, frameworks, and techniques, demonstrating leadership and innovation in complex, rapidly evolving environments for work and study.
- Can communicate to specialist and non-specialist audiences clearly and unambiguously, presenting insights, analyses, and conclusions on Generative AI applications, whether derived from research, self-study, or practical experience.
- Performs critical evaluations and analysis of Generative AI systems and their impacts using incomplete or limited information, solving problems in new or unfamiliar domains and contributing to original research or innovation.
- Demonstrates autonomy in the direction of learning and a high level of understanding in the domain of Generative AI.
- Creates a research-based diagnosis of problems by integrating knowledge of Generative AI with other interdisciplinary domains and makes judgements in situations with incomplete or limited data.
- Demonstrates autonomy in directing learning related to Generative AI and exhibits a deep understanding of advanced learning processes to stay current in this dynamic field.
- Manages Generative AI projects and teams, demonstrating the ability to adapt and respond effectively to the fast-changing AI-driven business and technology landscape.
Master of Science in Data Science
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