Master of Science in Data Science

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.

The course helps students develop an appreciation for programming as a problem-solving tool. It teaches students how to think algorithmically and solve problems efficiently, and serves as the foundation for further computer science studies. Using a project-based approach, students will learn to manipulate variables, expressions, and statements in Python, and understand functions, loops, and iterations. Students will then dive deep into data structures such as strings, files, lists, dictionaries, tuples, etc. to write complex programs. Over the course of the term, students will learn and apply basic data structures and algorithmic thinking.

Finally, the course will explore design and implementation of web apps in Python using the Flask framework.

Throughout the course, students will be exposed to abstraction and will learn a systematic way of constructing solutions to problems. They will work on team projects to practice pair programming, code reviews, and other collaboration methods common to industry. The course culminates in a final group project and presentation during which students demonstrate and reflect on their learning.

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.

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. Data management tasks will involve cleaning and preprocessing the data, as well as storing and organizing it in a way that is efficient and easy to access. Exploratory data analysis will involve using statistical techniques to understand the data, such as identifying patterns, trends, and outliers. Data visualization will involve creating visualizations of the data, such as charts, graphs, and maps, to help communicate the findings of the analysis. Predictive modeling will involve using machine learning techniques to build models that can predict future outcomes.

The technologies and modes of analysis behind big data yield great promise in addressing social problems in various domains such as healthcare and terrorism; at the same time, their most frequent deployment is in adtech. Massive data sets often promise that they are anonymized, yet researchers continually discover how easily individual identity can be reconstructed from seemingly incongruent data points.

This course addresses modern debates and regulations around privacy and the ethical use of data. Addressing equally ethical and theoretical considerations as well as practical/applied exercises in data mining.

This course will provide an introduction to the fundamentals of deep learning. Deep learning is a branch of machine learning that uses artificial neural networks to learn from data. Neural networks are inspired by the human brain, and they are able to learn complex patterns in data that would be difficult or impossible to learn using traditional machine learning techniques. Concepts will include the basics of neural networks, different types of neural networks, mathematics of deep learning, programming frameworks for deep learning and the application of deep learning to real-world problems. Students will learn the fundamental concepts of deep learning, and they will gain hands-on experience with implementing neural networks in Python. The course will also cover the application of deep learning to real-world problems. By the end of this course, students will be able to explain the basic concepts of deep learning, implement neural networks in Python and apply deep learning to real-world problems.

Big Data involves the computational treatment of massively large data sets in order to gain actionable insights. It is changing myriad aspects of research and business, from astronomy to recommendation engines to the study of literary history.

Big Data and Its Applications introduces some of the algorithms, statistical models, and computing tools most common in data science today, as well as the demonstration the value of these for specific applications. Tools to be covered will vary, but will certainly include Hadoop and Apache Spark, as well as platforms such as Amazon AWS, Microsoft Azure, and Google Cloud.

The course will focus on data reduction and information extraction, as well as the ability to perform regression analysis on massive data sets, and the ability to work effectively in distributed/cloud environments.

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.

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.

This module provides extensive knowledge of splitting data into training, validating, and creating test sets. Develop and assess predictive mining models by integrating a framework and practical perception. There are numerous performance metrics for estimation and categorisation systems presented. The most prevalent predictive modelling approaches, including artificial neural networks, support vector machines, k-nearest neighbour, Bayesian learning, ensemble models, and different decision trees, are reviewed in this module, along with their internal workings, capabilities, and applications. Most of these strategies can tackle prediction difficulties of the classification and regression kinds. They are commonly employed to address challenging prediction challenges when other, more traditional approaches fail to deliver results.

This module addresses the principles of creating reliable spreadsheet models, translating conceptual models into mathematical models, and applying them in spreadsheets. It also demonstrates a knowledge of three analytic tools in Excel, Excel functions, and the process of auditing spreadsheet models to assure accuracy. Additionally covered in this module are Decision analysis, Payoff Tables, and Decision Trees. Microsoft Power BI helps users derive practical knowledge from data to solve business concerns, bringing analytical models to corporate decision-making. Learners acquire insight into advanced analytic features of Power BI, such as prediction, data visualizations, and data analysis expressions.

Database Management module explains the fundamentals of database design, creation, and administration. The course covers topics such as the relational data model, database normalization, SQL, and database security. Students will learn how to design and create databases that are accurate, secure, and efficient. They will also learn how to administer databases, including tasks such as backup and recovery, performance tuning, and security management. Database management makes data more accessible to users. This is because the data is stored in a central location and can be accessed by authorized users through a variety of applications. The module also teaches is essential in information technology, particularly in the field of database administration. It is also a valuable course for students who want to learn more about how databases work and how to use them to store and manage data. By the end of the module, students will have gained a comprehensive understanding of database management systems and their importance in AI and ML applications. They will be able to identify different types of database management systems and their components and apply the concepts of database normalization to design and develop efficient databases. This knowledge will prepare them for more advanced courses in the curriculum and for database management roles in the industry.

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.

This module will provide learners with knowledge and understanding of the application of machine learning methodologies to handle industrial difficulties, to a more extensive array of data mining and classification type activities. Learners will discover the machine learning algorithms by utilizing neural networks, k-means clustering, and support vector machines in computer vision to analyse data based on supervised, unsupervised, and partially supervised. Additionally covered in this module are, Tensor flow, Faster- RCNN-Inception-V2 model, and Anaconda software development environment utilized to recognize autos and individuals in pictures that provides insight into the usage of current deep learning network models like CNN.

Upon completion of this course, you will gain a deep understanding of how business analytics supports data-driven decision-making in an evolving business landscape. You will explore key analytics frameworks, learning how organisations leverage data to navigate uncertainty and drive strategic growth. Through practical applications, you will differentiate between various data-driven techniques and examine their real-world implementation across industries such as banking and healthcare. Additionally, you will critically assess the challenges and ethical considerations of integrating analytics tools into business processes, equipping you to apply these insights effectively in your organisation.

In this course, you will develop the strategic awareness and practical skills needed to lead digital transformation effectively within your organisation. You will explore the drivers of digital disruption, learn how to critically assess emerging technologies, and understand how to deliver transformation projects that align with organisational goals. You will also gain essential insights into cyber risk: how

to anticipate, mitigate, and respond to threats, and learn how to embed cyber resilience into your leadership approach. Through case studies, frameworks, and reflection exercises, you will build the confidence to lead digital initiatives in an informed, strategic, and future-ready way.

Upon completion of this programme, you will develop fluency in the fundamental frameworks and analytical tools needed to effectively assess an organisation's strategic landscape. Through a blend of theoretical exploration and practical application, you'll gain the ability to develop insightful strategic recommendations for organisational success. Additionally, you will develop the knowledge and skills to analyse and improve how work is performed in your organisation.

Upon completion of this programme, you will develop a customer-centric and future-oriented marketing mindset to promote sustainable growth in your organisation, or organisations you might work with in the future. Additionally, you will delve into the foundational topic of finance and economics-valuation. You will gain a comprehensive understanding of how key concepts are applied in financial decision-making and investment strategies.

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.

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. Data management tasks will involve cleaning and preprocessing the data, as well as storing and organizing it in a way that is efficient and easy to access. Exploratory data analysis will involve using statistical techniques to understand the data, such as identifying patterns, trends, and outliers. Data visualization will involve creating visualizations of the data, such as charts, graphs, and maps, to help communicate the findings of the analysis. Predictive modeling will involve using machine learning techniques to build models that can predict future outcomes.

The technologies and modes of analysis behind big data yield great promise in addressing social problems in various domains such as healthcare and terrorism; at the same time, their most frequent deployment is in adtech. Massive data sets often promise that they are anonymized, yet researchers continually discover how easily individual identity can be reconstructed from seemingly incongruent data points.

This course addresses modern debates and regulations around privacy and the ethical use of data. Addressing equally ethical and theoretical considerations as well as practical/applied exercises in data mining.

Big Data involves the computational treatment of massively large data sets in order to gain actionable insights. It is changing myriad aspects of research and business, from astronomy to recommendation engines to the study of literary history.

Big Data and Its Applications introduces some of the algorithms, statistical models, and computing tools most common in data science today, as well as the demonstration the value of these for specific applications. Tools to be covered will vary, but will certainly include Hadoop and Apache Spark, as well as platforms such as Amazon AWS, Microsoft Azure, and Google Cloud.

The course will focus on data reduction and information extraction, as well as the ability to perform regression analysis on massive data sets, and the ability to work effectively in distributed/cloud environments.

This module addresses the principles of creating reliable spreadsheet models, translating conceptual models into mathematical models, and applying them in spreadsheets. It also demonstrates a knowledge of three analytic tools in Excel, Excel functions, and the process of auditing spreadsheet models to assure accuracy. Additionally covered in this module are Decision analysis, Payoff Tables, and Decision Trees. Microsoft Power BI helps users derive practical knowledge from data to solve business concerns, bringing analytical models to corporate decision-making. Learners acquire insight into advanced analytic features of Power BI, such as prediction, data visualizations, and data analysis expressions.

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.

The technologies and modes of analysis behind big data yield great promise in addressing social problems in various domains such as healthcare and terrorism; at the same time, their most frequent deployment is in adtech. Massive data sets often promise that they are anonymized, yet researchers continually discover how easily individual identity can be reconstructed from seemingly incongruent data points.

This course addresses modern debates and regulations around privacy and the ethical use of data. Addressing equally ethical and theoretical considerations as well as practical/applied exercises in data mining.

This course will provide an introduction to the fundamentals of deep learning. Deep learning is a branch of machine learning that uses artificial neural networks to learn from data. Neural networks are inspired by the human brain, and they are able to learn complex patterns in data that would be difficult or impossible to learn using traditional machine learning techniques. Concepts will include the basics of neural networks, different types of neural networks, mathematics of deep learning, programming frameworks for deep learning and the application of deep learning to real-world problems. Students will learn the fundamental concepts of deep learning, and they will gain hands-on experience with implementing neural networks in Python. The course will also cover the application of deep learning to real-world problems. By the end of this course, students will be able to explain the basic concepts of deep learning, implement neural networks in Python and apply deep learning to real-world problems.

Big Data involves the computational treatment of massively large data sets in order to gain actionable insights. It is changing myriad aspects of research and business, from astronomy to recommendation engines to the study of literary history.

Big Data and Its Applications introduces some of the algorithms, statistical models, and computing tools most common in data science today, as well as the demonstration the value of these for specific applications. Tools to be covered will vary, but will certainly include Hadoop and Apache Spark, as well as platforms such as Amazon AWS, Microsoft Azure, and Google Cloud.

The course will focus on data reduction and information extraction, as well as the ability to perform regression analysis on massive data sets, and the ability to work effectively in distributed/cloud environments.

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.

This module provides extensive knowledge of splitting data into training, validating, and creating test sets. Develop and assess predictive mining models by integrating a framework and practical perception. There are numerous performance metrics for estimation and categorisation systems presented. The most prevalent predictive modelling approaches, including artificial neural networks, support vector machines, k-nearest neighbour, Bayesian learning, ensemble models, and different decision trees, are reviewed in this module, along with their internal workings, capabilities, and applications. Most of these strategies can tackle prediction difficulties of the classification and regression kinds. They are commonly employed to address challenging prediction challenges when other, more traditional approaches fail to deliver results.

This module provides extensive knowledge of splitting data into training, validating, and creating test sets. Develop and assess predictive mining models by integrating a framework and practical perception. There are numerous performance metrics for estimation and categorization systems presented. The most prevalent predictive modelling approaches, including artificial neural networks, support vector machines, k-nearest neighbour, Bayesian learning, ensemble models, and different decision trees, are reviewed in this module, along with their internal workings, capabilities, and applications. Most of these strategies can tackle prediction difficulties of the classification and regression kinds. They are commonly employed to address challenging prediction challenges when other, more traditional approaches fail to deliver results.

The technologies and modes of analysis behind big data yield great promise in addressing social problems in various domains such as healthcare and terrorism; at the same time, their most frequent deployment is in adtech. Massive data sets often promise that they are anonymized, yet researchers continually discover how easily individual identity can be reconstructed from seemingly incongruent data points.

This course addresses modern debates and regulations around privacy and the ethical use of data. Addressing equally ethical and theoretical considerations as well as practical/applied exercises in data mining.

Big Data involves the computational treatment of massively large data sets in order to gain actionable insights. It is changing myriad aspects of research and business, from astronomy to recommendation engines to the study of literary history.

Big Data and Its Applications introduces some of the algorithms, statistical models, and computing tools most common in data science today, as well as the demonstration the value of these for specific applications. Tools to be covered will vary, but will certainly include Hadoop and Apache Spark, as well as platforms such as Amazon AWS, Microsoft Azure, and Google Cloud.

The course will focus on data reduction and information extraction, as well as the ability to perform regression analysis on massive data sets, and the ability to work effectively in distributed/cloud environments.

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.

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.

This module provides extensive knowledge of splitting data into training, validating, and creating test sets. Develop and assess predictive mining models by integrating a framework and practical perception. There are numerous performance metrics for estimation and categorization systems presented. The most prevalent predictive modelling approaches, including artificial neural networks, support vector machines, k-nearest neighbour, Bayesian learning, ensemble models, and different decision trees, are reviewed in this module, along with their internal workings, capabilities, and applications. Most of these strategies can tackle prediction difficulties of the classification and regression kinds. They are commonly employed to address challenging prediction challenges when other, more traditional approaches fail to deliver results.

The technologies and modes of analysis behind big data yield great promise in addressing social problems in various domains such as healthcare and terrorism; at the same time, their most frequent deployment is in adtech. Massive data sets often promise that they are anonymized, yet researchers continually discover how easily individual identity can be reconstructed from seemingly incongruent data points.

This course addresses modern debates and regulations around privacy and the ethical use of data. Addressing equally ethical and theoretical considerations as well as practical/applied exercises in data mining.

Big Data involves the computational treatment of massively large data sets in order to gain actionable insights. It is changing myriad aspects of research and business, from astronomy to recommendation engines to the study of literary history.

Big Data and Its Applications introduces some of the algorithms, statistical models, and computing tools most common in data science today, as well as the demonstration the value of these for specific applications. Tools to be covered will vary, but will certainly include Hadoop and Apache Spark, as well as platforms such as Amazon AWS, Microsoft Azure, and Google Cloud.

The course will focus on data reduction and information extraction, as well as the ability to perform regression analysis on massive data sets, and the ability to work effectively in distributed/cloud environments.

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.

This module will provide learners with knowledge and understanding of the application of machine learning methodologies to handle industrial difficulties, to a more extensive array of data mining and classification type activities. Learners will discover the machine learning algorithms by utilizing neural networks, k-means clustering, and support vector machines in computer vision to analyse data based on supervised, unsupervised, and partially supervised. Additionally covered in this module are, Tensor flow, Faster- RCNN-Inception-V2 model, and Anaconda software development environment utilized to recognize autos and individuals in pictures that provides insight into the usage of current deep learning network models like CNN.