MSc — 2027 entry Data Science (Conversion)

The module provides for coverage of a variety of statistical methods, including descriptive statistics and validating formulated hypotheses, as well as predictive analytics. The computational foundations and methods of importance to data science are also covered, along with consideration for relevant supporting software and tools.

Mathematics is a key tool in Data Science. This module is designed to introduce students to the foundational mathematical techniques that are required to support future data science modules.

This module provides a solid foundation in Python programming relevant for data science. It introduces students to core programming concepts, essential Python libraries, and practical coding techniques widely used in data analysis and machine learning. By the end of the module, students will be confident in writing Python programs for solving real-world data problems, handling data, performing analysis, and creating visualisations.

Machine Learning for Data Science incorporates a wide range of machine learning algorithms and data mining techniques, which can be applied to real-world problems and datasets with various characteristics to generate new insights and understanding. Through treatment of the principles and fundamental requirements for machine learning, example applications, and related exercises, this module will offer coverage of a range of contemporarily important and emergent machine learning algorithms. The module will provide for the means to critically evaluate, extend, and apply, appropriate techniques to datasets exemplifying specific characteristics in order to derive suitable and defensible results.

In today¿s world where companies can amass more and more fine-grained data, it is crucial for a business to understand how this data can be used to effectively drive the business forward. Business Analytics is a set of methods and tools that can transform data into useful insights for decision-making. For example machine learning algorithms can be used to discover interesting patterns in the current market data or to predict customer behaviour (e.g. customer churn) from past data.

This module introduces students to the research skills required to engage in data science projects in both industry and academia, whilst also covering the relevant ethical and security considerations when designing and implementing data driven projects. Areas of specific concern for ethics and security in machine learning and statistical analysis are highlighted. Good practice in the responsible and effective use of generative AI for research development and academic writing will be demonstrated and critically discussed. This module serves as an important initial module towards planning and executing the dissertation project. It also provides a platform to facilitate team building skills and make the student aware of the challenges of working effectively with other people embracing equality, diversity and inclusion.

The dissertation consists of a substantial written report. This report is based on a major piece of work that involves applying material encountered in the taught component of the degree, and extending that knowledge with the student's contribution, under the guidance of a supervisor. The dissertation usually involves a substantial literature survey on a specific topic, followed by the identification of a problem to tackle, and thereafter the development of a technical solution, and experimental or theoretical evaluation of the achievement.

Mathematics is a key tool in Data Science. This module is designed to introduce students to the foundational mathematical techniques that are required to support future data science modules.

This module provides a solid foundation in Python programming relevant for data science. It introduces students to core programming concepts, essential Python libraries, and practical coding techniques widely used in data analysis and machine learning. By the end of the module, students will be confident in writing Python programs for solving real-world data problems, handling data, performing analysis, and creating visualisations.

Machine Learning for Data Science incorporates a wide range of machine learning algorithms and data mining techniques, which can be applied to real-world problems and datasets with various characteristics to generate new insights and understanding. Through treatment of the principles and fundamental requirements for machine learning, example applications, and related exercises, this module will offer coverage of a range of contemporarily important and emergent machine learning algorithms. The module will provide for the means to critically evaluate, extend, and apply, appropriate techniques to datasets exemplifying specific characteristics in order to derive suitable and defensible results.

This module introduces students to the research skills required to engage in data science projects in both industry and academia, whilst also covering the relevant ethical and security considerations when designing and implementing data driven projects. Areas of specific concern for ethics and security in machine learning and statistical analysis are highlighted. Good practice in the responsible and effective use of generative AI for research development and academic writing will be demonstrated and critically discussed. This module serves as an important initial module towards planning and executing the dissertation project. It also provides a platform to facilitate team building skills and make the student aware of the challenges of working effectively with other people embracing equality, diversity and inclusion.

The dissertation consists of a substantial written report. This report is based on a major piece of work that involves applying material encountered in the taught component of the degree, and extending that knowledge with the student's contribution, under the guidance of a supervisor. The dissertation usually involves a substantial literature survey on a specific topic, followed by the identification of a problem to tackle, and thereafter the development of a technical solution, and experimental or theoretical evaluation of the achievement.

The module provides for coverage of a variety of statistical methods, including descriptive statistics and validating formulated hypotheses, as well as predictive analytics. The computational foundations and methods of importance to data science are also covered, along with consideration for relevant supporting software and tools.

In today¿s world where companies can amass more and more fine-grained data, it is crucial for a business to understand how this data can be used to effectively drive the business forward. Business Analytics is a set of methods and tools that can transform data into useful insights for decision-making. For example machine learning algorithms can be used to discover interesting patterns in the current market data or to predict customer behaviour (e.g. customer churn) from past data.

The dissertation consists of a substantial written report. This report is based on a major piece of work that involves applying material encountered in the taught component of the degree, and extending that knowledge with the student's contribution, under the guidance of a supervisor. The dissertation usually involves a substantial literature survey on a specific topic, followed by the identification of a problem to tackle, and thereafter the development of a technical solution, and experimental or theoretical evaluation of the achievement.