Data Science MSc — 2027 entry

The need for computational power and data storage continues to drive demand for more highly capable systems. Highly data intensive applications demand fast access to terabytes, petabytes, even exabytes of storage; processor intensive applications demand access to various types of processors in various configurations. Such applications are increasingly being developed in both scientific and industrial contexts and need to be variously scalable and supportable for large numbers of geographically distributed users. This module will provide insights into how Cloud Computing attempts to meet the varying needs of such applications.

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.

A key aspect of business operations today, across sectors almost, has to do with gathering the right type of data and storing it in a way that it can be readily available to the right person at the right time. This course looks into the techniques that allow us nowadays to define and operate on large volumes of data as and when it is created. This paves the way for making more intelligent uses of data, whether this has to do with correctness (reliability and consistency) or informing more strategic decisions of the business so it can better prepare itself for the future.

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.

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.

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.

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 need for computational power and data storage continues to drive demand for more highly capable systems. Highly data intensive applications demand fast access to terabytes, petabytes, even exabytes of storage; processor intensive applications demand access to various types of processors in various configurations. Such applications are increasingly being developed in both scientific and industrial contexts and need to be variously scalable and supportable for large numbers of geographically distributed users. This module will provide insights into how Cloud Computing attempts to meet the varying needs of such applications.

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.

A key aspect of business operations today, across sectors almost, has to do with gathering the right type of data and storing it in a way that it can be readily available to the right person at the right time. This course looks into the techniques that allow us nowadays to define and operate on large volumes of data as and when it is created. This paves the way for making more intelligent uses of data, whether this has to do with correctness (reliability and consistency) or informing more strategic decisions of the business so it can better prepare itself for the future.

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.

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.

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.

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 need for computational power and data storage continues to drive demand for more highly capable systems. Highly data intensive applications demand fast access to terabytes, petabytes, even exabytes of storage; processor intensive applications demand access to various types of processors in various configurations. Such applications are increasingly being developed in both scientific and industrial contexts and need to be variously scalable and supportable for large numbers of geographically distributed users. This module will provide insights into how Cloud Computing attempts to meet the varying needs of such applications.

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.

A key aspect of business operations today, across sectors almost, has to do with gathering the right type of data and storing it in a way that it can be readily available to the right person at the right time. This course looks into the techniques that allow us nowadays to define and operate on large volumes of data as and when it is created. This paves the way for making more intelligent uses of data, whether this has to do with correctness (reliability and consistency) or informing more strategic decisions of the business so it can better prepare itself for the future.

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.

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.

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 provides information, guidance and support for developing the student¿s employability.In addition, this module helps students build on their previous skills and generate documents that are required to demonstrate their skill sets to external parties, recruiters and recruiting agencies. Furthermore, skills acquired in this module will enable students to actively participate in placement and employment searches.

This module provides information, guidance and support for developing the student¿s employability.In addition, this module helps students build on their previous skills and generate documents that are required to demonstrate their skill sets to external parties, recruiters and recruiting agencies. Furthermore, skills acquired in this module will enable students to actively participate in placement and employment searches.

This module supports students' development of personal and professional attitudes and abilities appropriate to a Professional Training placement. It supports and facilitates self-reflection and transfer of learning from their Professional Training placement experiences to their dissertation and their future employment. The module is concerned with Personal and Professional Development towards holistic academic and non-academic learning. Development and learning may occur before and during the placement. However, the graded assessment takes place primarily towards the end of, or after, the placement. Additionally, the module aims to enable students to evidence and evaluate their placement experiences and transfer that learning to other situations through written skills. Through the professional placement students will refine both their subject specific and general soft skills as a Data Scientist, and gain practical experience of the use of Data Analytics and domain knowledge in the application of Data Science methods in a business or research context. The assessment is aligned with the Edison Data Science Competence Framework and includes a technical component describing the work undertaken, as relates to Data Science competence group Data Analytics (DSDA). Further it encourages students to reflect both on their ability to think and act like a Data Scientist (Edison skill DSPS) and their 21st Century Workplace skills (Edison skill SK21C), and on the experience of Business Process or Scientific Research gained during the placement (Edison competence DSBPM). This includes but is not limited to: Effective use of a variety of data analytics techniques, such as Machine Learning (including supervised, unsupervised, semi- supervised learning), Data Mining, Prescriptive and Predictive Analytics, for complex data analysis through the whole data lifecycle (DSDA01). Understand and use different performance and accuracy metrics for model validation in analytics projects, hypothesis testing, and information retrieval (DSDA04). Visualise results of data analysis, design dashboard and use storytelling methods (DSDA06). Being aware of the power and limitations of the main machine learning and data analytics algorithms and tools (DSPS08). Working in a multi-disciplinary team, with the ability to communicate with domain and subject matter experts (DSPS11). Being aware of ethical issues around the use of data and insight delivered (DSPS15). Critical Thinking: Demonstrating the ability to apply critical thinking skills to solve problems and make effective decisions (SK21C01). Collaboration: Working with others, appreciation of multicultural differences (SK21C03). Planning & Organizing: Planning and prioritizing work to manage time effectively and accomplish assigned tasks (SK21C05). Dynamic (self-) re-skilling: Continuously monitor individual knowledge and skills as shared responsibility between employer and employee, ability to adopt to changes (SK21C09). Translating unstructured business problems into an abstract mathematical framework (DSBPM01). Using data to improve existing services or develop new services (DSBPM02). Providing scientific, technical, and analytic support services to other organizational roles (DSBPM04).

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.