MSc Data Science and Machine Learning with Extended Professional Practice

University of Hertfordshire

In this module, you will develop your theoretical, professional, technical, and academic skills to help you succeed in various data engineering contexts. You will take a practical approach to converting raw data into valuable knowledge. Working in teams, you will apply skills in cloud computing, big data, Machine Learning, and data visualisation, among others.

In this module, you will gain hands-on experience in all aspects of typical Data Science and Machine Learning applications. You will focus on the practical aspects of Data Science and Machine Learning, such as image processing, time series analysis, and natural language processing. You will explore typical data analysis and Machine Learning pipelines with the help of concrete applications in areas such as finance, healthcare, marketing, real-time and historical data. Additionally, you will be introduced to essential tools for Data Science and Machine Learning. The content may vary yearly to incorporate evolving applications and techniques in these fields.

In this module, you will be introduced to fundamental mathematical concepts and skills essential for Data Science, including linear algebra, set theory, advanced calculus, probability, and statistics. You will learn to apply these mathematical foundations as computational techniques to solve Data Science problems. The module also covers the critical evaluation of methodologies and algorithmic approaches, data interpretation challenges, and the practical use of programming languages and environments for Data Science purposes. Content may vary yearly to incorporate evolving applications and techniques in Data Science.

In this module, you will study a selection of research topics centred around neural network theory and design, Machine Learning, including supervised and unsupervised learning, and some interesting applications such as data mining, biocomputation, evolutionary algorithms, neural networks as models of brain function in health, disease and development, and data visualisation. The actual topics taught may vary from year to year.

In this module, you will explore legal, social, ethical, and professional issues that may affect the work of practitioners in the computing and technology sectors. You will focus on the ethical considerations inherent in the development of responsible technologies. The topics covered will vary from year to year to reflect contemporary research and issues.

In this module, you will learn about the benefits of the supervised work placement and be encouraged to apply. You will receive support in your application by gaining insight into the types of employers and job roles available, helping you select the most appropriate opportunities based on your strengths and weaknesses, and understanding how employers conduct the recruitment process. The module will guide you throughout the entire application process, including writing CVs and letters of application, developing interview techniques, and preparing for other forms of assessment. For those who secure a placement, further assistance will be provided to prepare for employment.

In this module, you will gain the opportunity to set your academic studies in a broader context, acquire practical experience in specific technical areas, and strengthen your communication and time-management skills. You will develop as an independent learner, enabling you to gain maximum benefit from future learning opportunities in your programme of study. Depending on the nature of the placement experience, you will acquire a foundation of technical expertise in specialist areas, which you may be able to enhance through further study, particularly in your final project.

The project module includes both a taught and a non-taught component. During the taught part, you will cover the following topics: Project plan development, Project risk assessment, Ethics and working with human participants, Problem analysis and decision-making, Literature review, Conducting investigative work, Documenting a project, and Report writing and presentation. In the non-taught part, under the guidance of a supervisory team, through a series of scheduled meetings lasting up to 6 hours, you will work towards completing a project in Data Science and Machine Learning.