12-month top British Master’s degree in Germany
From business and finance to health and medicine, from infrastructure to societal studies, data science plays a vital role in all aspects of the modern world. Our MSc programme will ensure you have an advanced level of skills, knowledge, and experience in this rapidly expanding, highly in-demand field to achieve your career aspirations.
Studying for an MSc in Data Science at Lancaster University in Leipzig will provide you with the perfect environment to develop an expertise in the discipline. You will develop and consolidate your fundamental skills in both computing and statistics before progressing onto more advanced modules which allow you to acquire and enhance advanced technical skills, while gaining professional knowledge that will support and advance your future career choices.
In addition to these taught modules, you will have the option to undertake a 12-week placement in industry as part of dissertation project. This will provide you with a fantastic opportunity to apply your skills and knowledge to real-world situations and challenges, allowing you to gain valuable professional experience and demonstrate a firm grasp of the discipline.
Your dissertation project represents a substantial, independent research project. Supervised by an academic specialist, you will develop your ability to formulate a project plan, gather and analyse data, interpret your results, and present findings in a professional environment. This research will be an opportunity to bring together everything you have learned over the year, expand your problem-solving abilities and manage a significant project. This will be great experience for you to draw upon in an interview and in your career.
1 academic year
English
The aim of this programme is to equip you with state-of-the-art knowledge and the skill set required to lead a successful career in data science in an international context. The ambition is to help shape the data analysts of the future, enabling you to analyse and solve problems and to make decisions with an awareness of the strategic context, the role of technology and of managing within and between organisations. The programme is designed to provide you with a solid core knowledge in data science, data mining, statistics and related programming.
The programme is designed for graduates interested in careers in data science and statistics. Graduates will be able to work as consultants or employees in any industry.
Programme structure: Information contained on the website with respect to modules is correct at the time of publication, and the University will make every reasonable effort to offer modules as advertised. In some cases, changes may be necessary and may result in some combinations being unavailable, for example as a result of student feedback, timetabling, staff changes and new research. Not all optional modules are available every year.
Students are provided with a comprehensive coverage of the problems related to data representation, manipulation and processing in terms of extracting information from data, including big data. They will apply their working understanding to the data primer, data processing and classification. They will also enhance their familiarity with dynamic data space partitioning, using evolving, clustering and data clouds, and monitoring the quality of the self-learning system online.
Students will also gain the ability to develop software scripts that implement advanced data representation and processing and demonstrate their impact on performance. In addition, they will develop a working knowledge in listing, explaining and generalising the trade-offs of performance, as well as the complexity in designing practical solutions for problems of data representation and processing in terms of storage, time and computing power.
This module will help you understand what the data science role entails and how that individual performs their job within an organisation on a day-to-day basis. You will look at how research is performed in terms of formulating a hypothesis and the implication of research findings and be aware of different research strategies and when these should be applied. You will gain an understanding of data processing, preparation and integration, and how this enables research to be performed and you will learn how data science problems are tackled in an industrial setting, and how such findings are communicated to people within the organisation.
This module is designed for students that are completely new to programming, and for experienced programmers, bringing them both to a high-skilled level to handle complex data science problems. Beginner students will learn the fundamentals of programming, while experienced students will have the opportunity to sharpen and further develop their programming skills. The students are going to learn data-processing techniques, including visualisation and statistical data analysis. For a broad formation, in order to handle the most complex data science tasks, we will also cover problem solving, and the development of graphical applications. Two open-source programming languages will be used, R and Python.
This module will motivate the use of statistical modelling as a tool for making inference on a population given a sample of data. Students will be introduced to basic terminology of statistical modelling, and the similarities and differences between statistical and machine learning approaches will be discussed to lay the foundations for the development of both of these over the remaining core modules They will cover the concepts of sampling uncertainty, statistical inference and model fitting, with sampling uncertainty used to motivate the need for standard errors and confidence intervals. Once core concepts have been established, linear regression and generalised linear models will be introduced as essential statistical modelling tools. An understanding of these models will be obtained through implementation in the statistical software package R.
This module provides an introduction to statistical learning. General topics covered include big data, missing data and biased samples. Likelihood and cross-validation will be introduced as generic methods to fit and select statistical learning models. Cross-validation will require an understanding of sample splitting into calibration, training and validation samples. The focus will then move to handling regression problems for large data sets via variable reduction methods such as the Lasso and Elastic Net. A variety of classification methods will be covered including logistic and multinomial logistic models, regression trees, random forests and bagging and boosting. Examination of classification methods will culminate in neural networks which will be presented as generalised linear modelling extensions. Unsupervised learning for big data is then covered including K-means, PAM and CLARA, followed by mixture models and latent class analysis.
This module provides students with up-to-date information on current applications of data in both industry and research. Expanding on the module ‘Data Mining’, students will gain a more detailed level of understanding about how data is processed and applied on a large scale across a variety of different areas.
Students will develop knowledge in different areas of science and will recognise their relation to big data, in addition to understanding how large-scale challenges are being addressed with current state-of-the-art techniques. The module will provide recommendations on the Social Web and their roots in social network theory and analysis, in addition their adaption and extension to large-scale problems, by focusing on primer, user-generated content and crowd-sourced data, social networks (theories, analysis), recommendation (collaborative filtering, content recommendation challenges, and friend recommendation/link prediction).
On completion of this module, students will be able to create scalable solutions to problems involving data from the semantic, social and scientific web, in addition to abilities gained in processing networks and performing of network analysis in order to identify key factors in information flow.
In this module we explore the architectural approaches, techniques and technologies that underpin today’s Big Data system infrastructure and particularly large-scale enterprise systems. The module provides a broad knowledge and context of systems architecture enabling students to assess new systems technologies, to know where technologies fit in the larger scheme of enterprise systems and state of the art research thinking, and to know what to read to go deeper.
The principal ethos of the module is to focus on the principles of Big Data systems and applying those principles using state of the art technology to engineer and lead data science projects. Detailed case studies and invited industrial speakers will be used to provide supporting real-world context and a basis for interactive seminar discussions.
The module provides an introduction to the fundamental methods and approaches from the interrelated areas of data mining, statistical/machine learning, and intelligent data analysis. The module covers the entire data analysis process, starting from the formulation of a project objective, developing an understanding of the available data and other resources, up to the point of statistical modelling and performance assessment. The focus of the module is classification and uses the R programming language.
Optimisation, sometimes called mathematical programming, has applications in many fields, including operational research, computer science, statistics, finance, engineering and the physical sciences. Commercial optimisation software is now capable of solving many industrial-scale problems to proven optimality.
The module is designed to enable students to apply optimisation techniques to business problems. Building on the introduction to optimisation in the first term, students will be introduced to different problem formulations and algorithmic methods to guide decision making in business and other organisations.
This starts with the students selecting an industry or research partner, undertaking a placement in June-July, and then submitting a written dissertation of 20,000-30,000 words in early September.
This is a self-study module designed to provide the foundation of the main dissertation, at a level considered to be of publishable quality. The project also offers students the opportunity to apply their technical skills and knowledge on current world class research problems and to develop an expert knowledge on a specific area.
The topic of the project will vary from student to student, depending on the interests of the student and availability of research staff and industry partners.
Teaching is delivered via a combination of small group lectures and group-based tutorials. Assessment is via individual or group coursework, research projects and examinations. You will be expected to undertake independent study throughout to supplement what is being taught/learned and to broaden your personal knowledge.
All teaching is conducted in English.
After finishing your programme, you will receive your degree certificate from the Lancaster University, UK.
Today’s most rewarding technical and managerial jobs often involve working with data and systems, managing the large quantities of data entering an organisation, and translating data into words and data visualizations that others can easily understand.
Therefore, you will develop and consolidate your fundamental skills in both, computing and statistics, before progressing onto one of our specialist pathways which allows you to acquire and enhance advanced technical skills, while gaining professional knowledge that will support and advance your future career choices.
The programme is designed for graduates interested in careers in data science. The offering will be also suitable for students pursuing a Master’s degree as an extension to their BSc in Computer Science/ Statistics or other fields that equipped them with the necessary pre-requisites. Graduates from this course can expect to work as data scientist in general, but more specifically as:
It is not only the IT giants like Google that deal with Big Data. Virtually every company accumulates, processes, and evaluates data. Therefore, after obtaining the degree you will be equipped to work in a wide range of fields and responsibilities. In Leipzig specifically, the main industries include Life Science and Biotechnology and, of course, IT that handle large amounts of data every day. The three Max-Planck-Institutes, Bio City Leipzig and over 1200 IT companies in Leipzig are just examples for Leipzig’s diverse biotech and digital innovation landscape. Here you can apply your skills to advance local research and industry as a Data Scientist.
