Business Analytics – MSc

Our MSc Business Analytics covers a combination of technical skills, critical thinking skills and soft skills.

This programme will train you in analytical decision-making. Not only do you learn the theory of business analytics, but also how to apply it in practice. This involves generating relevant business insights using data-driven methodologies and tools. Our programme is one of the few to teach the entire business analytics life cycle, covering descriptive, predictive and prescriptive analytics.

You will enhance your programming in both R and Python, the two most popular languages in the areas of machine learning, statistics and data mining.

You will strengthen your skills in statistics, data analytics and visualisation. You will improve your problem structuring and problem solving. You will also hone your presentation, negotiation, and leadership skills.

All these skills enable you to develop the competence and confidence to contribute to the grand challenges faced by organisations.

Modules:

• • Statistics and Descriptive Analytics
    The module includes the following topics:Using graphs to describe dataDescriptive statisticsTheory of probability and discrete distributionsConfidence intervals and the basics of hypothesis testingIntroduction to linear regressionMultiple linear regressionHypothesis testing in the context of regressionRegression diagnosticsLikelihood theoryModel buildingAll the topics are motivated by specific real time problems and will build upon the idea of solving problems using statistical methods.The topics are supported by workshops and computer labs. The course is delivered in R.

• • Programming for Data Scientists
    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.In particular, students will gain experience with two programming languages. One will be covered by the Mathematics and Statistics Department, with a greater emphasis on data analysis and visualisation (e.g., R); while another will be covered by the School of Computing and Communications, with a greater emphasis on the fundamentals of programming and problem solving (e.g., Python).Additionally, students will gain experience by working through exercise tasks and discussing their work with their peers; thereby fostering interpersonal communications skills. Students that are new to programming will find help in their experience peers, and experienced programmers will learn how to assist and explain the fundamental concepts to beginners.

• • Operational Research and Prescriptive Analytics
    This module provides an introduction to business analytics, management science and related disciplines, with a particular emphasis on the modelling and solution of real-world problems.It includes an introduction to three very useful approaches: optimisation, stochastic modelling and simulation.Several examples and case studies will be given to illustrate how the approaches can be used in practice.Students will also be shown how to implement models in a spreadsheet. Some hands-on experience with Excel, including the development of simple macros, will be given.

• • Analytics in Practice
    In this era of unprecedented data and computational power, analytics is proving to be the cornerstone of ever-growing areas of organisational decision making, be it for example for businesses to compete or for public sector organisations to innovate. There are several aspects in applying analytics to real business and decision-making scenarios and problems. These not only include computational and analytical methodologies, but also how to systematically structure problems and effectively communicate results. A key ability expected of an analyst or analytics practitioner is to have a good idea and understanding of how these aspects of analytic decision-making play together in any given context. Hence, the aim of this module is to enable students to develop analytic thinking at a holistic level and the ability to conduct analytical projects in a real-world context.

• • Optimisation and Heuristics
    Optimisation has applications in many fields, including Business Analytics, Computer Science, Data Science, Finance, Engineering and the Physical Sciences. Commercial optimisation software is now capable of solving many industrial-scale problems to proven optimality. On the other hand, there are still many practical applications where finding a provably-optimal solution is not computationally viable. In such cases, heuristic methods can allow good solutions to be found within a reasonable computation time.The module covers theory, applications, algorithms and software. Examples from Analytics, Finance and Data Science are given. The following topics are covered: Nonlinear programming, Linear programming, Integer programming, Heuristics

• • Intelligent Data Analysis and Visualisation
    The course provides an introduction to the fundamental methods and approaches from the interrelated areas of data mining, statistical/ machine learning, and intelligent data analysis. The course covers then 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 course is classification. The course content covers:Exploratory data analysis and visualisation including dimensionality reduction methods like principal component analysis and multi-dimensional scaling
    Classification methods like: k-Nearest Neighbours, Naive Bayes, Logistic Regression, Decision trees (Random forests), and Artificial Neural Networks
    Performance Assessment and Model Selection
    The course uses the R programming language and more specifically the RStudio integrated programming environment. The course makes extensive use of online video lectures from top scientists in the field, and (I hope) will be supported by DataCamp (I am currently in the process of enrolling students to DataCamp for the classroom to allow them free access to a large number of otherwise non-free DataCamp courses).

• • Forecasting and Predictive Analytics
    After introducing the topic of forecasting in business organisations, issues concerned with forecasting model building in regression and its extensions are presented, building on material covered earlier in the course(s). Extrapolative forecasting methods, in particular Exponential Smoothing are then considered, as well as Machine Learning / Artificial Intelligence methods, in particular Neural Networks. All methods are embedded in a case study in forecasting in organisations. The course ends by an examination of forecasting as it applies to operations and how forecasting can best be improved in an organisational context. Assessment is through two projects aimed at extending and evaluating student learning in regression modelling and time series analysis and concurrent practical exercises marked as homework.

• • Transportation and Logistics Analytics
    The following main subjects will be covered:Emerging logistical concepts Logistics decision making requirements (strategic, tactical and operational decisions) Network flow models Facility location (single and multi-objective formulations) Network design Warehousing Vehicle routing and scheduling (static, dynamic, and single and multi-objective) Terminal (airport) capacity management (deterministic, and single and multi-objective)

• • Dissertation (Industry/Research)
    Dissertation (Industry): At the end of the programme’s taught modules you will complete a three-month project, which will be organisation-based (with organisations from both public and private sectors) or organisation-driven. Eligibility for organisation-based projects will be competitive and based on merit and marks achieved.These projects are an essential part of the learning on each of the MSc programmes and give you a chance to use the analytical methods (quantitative/qualitative) and professional skills developed on the taught modules.Dissertation (Research): At the end of the taught modules you will complete a supervised three-month research project, providing you with the opportunity to deep-dive in to a specific topical area and investigate the emerging state of the art.These projects are an essential part of the learning on the MSc programme and gives you a chance to extend your understanding of the analytical methods (quantitative/qualitative) and professional skills developed on the taught modules.

MSc Direct Entry

Academic:

2:2 Hons degree UK or equivalent.

Specific degree background is required: Completed degree with quantitative methods content (business, engineering, mathematics, sociology etc.)

For specific national curriculum, please view the website.

English:

IELTS 6.5 overall (no band below 6.0)
For other accepted English language tests, please view the website.

Pre-Master’s entry

Applicants who do not meet stated MSc direct entry requirements, may start with a Pre-Master’s programme. Please view full entry requirements here.

Teaching is delivered via a combination of small group lectures and group-based tutorial coursework (oral and written presentation), and assessment is via individual coursework (oral and written presentation) and examinations. You will be encouraged throughout to undertake independent study to supplement what is being taught/learnt and to broaden your personal knowledge.

All modules are delivered in a block teaching, allowing students to concentrate on each subject. All teaching is conducted in English.

German language skills are not required for admission into the programme. You will learn in English, and converse with classmates and academics in English.

All MSc Business Analytics students will receive their undergraduate degree from Lancaster University’s Bailrigg campus in the UK.

• Fees: 

Our tuition fee is set for a 12-month time frame encompassing one academic year.

There are two types of fees at Lancaster University Leipzig:

1.  EU/UK fee status: applicable to citizens of EU/UK and EEA member countries
2.  International fee status: applicable to citizens of the rest of the world

The tuition fee that you will pay depends on your citizenship or your immigration status. International citizens with legal residence in the EU/UK or a EEA member country will be assessed for EU/UK fee status on a case by case basis. The admissions department will provide you with more guidance regarding the fee status review during the application assessment stage. 

• Funding: 

Eligible students may benefit from various funding options available at Lancaster University Leipzig. Explore what options you may be qualified for.

A Business Analytics degree can be widely applied across the business world. Our graduates at Lancaster University UK go on to work for a wide range of companies, large and small, around the world, in a variety of roles.

Recent graduate destinations include:

• Virgin Atlantic
• Avanti West Coast
• EY
• Amazon

The roles our graduates have taken on include:

• Data Scientist
• Business Analyst
• Insights Analyst
• Account Executive
• Financial Analyst
• eCommerce Data Analyst
• Customer Analyst
• Strategy Analyst
• Product Analyst
• Fraud Specialist
• Business Intelligence Developer
• Business Consultant
• Credit Analyst

With data-driven decision-making now central to business success, the demand for professionals skilled in business analytics is rapidly rising across industries. MSc Business Analytics graduates are equipped with a powerful blend of analytical, technical, and strategic skills, enabling them to transform data into actionable insights. This makes them highly valuable in sectors such as finance, healthcare, retail, technology, and consulting. As organisations continue to invest in data capabilities, MSc Business Analytics graduates are exceptionally well-positioned to drive innovation and shape strategic decisions in an evolving business landscape.

For information on Careers Centre at LU Leipzig go to Careers Centre – Lancaster University Leipzig .