Why choose MSc Business Analytics at Lancaster?
Richlove talks about why she chose the course and what she has got out of it so far.
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Lancaster University is top 10 in The Complete University Guide 2024
We live in a world where analytical decision-making happens every second. Data is collected about everything in order to construct, operate and maintain systems. If you want a career in decision support, operational research, industrial engineering or management science, the MSc Business Analytics is the ideal programme for you.
This course 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.
We have excellent partnerships with industry business analytics practitioners. We constantly update this programme to reflect the dynamically changing landscape of business analytics. We place a very high emphasis on creating graduates with skills that are valuable in the job market. For example, you will experience a real-life analytics project as part of your course.
Lancaster University is a pioneer of analytics in UK and Europe. We were one of the first universities in the UK to establish an Operational Research department, which developed into Business Analytics. More than 50 years on, we are still at the forefront of applying analytics to business in teaching and research.
Our academics are leaders in fields such as forecasting, optimisation, simulation and stochastic modelling. You will learn technical aspects like machine learning and data mining. We also have domain expertise like logistics and supply chain analytics, healthcare analytics, sports analytics, network analytics, marketing and pricing analytics.
Our graduates become business analysts, data scientists, analytics engineers and logistics consultants, and some also go into academic research. Many students get job offers whilst still studying.
The MSc in Business Analytics consists of compulsory and optional modules, plus a project. The compulsory modules ensure that you have a firm grounding in important subjects. The wide range of optional modules allows you to specialise in subjects that particularly interest you.
During your first term from October to December you will study four core modules:
The second part of the course runs from January to May, and includes the Analytics in Practice module plus three optional modules.
From May to September you will choose one of the two dissertation modules.
2:1 Hons degree (UK or equivalent) in a quantitative discipline such as Mathematics and/or Statistics or Engineering. Businesss and Management degrees with a very strong quantitative component may also be considered. Information Technology and Computer Science degree are only suitable with very strong quantitative content.
Previous study needs to have included topics such as maths, probability, statistics, econometrics, game theory, data analysis, operations research, simulation and business modelling. Prior study in computer programming is not required.
If you have studied outside of the UK, we would advise you to check our list of international qualifications before submitting your application.
For recent graduates, relevant experience can be an advantage, but is not essential. For other applicants, work experience in analytics and/or data modelling is required.Ideally no more than 5 years should have elapsed since previous full-time study.
We may ask you to provide a recognised English language qualification, dependent upon your nationality and where you have studied previously.
We normally require an IELTS (Academic) Test with an overall score of at least 6.5, and a minimum of 6.0 in each element of the test. We also consider other English language qualifications.
If your score is below our requirements, you may be eligible for one of our pre-sessional English language programmes.
Contact: Admissions Team +44 (0) 1524 592032 or email email@example.com
You will study a range of modules as part of your course, some examples of which are listed below.
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 algorithmic methodologies, which encompass three main strands of analytics (descriptive, predictive and prescriptive) but also problem structuring methodologies like SSM. 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 to make informed choices when using or recommending analytics in a real-world context.
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 analysing how forecasting is applied to operations and how forecasting can best be improved in an organisational context.
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.
Python is a simple, yet very powerful, high-level computer programming language that is extremely popular today. It is widely used in many scientific areas for data exploration whilst being the preferred programming language in a range of modern organisations.
This module will cover a range of the most commonly used algorithms and techniques, including sorting and searching algorithms, data manipulation, visualisation and the use of package extensions such as pandas and numpty, as well as applications in Business Analytics such as simulation and optimisation.
At the heart of many management problems lies data that need to be described, analysed and interpreted. The overall objective of this module is to develop the students’ abilities to describe, analyse and interpret data soundly, making effective use of computer software. The skills learned on this course underpin many of the other modules on the Management Science Department programmes. Although the module assumes little prior knowledge of statistics, those with prior knowledge should not expect to find it easy.
The aim of this module is to provide you with a valuable opportunity to research a real business problem for an organisation, delivering a viable and well-documented solution to the client. For a successful project, you will employ your knowledge of methods (qualitative/quantitative), problem scoping and the research literature to your knowledge and understanding of professional engagement both in their interactions with the client and in the reporting of their outputs and discourse on your research.
The aim of this module is to provide students with a valuable opportunity to research an area of your degree programme in-depth, linking this to business problems that arise in the real world. For a successful project, you will employ their knowledge of analytical methods, problem scoping, modelling and the research literature to identify and implement a solution approach to the business problem or expand on your knowledge of the state of the area being researched. Throughout the project, you will develop your knowledge and understanding of professional academic engagement both in your interactions with your supervisor and in the reporting of your outputs and discourse on your research.
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 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 course is classification and uses the R programming language.
This module introduces you to the basic principles of marketing and places particular emphasis on the role of analytical modelling in supporting marketing decisions in organisations. It is hoped that you will be in a position at the end of this course to fully appreciate how analytical models enable managers to make better marketing decisions instead of relying on their intuition or gut feel.
Topics covered include Marketing Mix Models (MMM) and text mining and are supported by workshops, which will offer opportunities to build up modelling skills. These will be done in R (or equivalently other open-source statistical programming languages, such as Python).
The Operations Management discipline has traditionally been concerned with how organisations achieve excellence in processes and operations, and to this end several (continuous) improvement approaches and tools and techniques have been stressed. The interest in intra-firm improvement and excellence has more recently been extended to address also how firms can improve their operations in coordination with their supply chain counterparts. This has become relevant since specialisation of labour across industries and outsourcing trends entail that a large share of the value of products and services is created outside the focal firm.
This module provides an understanding of both intra- and inter-organisational approaches to operations improvement and excellence. In particular, it address: a) improvement tools and techniques that firms implement primarily in their internal operations, and b) supply processes and capabilities required to improve operations and achieve excellence in coordination with supply chain partners. The themes above are addressed both in manufacturing and service industries and cross private and public sector settings.
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.
Computer simulation methods are among the most commonly used approaches within Operational Research. Modern simulation packages are a valuable aid in building a simulation model and this module will emphasise the practical application of simulation, with a good understanding of how a simulation model works being an essential part of this. Stochastic modelling methods provide analytical tools which enable Operational Researchers to gain insight into complicated and unpredictable real-world processes. The application of these methods requires careful consideration of the dynamics of the real-world situation being modelled, and (in particular) the way that uncertainty evolves.
Simulation and stochastic modelling are inter-related in several ways. Insights from stochastic modelling can help in the design of simulation models. On the other hand, simulation can be used to investigate the sensitivity of stochastic models to some of their underlying assumptions. A number of fundamental concepts, such as probabilistic variations, steady state behaviour and time-dependent behaviour are common to both areas.
The purpose of this course is to understand and use mathematical models in making strategic, tactical, and operational logistics decisions. Emerging logistical concepts will be introduced and the associated mathematical modelling needs will be discussed. Algebraic formulations will be used as vehicle for describing models and discussing their relationships. There will be a focus on modelling, the use of professional software, and the understanding of results. For problems where exact solutions are hard to achieve even for simple instances of the problem, heuristics will be discussed. The main topics covered are: facility location, network design, warehousing, vehicle routing and scheduling, and Terminal (airport) capacity management.
Depending on students need and level of programming skill, the computer workshops will focus on either solver languages (e.g. GAMS, AMPL, MPL) and/or programming interfaces (PYOMO, CPLEX Concert, Gurobi Python Interface).
Information contained on the website with respect to modules is correct at the time of publication, but changes may be necessary, for example as a result of student feedback, Professional Statutory and Regulatory Bodies' (PSRB) requirements, staff changes, and new research. Not all optional modules are available every year.
We set our fees on an annual basis and the 2024/25 entry fees have not yet been set.
There may be extra costs related to your course for items such as books, stationery, printing, photocopying, binding and general subsistence on trips and visits. Following graduation, you may need to pay a subscription to a professional body for some chosen careers.
Specific additional costs for studying at Lancaster are listed below.
Lancaster is proud to be one of only a handful of UK universities to have a collegiate system. Every student belongs to a college, and all students pay a small College Membership Fee which supports the running of college events and activities.
For students starting in 2023, the fee is £40 for undergraduates and research students and £15 for students on one-year courses. Fees for students starting in 2024 have not yet been set.
To support your studies, you will also require access to a computer, along with reliable internet access. You will be able to access a range of software and services from a Windows, Mac, Chromebook or Linux device. For certain degree programmes, you may need a specific device, or we may provide you with a laptop and appropriate software - details of which will be available on relevant programme pages. A dedicated IT support helpdesk is available in the event of any problems.
The University provides limited financial support to assist students who do not have the required IT equipment or broadband support in place.
For most taught postgraduate applications there is a non-refundable application fee of £40. We cannot consider applications until this fee has been paid, as advised on our online secure payment system. There is no application fee for postgraduate research applications.
For some of our courses you will need to pay a deposit to accept your offer and secure your place. We will let you know in your offer letter if a deposit is required and you will be given a deadline date when this is due to be paid.
If you are studying on a programme of more than one year’s duration, the tuition fees for subsequent years of your programme are likely to increase each year. Read more about fees in subsequent years.
Details of our scholarships and bursaries for 2024-entry study are not yet available, but you can use our opportunities for 2023-entry applicants as guidance.
Check our current list of scholarships and bursaries.
The information on this site relates primarily to 2023/2024 entry to the University and every effort has been taken to ensure the information is correct at the time of publication.
The University will use all reasonable effort to deliver the courses as described, but the University reserves the right to make changes to advertised courses. In exceptional circumstances that are beyond the University’s reasonable control (Force Majeure Events), we may need to amend the programmes and provision advertised. In this event, the University will take reasonable steps to minimise the disruption to your studies. If a course is withdrawn or if there are any fundamental changes to your course, we will give you reasonable notice and you will be entitled to request that you are considered for an alternative course or withdraw your application. You are advised to revisit our website for up-to-date course information before you submit your application.
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