msc business analytics dissertation topics

Business Analytics Dissertation: what to expect and how to make the most of it

• Tuesday, June 18, 2019

Juan Felipe Alvarez

• minute read

My academic year has been filled with numerous activities. Ever since fresher’s week started in September, there have been things going on (both academic and non-academic) and I can assure you that you will have a very intense and gratifying year as an MSc student.

Now that it's June and I have already finished with classes and exams, it is time to focus on the dissertation. I am writing this blog in order to let you know what to expect and my top tips for enjoying the experience.

Let’s start with the basics: what is a dissertation and what are the business school’s expectations? The objective of the dissertation is to provide students with an opportunity to produce an original piece of work and specialize in a particular topic of interest. The typical word count would be between 15,000 and 18,000 words, depending on the topic and needs to be developed between June and September.

The MSc in Business Analytics has a very pragmatical approach to dissertations; they do this, so you can try and solve a real-world problem with real-world data, which I think is exciting and will teach you applied problem-solving skills. You can either propose your own topic or choose to participate in one of the several ongoing research projects that the University has. If you have a very strong interest in one field and have the means to obtain the data, you can propose your own topic before February. However, most students decide to take part in the topics that are proposed by the University, like I did.

By the end of January, we received a list containing more than 60 different projects that were very diverse in terms of research field and industry. Many of them were in collaboration with companies that had proprietary datasets and were interested in analysing them thoroughly, while others dealt with publicly available datasets. In regards of the type of analysis there was plenty to choose from: text mining and natural language processing, machine learning modelling, simulation, operations research, forecasting, optimisation and decision analysis, just to mention a few. One of the ones that really caught my eye was to build a machine learning model to predict the best strategy for goalkeepers during penalty kicks.

We had to select 7 topics then rank them and the programme director would try to assign you the topic that was highest in your priority. My suggestion that will help you make an informed choice is to read very well the project description and understand what it is about. Don’t choose the dissertation topic only because the company has a well-known name. For me, it is more important to choose a type of analysis that you really like and feel comfortable doing. If you already know the supervisor, you could drop him an email asking for more information about the project and about the required skills.

You will be assigned a topic and a supervisor by the beginning of March and if you selected a topic with a company, some meetings might start taking place during the following months. It is very important to talk early to your supervisor and my tip would be to take the initiative and contact them with plenty of time. Your supervisor will help you to structure the research question, build the appropriate methodology and assess the quality of your work. Make sure you establish clear communication from the beginning in order to make your life easier. Another tip I could give you is to be ordered with your meetings and keep a minute for each one, so you can keep track of the tasks you are supposed to be doing.

A little bit about my topic: In case you are interested, I will be working with Europe’s Largest digital healthcare providers and I will be creating a model to optimise their medicine buying strategy. I have already met the company’s representatives and I hope to be able to frame my research question in the following weeks.

In order to close, I would like to add that the dissertation might look a little bit intimidating at the beginning. I just wanted to let you know that as soon as you begin to divide the whole deliverable into smaller sub-parts, you will feel that you are making quick progress. I hope that this article would be useful to level your expectations about this topic and I wish you the best when the time comes for you to start with your dissertation.

I'm Juan Felipe Alvarez, an MSc Business Analytics student from the class of 2019. Follow my blogs for an insight into life as a student at Alliance MBS.

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Home » Blog » Dissertation » Topics » Business » Business Intelligence » 99 Business Intelligence Dissertation Topics | Research Ideas

99 Business Intelligence Dissertation Topics | Research Ideas

By Liam Oct 12, 2023 in Business , Business Intelligence | No Comments

Welcome to our insightful blog post, where we delve into an array of compelling business intelligence dissertation topics suitable for aspiring scholars at the undergraduate, master’s, or doctoral levels. Crafting a well-structured dissertation topic is an academic rite of passage, serving as the pinnacle of one’s educational journey. The choice of business intelligence dissertation topics […]

Welcome to our insightful blog post, where we delve into an array of compelling business intelligence dissertation topics suitable for aspiring scholars at the undergraduate, master’s, or doctoral levels. Crafting a well-structured dissertation topic is an academic rite of passage, serving as the pinnacle of one’s educational journey. The choice of business intelligence dissertation topics is paramount to the success of this scholarly endeavor, providing a solid foundation for exploration and analysis. In the realm of academia, akin to the synonyms of environmental economics, these dissertation topics act as the bedrock for understanding and addressing the intricacies of modern business strategies, data analysis, and decision-making processes.

• Download Business Intelligence Dissertation Sample For Your Perusal

The following is a list of business intelligence research paper topics to help choose a good topic.

A list of Business Intelligence dissertation topics:

The integration of business intelligence in smart cities: Analyzing the potential for sustainable urban development.

Business intelligence and sustainability reporting: Analyzing the integration of environmental and social data.

Examining the impact of business intelligence on operational efficiency and cost reduction in the UK healthcare sector.

Does the hotel and tourism industry require business intelligence systems apart of the social media applications it is currently exploiting? An analysis.

Assessing the performance and efficacy of business intelligence to SMEs in the UK.

Evaluating the impact of business intelligence on optimizing procurement and vendor management processes.

The needs assessment and application of business intelligence systems in the hotel and tourism industry.

Analyzing the impact of business intelligence on sustainable supply chain management .

Exploring the influence of business intelligence on mergers and acquisitions: A comparative analysis.

The future of mobile commerce in Africa- an assessment of physical infrastructure and software compatibility.

Business intelligence for crisis response and recovery: A post-pandemic analysis in the hospitality industry.

A comprehensive review of business intelligence adoption and its impact on organizational performance in UK small and medium-sized enterprises.

Exploring the synergy of cognitive psychology in enhancing business intelligence decision-making.

The role of business intelligence in enabling data-driven decision-making in healthcare organizations.

Analyzing the role of business intelligence in optimizing customer journey mapping and user experience.

Do managers require formal training to use business intelligence? Perspectives from the UK.

Evaluating the effectiveness of business intelligence in optimizing risk assessment and compliance in banking.

The use of self-service business intelligence for small businesses- an exploration.

Business intelligence critical success factors- an understanding.

Exploring the impact of business intelligence on enhancing cybersecurity measures in organizations.

How does business intelligence aid project lifecycle management?

How do accounting firms utilize business intelligence? An investigation from the UK.

Espionage, hacking and business intelligence- what is happening?

Business intelligence and organizational learning: A review of knowledge management strategies and practices.

The role of organizational culture in adoption of business intelligence systems in emerging economies: the role of national culture and critical success factors in adoption.

The impact of business intelligence on decision-making processes in the UK public sector: A comprehensive review.

Business intelligence and its role in fostering innovation and creativity within organizations.

Evaluating the effectiveness of business intelligence in managing global supply chains and logistics.

Analyzing the impact of business intelligence on optimizing healthcare resource allocation and patient care.

Exploring the ethical considerations in business intelligence: A review of best practices and guidelines.

Business intelligence for sustainability: Analyzing the environmental and social impacts of BI strategies.

Bridging the gap between theory and practice for business intelligence models- a systematic literature review.

Business intelligence for crisis recovery and resilience: A post-COVID-19 analysis.

Analyzing the role of business intelligence in optimizing operations and resource allocation in the agriculture sector.

A comparative review of predictive modeling techniques in business intelligence for demand forecasting.

Analyzing the influence of business intelligence on strategic human resource management practices.

The impact of business intelligence on profit generation in the US.

Analyzing the impact of business intelligence on optimizing call center operations and customer service.

Business intelligence in the digital era: An analysis of emerging technologies and their impact on decision-making.

Research on consumer behaviour through mobile business intelligence systems- ethical considerations.

Analyzing the impact of business intelligence on risk management in the global financial industry.

Business intelligence in the era of big data: A review of challenges and opportunities.

The effect of data quality on business intelligence accuracy and decision-making in the UK retail industry.

The impact of business intelligence on improving operational efficiency in the manufacturing sector.

A review of business intelligence applications in improving educational outcomes and student performance.

A review of business intelligence applications in optimizing marketing campaigns and strategies.

An extended literature review of business intelligence- parameters, models and implications.

Business intelligence and data mining and the creation and applicability of the Web Business Intelligence- developing an understanding.

Analyzing the impact of business intelligence on optimizing digital advertising and targeting strategies.

The integration of business intelligence in the pharmaceutical industry: Analyzing drug development and regulatory compliance.

Evaluating the influence of market intelligence on strategic pricing decisions in the telecommunications sector of the UK.

Business intelligence and data analytics in talent management : A review of effective HR strategies.

Organizational culture and business intelligence- tracing a relationship.

Analyzing the role of business intelligence in enhancing supply chain efficiency and resilience: A case study approach.

Exploring and evaluating the future of business intelligence.

How relevant is mobile business intelligence to multinationals in implementing disaster management programs in emerging economies?

Business intelligence and risk mitigation: Analyzing strategies for effective decision-making in volatile markets.

The integration of business intelligence in the retail industry: Analyzing consumer behavior and demand forecasting.

Business intelligence for sustainable tourism: A review of data-driven strategies for responsible travel.

Business intelligence for enhancing government services and public sector performance: A review.

Business intelligence and customer experience: A review of personalized marketing strategies.

A historical perspective of business intelligence, current practice and future developments.

Assessing the effects of data privacy regulations on business intelligence practices in the UK: A comparative study.

An investigation into the role of data governance in enhancing the effectiveness of business intelligence strategies in UK organizations.

Exploring the potential of business intelligence in optimizing project management and resource allocation.

Analyzing the effects of business intelligence on customer churn prediction and retention strategies.

An analysis of agile analytics as an extension of the rapidly growing business intelligence systems- applications and barriers.

Business intelligence for personalized healthcare: A review of data-driven patient treatment and diagnosis.

Evaluating the influence of business intelligence on e-commerce user experience and conversion rates.

Business intelligence and waste reduction: A review of data-driven strategies for sustainable operations.

Business intelligence and circular economy: A review of data-driven strategies for sustainable resource management.

Business intelligence and its role in enhancing innovation and product development processes.

Optimizing business intelligence strategies for global expansion and competitive advantage in international markets .

Business intelligence and environmental sustainability: A review of data-driven strategies for reducing ecological footprint.

The role of business intelligence in accelerating digital transformation: A review of successful implementation strategies.

Exploring the impact of business intelligence on optimizing manufacturing processes and efficiency.

The role of business intelligence in optimizing energy consumption and sustainability in organizations.

Big data and meta fraud—using business intelligence concepts to establish theoretical links.

Exploring the impact of business intelligence on optimizing procurement and vendor relationships in the oil and gas industry.

Business intelligence for disaster response and recovery: A post-COVID-19 analysis in the tourism industry.

The integration of business intelligence in enhancing customer engagement and satisfaction in the service industry.

Business intelligence for optimizing energy consumption and sustainability in the manufacturing sector.

A comparative analysis of business intelligence usage in developed and developing countries.

Procurement and logistics and use of business intelligence- exploring the degree of efficacy achieved.

Analyzing the role of business intelligence in optimizing marketing ROI and attribution modeling.

Business intelligence and disaster recovery: A review of strategies for data resilience and continuity.

Valuing business intelligence within the context of multinationals, SMEs, and family-owned enterprises.

Blogs as collectors of consumer preferences for business intelligence.

Business intelligence and its impact on corporate social responsibility strategies and reporting.

The role of business intelligence in optimizing public transportation and reducing congestion in urban areas.

The evolution of business intelligence: A comprehensive review of historical trends, current practices, and future prospects.

The role of business intelligence in optimizing inventory management and supply chain resilience.

Exploring the relationship between organizational culture and successful business intelligence implementation in UK multinational corporations.

Leveraging business intelligence for effective crisis management: A post-COVID-19 perspective.

Business intelligence and workforce optimization: A review of data-driven strategies for talent management.

Evaluating the effectiveness of business intelligence in risk assessment and fraud detection in the financial sector.

The future of business intelligence: Analyzing trends and emerging technologies shaping the industry.

A review of business intelligence tools and techniques for sentiment analysis in social media data: Applications in the UK hospitality sector.

There you go. Use the list on business intelligence dissertation topics well and let us know if you have any comments or suggestions for our topics-related blog posts for the future or looking to get help with dissertation writing , send us an email at [email protected] .

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UCL School of Management

University college london, ashleigh topping | 23 march 2022, msc business analytics student wins cdrc masters dissertation scheme award.

As a part of the MSc Business Analytics programme at UCL School of Management, students undertake a practical consulting project with a company or an independent research project. Using the skills and knowledge they have developed on the programme, students analyse data and come up with actionable insights which may be around improving a particular area of the business, changing a process, or getting a more thorough understanding of its customers and target market.

Disa Ramadhina from the Class of 2021, shares her experience from a project with the Consumer Data Research Centre (CDRC) and Entain , an FTSE 100 company specialising in sports betting and gaming interactive entertainment.

Disa shares what she learnt from the project, her reaction to winning the best dissertation award, and how the skills she developed studying MSc Business Analytics at UCL School of Management have prepared her for her role as a Data Analyst at Entain.

What attracted you to working on a project in partnership with the consumer data research centre (cdrc)?

Prior to starting my Master’s at UCL School of Management, I studied Psychology at King’s College London, so I’ve always been interested in the topic of consumer behaviour. Applying to a project in partnership with the CDRC was therefore a natural next step.

I came across the CDRC (a centre which leads engagement between industry and academia) through the MSc Business Analytics Program Director, David Alderton. I was particularly drawn to the project topic by Entain. I was unfamiliar with the company at first, until I realised they owned the betting brands Ladbrokes and Coral. Having been in London for four years, I’ve quite often come across their betting shops while walking around the city.

Ultimately, it was Entain’s reputation as a leading company within the gaming and sports betting industry, as well as the opportunity to analyse consumer behaviour, that motivated me to apply to their project in partnership with the CDRC.

WHat problem or need was investigated through the student consulting project?  

The pandemic-driven changes in consumer behaviour led to the hypothesis that during lockdown, a lot of Entain’s new online customers have a retail background, i.e. they are retail customers who may have migrated online due to retail shop closures in the UK.

With methods of classification, we can predict whether an online customer has a retail background; and we found demographical and behavioural differences between groups of online customers with and without a retail background. In turn, the findings of the project can be used to further investigate the differences in manifestations of problem-gambling between the two customer groups to create a more sustainable customer base. More on the project can be found here .

how do you think the skills that you learned on the msc business analytics programme helped you support entain?

The project was dependent on the use of programming tools, which I had no prior knowledge of before the programme. The modules Statistical Foundations of Business Analytics, Marketing Analytics, Programming, and Predictive Analytics particularly helped me support Entain throughout the project, as they taught me to utilise R and Python to analyse data, and to train machine learning models. Not only that, but the way the module leaders at the school dealt with the pandemic and structured our online learning taught me soft skills, which helped me adjust to the ways of online working. This was particularly useful while conducting the project during the pandemic, as the Entain team were based in Gibraltar while I was based in London.

Can you tell us a little about winning the best dissertation award? 

After completing the project, I was notified by the CDRC that I was shortlisted for the 2021 cohort’s top three dissertations. I was invited to present my project alongside other shortlisted candidates, which was followed by a virtual prizegiving ceremony. Winning the best dissertation award was very rewarding, and I would like to share two lessons that I have learned:

     1. Projects come in different shapes and sizes

Having the opportunity to watch other candidates’ presentations of their projects gave me insights into what other students worked on for months, which in hindsight was completely different to my project. This showed me that dissertation projects encompass a broad range of topics, which made me appreciate the scale at which analytics could be applied into.

     2. The importance of communication skills

I learned that having effective communication skills is critical to dissertation projects. I thought, how do I present my results so they are meaningful to the audience? Whether it be academics, or business stakeholders, no matter how good the analysis or how complex the methodology, the project must be communicated well for others to appreciate it. 

When did you graduate and what have you been doing since you graduateD?

I graduated from MSc Business Analytics in December 2021. Since then, I have been working as a Data Analyst at Entain in their Compliance/Safer Gambling Analytics Department. Within the role, I am responsible for managing end-to-end analytics projects relating to the management of customer journeys to promote safer gambling. The projects start with data extraction and analysis through SQL, R, and Python, and end in translating the findings into actionable insights presented through PowerPoint or visualized through Tableau dashboards. 

I would like to give special thanks to the Entain Gaming team, Piotr Smolinski, Joana Georgieva, and William Collins, for guidance and mentorship throughout the project; to the CDRC for the opportunity to work on the Masters’ Dissertation Scheme; and to David Alderton for the support from UCL as a Program Director and Personal Tutor.

MANM389 - DISSERTATION FOR BUSINESS ANALYTICS - 2023/4

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MSc Business Analytics: Operational Research and Risk Analysis

Year of entry: 2024

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We require a First or Upper Second class honours degree (2:1, with 60% average) from a UK university or the overseas equivalent in a quantitative subject such as mathematics, statistics, physics, engineering, computing, management science or economics. 

When assessing your academic record we take into account your grade average , position in class and the standing of the institution where you studied your qualification.  We particularly welcome applicants from institutions of high ranking and repute.

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Learn the fundamental theories, approaches and analytical toolkit of data analytics, decision sciences, applied operational research and statistics.

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For entry in the academic year beginning September 2024, the tuition fees are as follows:

• MSc (full-time) UK students (per annum): £18,000 International, including EU, students (per annum): £33,000

Further information for EU students can be found on our dedicated EU page.

The fees quoted above will be fully inclusive for the course tuition, administration and computational costs during your studies.

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Due to the competition for places and limited availability, our courses require a deposit of £1000 to cover non-recoverable costs and secure your place. The deposit will be deducted from your tuition fees when you register on the course.

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• you are refused a visa or entry clearance to enter the UK (proof must be submitted)

If an offer has been made specifying an English Language condition which you do not meet, the Admissions Team will require the official certificate of an English Language test taken after the date of offer as evidence that you have attempted to meet your offer conditions for a refund to be approved. The English Language test certificate provided with your application documents will not be accepted as proof that you have attempted to meet your offer conditions as such a certificate will predate the offer.

If an offer has been made specifying an academic condition, the Admissions Team will require the official university documentation showing that you have not met this academic condition from the institution at which you have studied, as evidence for a refund to be approved.

The Admissions Team reserves the right to refuse to refund of any deposit that does not meet with the requirements outlined above.

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All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

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Your statement of purpose should cover the areas outlined below: 

• Tell us why you are interested in the MSc Analytics: Operational Research and Risk Analysis course at Alliance MBS and how the course will impact on your future
• Please list any other quantitative courses or qualifications you have taken in addition to your undergraduate degree
• Describe what makes you an outstanding applicant and describe your potential to contribute to all aspects of the course.

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We can only process applications with the following documents:

• valid English language qualification
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• statement of purpose (this is included as part of your application form, you do not need to email your statement of purpose directly to the Admissions Team)

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Course details, course description.

• Gain a solid theoretical foundation and quantitative skills, alongside practical problem-solving techniques
• Apply your knowledge to real-life scenarios using case studies, individual and team consulting-based assignments, presentations and software tools
• Use SAS Enterprise packages as part of the `Data Analytics for Business Decision Making' unit. 
• Choose from a broad range of options to meet your interests or career aspirations
• Prepare for a career in consultancy, finance, retail, manufacturing, government analytics units, defence, IT systems, outsourcing and telecoms.

Special features

We run a core unit on Data Analytics which will introduce students to SAS Enterprise Guide and Enterprise Miner and other specialized software tools.

Recent highlights  included guest talks and dissertation projects with a wide range of industry partners.

The course is closely linked with the Decision and Cognitive Sciences Research Centre , meaning that you benefit from teaching that incorporates the latest research. You may also find opportunities to collaborate with ongoing research projects and attend research seminars organised by the Research Centre. 

Coursework and assessment

Assessment varies depending on course units taken.  It may include a combination of individual course work, group project assessment and presentations, assignments, in-class tests and examination. 

The dissertation is normally undertaken.

Course unit details

During the course you will be taking 180 credits in all. The eight taught modules during semester one and two total 120 credits and consists of both compulsory and optional taught units which can be viewed in the list below.  

The core courses unit introduce you to mathematical principles and practical tools for Optimization, Decision Making, Data Analytics, Statistical Analysis, Simulation and Risk Analysis. Specific software packages include SAS, SPSS, Excel, Excel Solver, SIMUL8, iThink, Risk Solver, IDS, and Python.

Over the summer period, you will carry out your Research Dissertation, worth 60 credits.  Examples of recent dissertation project topics include:

• Predictive analytics in direct marketing
• Systematic risk modelling and analysis for enterprise risk management
• Predicting the stock price using social media sentiment analysis
• Data analytics in sports
• Data analytics on the net-zero carbon goals of sustainable cities

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The course unit details given below are subject to change, and are the latest example of the curriculum available on this course of study.

Scholarships and bursaries

Disability support, career opportunities.

There are many potential career roles for postgraduates with an understanding of analytical approaches in business and management - including job titles such as operational research analyst, systems analyst, risk analyst, financial analyst, performance analyst, business analyst, data analyst, marketing analyst, data scientist, business modeller, and operations, logistics, production, project, risk, quality, performance, or general manager.

Employers include general and specialist consultancies, the finance, retail and manufacturing sectors, government analytics units, defence and major 'solution providers' in IT systems, outsourcing and telecoms.

In many of these areas an MSc is generally accepted as highly desirable for developing an initial career in the field. In addition to preparing you for specialist professional work, the course is also a valuable preparation for further study and for research degrees.

Recent recruiters 

America Veintiuno, Aon, Bank of East Asia, BIP Consulting,  Dell, Deloitte, Google, Goldman Sachs, Hainan Airlines, HMR, HSBC, IBM, PwC, Bank of China, Royal Bank of Scotland, Saudi Aramco, Disney Analytics, Sky, Swiss Re, Infosys and Peak AI.

Read more about graduate career destinations >> 

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• The University of Warwick

Dissertation award for MSc Business Analytics student

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• Graduate won first prize in CDRC Masters Research Dissertation Programme
• Yifan competed against 17 other projects to scoop the top prize
• The programme links retailers with students who conduct analytic research 
• Sainsbury's are integrating h er research to optimise their online business

Yifan Cao, who recently graduated with an MSc Business Analytics, rounded off her Masters with an award-winning dissertation project. She won first prize in the Consumer Data Research Centre (CDRC) Masters Research Dissertation Programme, for her dissertation entitled “An Application of Multinominal Logit Modelling in E-fulfilment Demand Management”, which was sponsored by Sainsbury’s.

The CDRC is funded by the Economic and Social Research Council (ESRC) and consists of researchers at Leeds, UCL, Liverpool and Oxford. The Master Research Dissertation Programme links retailers with students nationally who conduct analytic research on retail data. This year, Yifan competed against 17 other projects to scoop the top prize, and a description of her work will be featured in a future issue of the Operational Research Society's Impact magazine.

Speaking about Yifan’s award, Guy Lansley from CDRC commented: “Generally, all the projects this year were really good so it was a particular achievement.”

The award also resulted in prize money of £500, however the biggest reward for Yifan was what she learned during the process. She said: “Taking the project with Sainsbury’s has been a great challenge and yet the best decision I have made. It taught me a very valuable lesson, that efforts can really pay off. The whole experience not only strengthened my resume, but also enhanced my technical and soft skills.”

Yifan conducted an empirical study using online grocery sales data from Sainsbury’s to model customer choice behaviour in the context of delivery time slot selection. Specifically, she devised and estimated the parameters of a nested choice model that first considers the choice of the day of delivery, followed by choice of the time slot within a given day. The choice model has been validated using some holdout sample data and has been found to predict slot choice behaviour very well. The output of this work can be used, for example, to improve the efficiency of home delivery logistics.

Related course: MSc Business Analytics

Joel Lindop, Online Forecasting & Optimisation Manager at Sainsbury’s, said of the research: “Yifan’s project broke new ground for Sainsbury’s in providing a methodologically robust analysis of the interaction between the slots offered to customers and their likelihood of their placing orders. Her approach is being integrated within our in-house tools for the management of slot availability to optimise performance of the Sainsbury’s Online business. We thank Yifan for her valuable contribution.” 

Speaking of her success, Yifan said: “Having won the reward was a great surprise. I am very grateful for being chosen as first place and I would never have been able to achieve this without the full support from both my academic supervisor Arne Strauss and industrial Supervisor Matthew Pratt. I am lucky to have been given this great opportunity, and I am glad that I did not disappoint the people whom put faith in me.”

Yifan conducted her research as part of her MSc Business Analytics, which teaches students how to gain insights from large data sets through the use of statistical methods, optimisation techniques and predictive models, and apply these to business problems.

Arne Strauss, Course Director of MSc Business Analytics, and Yifan’s Dissertation supervisor, said: “Yifan produced excellent work on a challenging topic; the estimation of a choice model as complex as this is not trivial. She overcame major technical obstacles, and provided the retailer with valuable insights.

“External dissertation projects are an excellent way of experiencing analytic consultancy work in practice. It is a highly valuable and challenging learning experience with the potential to make an impact on real business operations as Yifan’s work demonstrates.”

Warwick Business School’s MSc Business Analytics course has been ranked seventh in the world in a new QS World University Ranking .

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Analytics Insight

10 Best Research and Thesis Topic Ideas for Data Science in 2022

These research and thesis topics for data science will ensure more knowledge and skills for both students and scholars

• Handling practical video analytics in a distributed cloud:  With increased dependency on the internet, sharing videos has become a mode of data and information exchange. The role of the implementation of the Internet of Things (IoT), telecom infrastructure, and operators is huge in generating insights from video analytics. In this perspective, several questions need to be answered, like the efficiency of the existing analytics systems, the changes about to take place if real-time analytics are integrated, and others.
• Smart healthcare systems using big data analytics: Big data analytics plays a significant role in making healthcare more efficient, accessible, and cost-effective. Big data analytics enhances the operational efficiency of smart healthcare providers by providing real-time analytics. It enhances the capabilities of the intelligent systems by using short-span data-driven insights, but there are still distinct challenges that are yet to be addressed in this field.
• Identifying fake news using real-time analytics:  The circulation of fake news has become a pressing issue in the modern era. The data gathered from social media networks might seem legit, but sometimes they are not. The sources that provide the data are unauthenticated most of the time, which makes it a crucial issue to be addressed.
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• Secure federated learning with real-world applications : Federated learning is a technique that trains an algorithm across multiple decentralized edge devices and servers. This technique can be adopted to build models locally, but if this technique can be deployed at scale or not, across multiple platforms with high-level security is still obscure.
• Big data analytics and its impact on marketing strategy : The advent of data science and big data analytics has entirely redefined the marketing industry. It has helped enterprises by offering valuable insights into their existing and future customers. But several issues like the existence of surplus data, integrating complex data into customers’ journeys, and complete data privacy are some of the branches that are still untrodden and need immediate attention.
• Impact of big data on business decision-making: Present studies signify that big data has transformed the way managers and business leaders make critical decisions concerning the growth and development of the business. It allows them to access objective data and analyse the market environments, enabling companies to adapt rapidly and make decisions faster. Working on this topic will help students understand the present market and business conditions and help them analyse new solutions.
• Implementing big data to understand consumer behaviour : In understanding consumer behaviour, big data is used to analyse the data points depicting a consumer’s journey after buying a product. Data gives a clearer picture in understanding specific scenarios. This topic will help understand the problems that businesses face in utilizing the insights and develop new strategies in the future to generate more ROI.
• Applications of big data to predict future demand and forecasting : Predictive analytics in data science has emerged as an integral part of decision-making and demand forecasting. Working on this topic will enable the students to determine the significance of the high-quality historical data analysis and the factors that drive higher demand in consumers.
• The importance of data exploration over data analysis : Exploration enables a deeper understanding of the dataset, making it easier to navigate and use the data later. Intelligent analysts must understand and explore the differences between data exploration and analysis and use them according to specific needs to fulfill organizational requirements.
• Data science and software engineering : Software engineering and development are a major part of data science. Skilled data professionals should learn and explore the possibilities of the various technical and software skills for performing critical AI and big data tasks.

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MBA Dissertation Topics

Published by Jamie Walker at January 5th, 2023 , Revised On August 15, 2023

Your dissertation topic should be unique and well-researched if you want to score a distinction. Developing ideas for a distinction-level MBA dissertation or thesis can be a challenge. Choose topics and areas that have not been sufficiently researched in the past, and you can make a valuable contribution.

Gone are the days when students would hop from one book to another in libraries and search for topics for their dissertations. Considering the amount of free data available from various sources on the internet, you can conduct desk-based research to figure out which topic has been covered extensively in the past and which topics, areas or subjects need attention according to the current scenario.

We have provided several unique MBA dissertation research ideas below. However, if you want more specific ideas customised to your personal needs, you can contact our dissertation writing experts . Our experts have years of experience in finding unique MBA issues for research. They provide free consultancy and help you write the perfect thesis paper on any MBA topic you choose.

Without further ado, here is our list of the best MBA dissertation topics you can consider for your project.

• The Impact of Leadership Styles on Organisational Performance: A Comparative Analysis
• The Role of Corporate Social Responsibility in Enhancing Brand Image and Customer Loyalty
• Analyzing the Effectiveness of Digital Marketing Strategies in the Hospitality Industry
• Investigating the Factors Influencing Employee Engagement and its Relationship with Organisational Productivity
• The Role of Emotional Intelligence in Effective Leadership: A Study of Top Executives in the IT Sector
• Exploring the Impact of E-commerce on Traditional Brick-and-Mortar Retailers
• Assessing the Relationship between Corporate Governance and Firm Performance: Evidence from the Banking Sector
• The Effect of Technological Innovation on Small and Medium Enterprises (SMEs) Growth and Competitiveness
• Analyzing the Impact of Employee Training and Development on Organisational Success
• Examining the Role of Entrepreneurship in Economic Development: A Comparative Study of Developed and Developing Countries
• Investigating the Challenges and Opportunities of Implementing Sustainable Supply Chain Management Practices
• Assessing the Effectiveness of Performance Appraisal Systems in Enhancing Employee Motivation and Job Satisfaction
• Exploring the Factors Influencing Consumer Behavior in Online Shopping: A Cross-Cultural Analysis
• The Impact of Globalisation on International Business Strategies: A Case Study of Multinational Corporations
• Analyzing the Effect of Mergers and Acquisitions on Firm Performance: Evidence from the Pharmaceutical Industry
• Investigating the Role of Corporate Culture in Enhancing Innovation and Creativity
• The Impact of Talent Management Practices on Employee Retention in Knowledge-Intensive Organisations
• Exploring the Strategies for Successful Market Entry in Emerging Economies: A Study of Multinational Companies
• Analyzing the Effect of Leadership Development Programs on Succession Planning in Family-Owned Businesses
• Investigating the Role of Ethical Leadership in Promoting Ethical Behavior within Organisations
• The Role of Emotional Intelligence in Negotiation and Conflict Resolution in the Workplace
• Analyzing the Impact of Corporate Culture on Employee Innovation and Entrepreneurial Behavior
• Exploring the Strategies for Effective Change Management in Organisations
• The Effect of Diversity and Inclusion Initiatives on Employee Performance and Organisational Outcomes
• Investigating the Relationship between Corporate Social Responsibility and Financial Performance: A Longitudinal Analysis
• Analyzing the Factors Influencing Consumer Buying Behavior in the Fashion Industry
• The Role of Corporate Branding in Building Customer Trust and Loyalty
• Examining the Impact of Employee Empowerment on Organisational Agility and Competitive Advantage
• Investigating the Challenges and Opportunities of Implementing Big Data Analytics in Supply Chain Management
• The Effect of Leadership Development Programs on Millennial Career Advancement and Retention
• Analyzing the Role of Technology in Enhancing Customer Experience in the Retail Banking Sector
• The Impact of Corporate Governance Practices on Firm Value: A Comparative Study of Developed and Developing Markets
• Exploring the Factors Influencing Successful International Joint Ventures: A Case Study Approach
• Assessing the Effectiveness of Performance-Based Pay Systems in Motivating Employees and Driving Performance
• The Role of Corporate Entrepreneurship in Achieving Sustainable Competitive Advantage
• Analyzing the Impact of E-commerce Platforms on Small Business Growth and Market Expansion
• Investigating the Relationship between Organisational Culture and Employee Engagement in Remote Work Environments
• Exploring the Strategies for Managing Cross-Cultural Teams in Global Organisations
• The Effect of Leadership Styles on Employee Job Satisfaction and Turnover Intention
• Analyzing the Factors Influencing Customer Loyalty in the Online Food Delivery Industry

More MBA Dissertation Topics With Aim

Topic 1: the impact of social media campaigns on the psychological triggers and conversation rate of customers. a case study of asos..

Research Aim: The research aims to analyse the impact of social media campaigns on the psychological triggers and conversation rate of customers concerning the case study of ASOS.

• To analyse the impact of social media marketing on the brand awareness and psychological triggers of customers.
• To determine the factors contributing to customer conversation rate and how it benefits the marketing strategies of firms.
• To investigate the impact of social media campaigns on the psychological triggers and conversation rate of customers of ASOS.

Topic 2: The impact of big data analytics on understanding online consumer psychology and digital marketing effectiveness of companies.

Research Aim: The research aims to evaluate the impact of big data analytics on understanding online consumer psychology and the digital marketing effectiveness of companies.

• To analyse the impact of big data analytics on organisational performance.
• To determine how online consumer psychology can be analysed for better digital marketing performance.
• To investigate the impact of big data analytics on understanding online consumer psychology and digital marketing effectiveness of companies.

Topic 3: How organisational culture and power impact conflicts and compliance in MNCs.

Research Aim: The research aims to analyse how organisational culture and power impact conflicts and compliance in MNCs.

• To analyse the impact of organisational culture on employee communication and compliance.
• To determine the influence of organisational power on conflicts and employee performance in MNCs.
• To investigate the impact of organisational culture and power impacts conflicts and compliance in MNCs.

Topic 4: Investigating the impact of gender equity in the workplace on worker wellbeing, innovation intensity and quality decision making.

Research Aim: The research aims to investigate the impact of gender equity in the workplace on worker wellbeing, innovation intensity and quality decision making.

• To analyse the influence of gender equity on employee performance and wellbeing.
• To examine the contributing factors of innovation and quality decision making across organisations.
• To investigate the impact of gender equity in the workplace on worker wellbeing, innovation intensity and quality decision making.

Topic 5: The impact of servant leadership on the market performance and wellbeing of the organisational stakeholders. The case study of Balfour Beatty.

Research Aim: The research aims to evaluate the impact of servant leadership on the market performance and wellbeing of the organisational stakeholders concerning the case study of Balfour Beatty.

• To analyse the implications of servant leadership on the market performance of organisations.
• To evaluate the influence of servant leadership on stakeholder wellbeing and organisational resilience.
• To investigate the impact of servant leadership on the market performance and wellbeing of the organisational stakeholders of Balfour Beatty.

MBA Trending Research Topics

Topic 6: comparative analysis between creative advertising and direct marketing.

Research Aim: Advertising in the business world has now changed. Direct marketing has been replaced with creative advertising. Even though the two types are very different, creative advertising has done more good to businesses than direct marketing. With today’s world evolving and advertising taking over the corporate world, companies need to keep up with their creativity. This will not only help them in gaining customers’ attraction but will also help the company in earning huge profits. Considering this recent change in the corporate world, this research will compare the two methods and conclude which method is the most suitable for businesses.

Topic 7: Innovation Management in Companies – The Way Forward

Research Aim: To keep up with the ever-evolving business world and its needs, companies need to stay up to date to make sure that they bring in new methods, products and ways to manage their company and take it forward successfully. This research will talk about how businesses should consider innovation management and how they should incorporate the same in their way of doing business. Not only will businesses gain from innovating their products or processes, but they will also understand how successful it can be for the business once it is effectively implemented. Thus, the main aim of this research will be to help businesses understand the importance of innovation and talk about how innovation has helped many reputed names in the corporate world.

Topic 8: Analysis and Evaluation of Investment Strategies in the Retail Sector

Research Aim: There are tons of investment strategies available for businesses. Companies should make investment-related decisions based on their financial inflow and the position of their business. Considering both these aspects while investing is critical. If they fail to do so, they will invest without a clear idea of how the investment might turn out, thus putting their company and its money in jeopardy. The investment strategies play a huge role in the company’s success, as they help in earning more profits. Thus, if the investment strategies are suitable for the business, they will be favourable for all. This research will discuss the different types of investment strategies that companies in the retail sector can take advantage of. Furthermore, this research will talk about the various investment strategies available for companies in the retail sector and how they can utilise them to their advantage.

Topic 9: Flexible Working Hours – How Will it Help Companies in Retaining and Recruiting Employees

Research Aim: The world is evolving with the way business is being conducted. All businesses need to understand the importance of flexible working hours along with fixed working hours. Introducing flexible working hours can help the business grow, as employees will be free to choose their working time, thus will put in more effort in their job. On the other hand, working mothers will be the ones who will benefit the most. They will be able to handle their home, as well as their office. This research will talk about the concept of flexible working hours, how it helps employees, and how the company can ensure that employees are retained through this new policy. The success rate of new recruitment based on flexible working hours will also be discussed in this research.

Topic 10: Technology as a Competitive Advantage – Investigating its Effectiveness

Research Aim: Today, no business can survive without technology. Implementing the right kind of technology is the key to success for any small, medium or large business enterprise. Businesses must make sure that they understand how important technology is for their company, how it will ease the business process, and bring down the cost of doing business. In addition to this, technology also acts as a competitive advantage in today’s world of business. Companies with better technologies have an advantage in the market where they can perform better and earn better profits than other companies in their industry. This research will talk about how technology assists companies in staying ahead of each other in the business and how each company should make sure that the right technology is implemented in the system not to help compete in the corporate world but also to offer ease of working its employees.

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MSc Business Analytics

The MSc Business Analytics is a professionally accredited, one-year specialist programme for graduates with a bachelor's degree with a substantial quantitative component, and highly qualified graduates from other backgrounds with demonstrable advanced quantitative skills. It will suit graduates or early career professionals who wish to pursue a career in business analytics across sectors such as digital marketing, human resources, logistics, retail, finance, banking, insurance, healthcare, and agriculture.

The programme accreditation is awarded by the Institute of Analytics (IoA) , which is the professional body for analytics and data science professionals across the world. Their mission aligns with our vision of developing and promoting the highest professional and ethical standards in the realm of data analytics. By becoming IoA Corporate Partners, we demonstrate our commitment to staying up to date in the fast-evolving field of analytics and data science. This in turn, will help to boost your employability, ensuring you are well-equipped to navigate the fast-changing landscape of analytics.

As well as benefiting from the accreditation, the MSc Business Analytics has been created in partnership with industry professionals from IBM, LV and UCL/IBM Industry Exchange Network. It provides students with the opportunity to work on business analytics projects and offer data-driven solutions to a real-life managerial decision-making problem or challenge, where possible, in partnership with IBM and other private, charity, and public sector organisations.

Students will gain a critical understanding of organisational, societal, and ethical issues in the use of Business Analytics. These issues are crucial for many organisations that seek to provide data-driven services while trying to balance innovation and competitiveness with public trust and corporate social responsibility. Examples of projects include optimisation of resource allocation, people analytics to support hiring decisions, sales forecasting, performance measurement and evaluation, customer segmentation, and sentiment analysis to improve a business strategic direction.

At the end of the programme, students will have learned:

• technical skills in data preparation (such as identification, extraction, and cleaning of data);
• the use of statistical and machine learning techniques to perform data mining and predictive analytics;
• the formulation and execution of statistical and mathematical models to optimise challenging business decisions;
• the visualisation, interpretation, and reporting/communication of results from statistical analysis.

Students will learn how to perform ad-hoc data analytics in Python and through specialised software and decision support platforms such as Lingo. They will also be offered guidance on how to successfully receive professional accreditation from the UK’s Operational Research (OR) Society as well as other affiliated organisations such as the Alliance of Data Science Professionals.

You will be taught by leading academics whose research tackles the major issues in business analytics. 88% of our Business and Management research is rated as world-leading or internationally excellent (REF 2021), reflecting its impact on shaping policy and practice. Bristol is a vibrant, ambitious, and entrepreneurial city and home to SETSquared, the world's top university business incubator (UBI Global).

On demand academic talks

Hear directly from an academic giving you a deeper insight into this programme.

Programme structure

Successful completion of five core units, two optional units, and a final applied research project lead to an MSc Business Analytics award.

The core units in the first term will provide students with the foundations in descriptive, predictive, and prescriptive analytics. You will learn skills in Python, data preparation, application of statistical models and machine learning, data visualisation to gain business insights, decision-making and optimisation for complex business processes, across a range of business areas (for example, marketing, people analytics, project analytics, financial portfolio optimisation, productivity analysis).

Depending on which elective you choose in the second term, you will have the opportunity to advance your knowledge and skills in social media and web analytics (such as social network analysis, Natural Language Processing, sentiment analysis), or optimisation (algorithms for solving large-scale optimisation models), or learn theory and practice of decision-making in business analytics (heuristics and biases in managerial judgment), or consult on a real-life business analytics project, where possible, with external industry partners.

Across the two teaching blocks, students will learn about ethics and sustainability issues in the adoption of business analytics. After learning research methods, students will have to complete an Applied Research Project in Business Analytics, which may be conducted in partnership with external organisations and will be assessed with a dissertation.

Visit our programme catalogue for full details of the structure and unit content for our MSc in Business Analytics.

World-leading research

The University of Bristol is ranked fifth for research in the UK ( Times Higher Education ).

94% of our research assessed as world-leading or internationally excellent.

Entry requirements

An upper second-class undergraduate honours degree or international equivalent in any of the following subjects:

Operational Research, Management Science, Decision science, Mathematics, Statistics, Data Science, Finance (not Accounting and Finance), Economics, Computer Science, Physics, Engineering, Biomedical/Life Sciences.

An upper second-class honours degree or international equivalent in any discipline that includes 65% or above in at least 2 quantitative units. Examples of acceptable units include:

Advanced Maths/ Algebra /Analysis /Bayesian Modelling /Calculus /Complex Functions /Decision Maths /Differential Equations / Ordinary Differential Equations /Discrete Mathematics /Econometrics /Financial Maths /Game Theory /Geometry /Information Theory /Kinematics /Kinetics /Quantum mechanics /Quantum computing /Linear Algebra /Linear Programming /Macroeconomics / Economics III / IV) /System dynamics /Thermodynamics /Complex systems/equilibrium /Mathematical Programming /Maths/mathematical methods/mathematical models/mathematical skills /Maths for Business / Business Maths /Maths for Economics /Mechanics (any type of mechanics) /Microeconomics (inc intermediate / advanced)/ Economics III / IV) /Multi Variate Analysis /Network Science /Number Theory /Optimisation /Probability (including stochastic models/methods (e.g. Markov chain model, monte carlo models)) /Proof / Intro to Proof /Pure Maths /Quantitative Methods /Statistics/Statistical Methods/Statistical Analysis etc /Time Series Analysis /Forecasting / Physics/Physical computing /Electronic/electrical engineering /Electricity and magnetism /Engineering materials /Geotechnical /materials/structural engineering /Analytical chemistry /Simulation /Probability /Computer Science (incl programming/algorithms) /Mechatronics /Data Mining/Data Science/ Data Analytics/business analytics /Management Science /Decision Analysis and Simulation; decision science /Operational research /Derivatives /Econometrics /Financial Modelling /Quantitative techniques (intro/ advanced) /Quantitative Methods /Quantitative Research Methods /Statistics/Statistical Methods/Statistical Analysis etc /Social network analysis /Computational research methods in the social sciences /Any computational methods /Machine learning /Robotics /Experiment (e.g. experimental design, studies or research; control trials) /Research methods in health/medical/biomedical/natural sciences.

For applicants who are currently completing a degree, we understand that their final grade may be higher than the interim grades or module/unit grades they achieve during their studies.

We will consider applicants whose interim grades are currently slightly lower than the programme's entry requirements. We may make these applicants an aspirational offer. This offer would be at the standard level, so the applicant would need to achieve the standard entry requirements by the end of their degree. Specific module requirements may still apply.

We will consider applicants whose grades are slightly lower than the programme's entry requirements, if they have at least one of the following:

• evidence of significant (minimum of 6 months in a paid role) relevant work experience in sectors such as Digital Marketing, Data Science, Data Engineering, Banking and Finance or roles which require expertise in data analytics or statistics.
• a relevant postgraduate qualification.

If this is the case, applicants should include their CV (curriculum vitae / résumé) when they apply, showing details of their relevant work experience and/or qualifications.

See international equivalent qualifications on the International Office website.

Read the programme admissions statement for important information on entry requirements, the application process and supporting documents required.

If English is not your first language, you will need to reach the requirements outlined in our  profile level B.

Further information about  English language requirements and profile levels .

Fees and funding

Fees are subject to an annual review. For programmes that last longer than one year, please budget for up to an 8% increase in fees each year.

More about tuition fees, living costs and financial support .

Alumni discount

University of Bristol students and graduates can benefit from a 25% reduction in tuition fees for postgraduate study.  Check your eligibility for an alumni discount.

Funding for 2024/25

Further information on funding for prospective UK and international postgraduate students.

Career prospects

The MSc Business Analytics Programme responds to the increasing demand for skills in business analytics both in the UK and abroad.

Our Careers Service offers support and online training to help you identify your career goals, apply for opportunities in professional settings, and perfect your interview technique during your year of study.

Career paths examples include:

• Business Analytics Specialist/Professionals
• Management Consultant
• Product Manager
• Market Research Analyst/Digital Marketing
• Operations/Data Analyst
• Modelling/Data Scientist
• Business Operations Analytics Specialist
• HR/People/Insurance Analytics Specialist
• Financial Analyst.

How to apply

Apply via our online application system. For further information, please see the guidance for how to apply on our webpages.

Due to high demand we will be closing MSc Business Analytics to applicants from   China on   4 December 2023   and to   remaining applicants from outside of the UK   on 24 July 2024 .

Please find out more information by reading our  guidance for when to apply .

Home applicants: 9 August 2024.

Places are limited and allocated on a continuous basis from October 2023 until all places are filled. Early applications are advised to avoid disappointment.

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Data and Decision Analytics (Online) programme structure

An overview of the MSc Data and Decision Analytics (Online) programme structure, including details of the course information across the two years of this part-time degree.

The online Data and Decision Analytics MSc is delivered part-time with a start date in September each year. The programme takes 24 months to complete and combines academic study with practical application.

Please note that live sessions will be scheduled to take place on Mondays and Tuesdays during term time (c.20 weeks each year). These live sessions will be a maximum of 4 hours per week and scheduled 09:00–18:00 GMT.

Planned Live Sessions Timetable for year 1 in 2023/24

The programme encompasses a number of core courses and your studies will culminate with a dissertation.

To give you an idea of what the programme structure and courses might consist of, the below information details the structure and courses for this programme in 2023/24.

Compulsory courses

• Applied Decision Optimisation
• Applied Machine Learning
• Data Analysis and Statistics for Business
• Python Programming
• Storytelling in Data and Decision Analytics

Option courses*

Time series, data management, analytics of decision making under multiple criteria, heuristic optimisation, introduction to stochastic optimisation, advanced stochastic optimisation, dissertation in data and decision analytics.

The dissertation is an in-depth study of a topic in which you are particularly interested in within the field of Data and Decision Analytics. Undertaking the dissertation requires you to develop a deep level of analysis and understanding of the theory and processes of organisations and the business environment through the completion of a piece of individual research.

Past dissertations

Find out what topics past students have researched by browsing a selection of dissertation summaries by our postgraduates:

Dissertation executive summaries

*We will notify applicants of any changes to the programme structure and courses by 15 June in the year of entry to the course. We cannot guarantee that all option courses will run each year and occasionally there will be last minute amendments after this date due to unforeseen circumstances such as staff illness.

The content of individual courses and the programme for any given degree are under constant academic review in light of current circumstances and may change from time to time, with some programmes and courses being modified, discontinued, or replaced.

Due to high demand we cannot guarantee students a place on the optional course of their preference. Equally, if there is not enough interest in a given year for an option course then it may not be viable for us to run that particular option course. Some combinations of option courses may not be possible due to scheduling constraints.

Time Series Forecasting is the actionable mathematical framework that equips aspiring Business Analysts and Data Scientists with the toolkit to perform predictions based on classic models that have stood the test of time as well as elementary Machine Learning models that can tap into the potential of big and nonlinear datasets. In this course the student will be initiated to methods and tactics on how to perform necessary time-series pre-processing; statistically sound model fitting; short-term and long-term forecasting; as well as constructing brief presentations with meaningful visualizations that can be communicated to non-experts.

Data management is the practice of collecting, organizing, and accessing organization's data so it can be analysed for business decisions. In this course, we will survey the theory and practice of data management, from conceptual and logical data modelling, to querying languages, such as Structured Query Language (SQL). Students will gain hands-on experience on these topics by practicing the tools and techniques surveyed on state-of-the-art software (MySQL).

In practice, Multi-Criteria Decision Making (MCDM) methods are very popular in addressing complex problems involving multiple and typically conflicting criteria as well as several stakeholders or decision makers with different preferences with respect to the evaluation criteria. This course aims at training students in the field of MCDM with emphasis on rating, ranking and classification problems and methods with applications in business.

Prescriptive analytics and optimisation have been rapidly developed, and successful applications have emerged, though several methodological challenges remain. Real-world problems tend to be very intricate. Heuristic algorithms are developed to determine near-optimal solutions by iteratively improving the quality of candidate solutions. Heuristics are time-efficient compared to the exact algorithms, allowing decision-makers to resolve the optimisation problem with ill-defined functions and intricate space under mild or no assumptions. This course provides the students with sufficient knowledge to understand and implement modern heuristic algorithms (such as genetic algorithm, simulated annealing, tabu search and swarm Intelligence) to address large-scale, high-dimensional optimisation problems.

This course aims at introducing students to concepts related to the quantitative modelling of decision-making problems under uncertainty. In almost all decision-making problems, data contain uncertainties that are uncontrollable and that need to be factored in to build systematic, long term and optimized decisions. This course covers various philosophies of approaching uncertainty in decision making, providing different perspectives on how to quantitatively conceive uncertainty and how to use these conceptions of uncertainty in formulating solvable decision-making models. The course also covers various tools to solve and analyse these decision-making problems so as to broaden the understanding of decision analytics and provide more versatility in conceiving, understanding, interpreting and solving decision-making models.

This course aims to broaden and deepen the understanding of uncertainty incorporation in decision-making problems. In many decision-making problems, uncertainty revelation does not happen all at once, but rather in a gradual way. This creates a different dynamic when it comes to optimising and adapting decisions to uncertainty revelation. This course discusses how to optimise decisions in cases where uncertainty revelation happens gradually. The course will equip students with tools to conceptualise decision-making problems that can be adapted to “time-stamped” data and translate different decision-making philosophies within this context. It will also teach students how to implement and solve these decision-making problems.

Core and elective courses

Full programme details, including all available core and elective courses can be found on the University Degree Programme Tables website.

Data and Decision Analytics Degree Programme Table