phd data science imperial

Imperial College London Imperial College London

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• Postgraduate taught, Online

Machine Learning and Data Science (Online)

Develop an in-depth understanding of machine learning models and learn to apply them to real-world problems.

Develop an in-depth understanding of machine learning models and learn to apply them to real-world problems

Benefit from flexible learning over 24 months on a fully online course

Build a portfolio and showcase your skills for a future career in mathematics, data or statistics

Course key facts

Qualification, september 2024, £17,175 per year home, £17,175 per year overseas, delivered by, department of mathematics, minimum entry standard, 2:1 in statistics, mathematics, engineering, physics or computer science, course overview.

Accelerate your career in engineering or data science on this online and part-time Master's course.

Via hands-on projects, you'll build a portfolio in everything from probabilistic modelling and deep learning to unstructured data processing and anomaly detection.

This programme will enhance your analytical abilities in relation to mathematics and statistics. You'll gain expertise in tackling complex data by implementing scalable solutions using industry-standard tools, including PySpark.

You'll also consider the ethics and limitations of machine learning, and learn how to ethically apply these techniques to your work.

All learning is delivered online.

Testimonials

This page is updated regularly to reflect the latest version of the curriculum. However, this information is subject to change.

Find out more about potential course changes .

Please note:  it may not always be possible to take specific combinations of modules due to timetabling conflicts. For confirmation, please check with the relevant department.

Core modules

• Individual Project

You’ll take all of these core modules

Ethics in Data Science and Artificial Intelligence Part 1

Explore the ethical implications of the new capabilities offered by data science and artificial intelligence in this comprehensive and technical module.

Delve into topics such as model explainability, causation, fairness, and privacy by examining real-world examples of shortcomings and negative outcomes. Increase your knowledge and skills in depth across two years through this three-part module.

Programming for Data Science

Gain fluency in both R and Python, for proficient use in later modules. Topics include random number generation, using vectors and matrices, working with APIs, and reading from/writing to different file formats.

The module also covers practices for ensuring code correctness, such as profiling, debugging and unit testing, and how to package code for distribution.

Applicable Mathematics

Gain familiarity with statistical and mathematical tools that will be used in later modules.

You’ll review the fundamentals of calculus, linear algebra, and probability theory, as well as other topics including matrix decomposition techniques, convergence of random variables, sample-based statistical inference, and numerical optimisation methods.

Exploratory Data Analytics and Visualisation

Produce convincing narrative summaries and informative visualisations for a variety of complex datasets.

You’ll learn how to evaluate the quality of a given dataset, diagnose and remedy missing and anomalous data, and consider the suitability of different exploratory analyses for various data types including spatial and temporal data.

Supervised Learning

Become familiar with data analysis and modelling, classification and resampling methods, and advanced topics like Random Forest and Support Vector Machines.

Gain the skills and knowledge to choose the appropriate supervised learning technique to effectively analyse and interpret data.

Ethics in Data Science and Artificial Intelligence Part 2

Build on your existing knowledge of ethics in data science and artificial intelligence and explore real-world issues.

Big Data: Statistical Scalability with PySpark

Learn statistical concepts such as parameter estimation with large scale data and explore data sampling strategies in a Big Data world.

Design and develop data analysis procedures using Big Data technology (Hadoop and Spark), learn to utilise Big Data technology to perform a rigorous statistical analysis, and describe and apply mathematical techniques for fitting statistical models at scale and dealing with streaming data.

Bayesian Methods and Computation

Examine subjective probabilities and the Bayesian paradigm for making coherent individual decisions in the presence of uncertainty.

Deep Learning

Select and explore an appropriate deep learning model architecture for a given supervised and unsupervised learning application.

You’ll be able to implement data and training pipelines for different types of neural networks, as well as implement appropriate evaluation measures and model selection strategies for supervised and unsupervised applications.

Unsupervised Learning

Assess the tools and techniques for solving unsupervised learning challenges, exploring topics including clustering, dimension reduction and density estimation.

Ethics in Data Science and Artificial Intelligence Part 3

Complete your studies examining the ethical implications of data science and artificial intelligence.

Unstructured Data Analysis

Learn the mathematics of techniques dealing with three unstructured data types: images, networks, and text.

Master deep learning and well-established statistical methods to tackle unstructured data and implement statistical and machine learning tasks.

Learning Agents

Investigate key decision-making frameworks and develop expertise for taking machine learning beyond the prediction process to formal decision-making processes.

You’ll carry out an extensive research project focused on machine learning and data science, working exclusively on the project in the summer term of Year 2.

Synthesize your learnings over the programme into a single, coherent and novel exercise. You can work on a theoretical, methodological or applied research project depending on your interests.

Teaching and assessment

Balance of teaching and learning.

• Lectures and tutorials
• Independent study
• 22% Lectures and tutorials
• 78% Independent study
• 15% Lectures and tutorials
• 85% Independent study

Teaching and learning methods

Assessment methods, entry requirements.

We consider all applicants on an individual basis, welcoming students from all over the world.

• Minimum academic requirement
• English language requirement
• International qualifications

2:1  in statistics, mathematics, engineering, physics or computer science.

All candidates must demonstrate a minimum level of English language proficiency for admission to Imperial.

For admission to this course, you must achieve the  higher university requirement  in the appropriate English language qualification. For details of the minimum grades required to achieve this requirement, please see the  English language requirements .

We also accept a wide variety of international qualifications.

The academic requirement above is for applicants who hold or who are working towards a UK qualification.

For guidance see our accepted qualifications  though please note that the standards listed are the  minimum for entry to Imperial , and  not specifically this Department .

If you have any questions about admissions and the standard required for the qualification you hold or are currently studying then please contact the relevant admissions team .

How to apply

Apply online.

You can submit one application form per year of entry. You can choose up to two courses.

Application deadlines – Friday 31 May 2024

Application deadline

The deadline for completed applications and references is  Friday 31 May 2024 , to be considered for September 2024.

Application fee

There is no application fee for MRes courses, Postgraduate Certificates, Postgraduate Diplomas, or courses such as PhDs and EngDs.

If you are applying for a taught Master’s course, you will need to pay an application fee before submitting your application.

The fee applies per application and not per course.

• £80 for all taught Master's applications, excluding those to the Imperial College Business School.
• £100 for all MSc applications to the Imperial College Business School.
• £150 for all MBA applications to the Imperial College Business School.

If you are facing financial hardship and are unable to pay the application fee, we encourage you to apply for our application fee waiver.

Read full details about the application fee and waiver

Application process

Find out more about  how to apply for a Master's course , including references and personal statements.

Important information

Important information for applicants from iran, sudan, crimea, cuba, syria and north korea.

The programme is delivered fully online via our in-house platforms. However, some computational labs will be accessible via the Coursera platform. United States export control regulations prevent Coursera from offering services and content to users in certain countries or regions.

More information about which countries or regions are affected can be found on Coursera’s website .

Coursera must enforce these restrictions in order to remain in compliance with US law and, for that reason, we advise that all interested applicants check this information before applying to the programme.

As a result, we are not able to consider applications for the programme for those who wish to study the programme from within these countries.

If any interested applicants have any queries regarding the above, please contact:  [email protected]

Dual enrolment

You cannot register/enrol for more than one award at the same time. This includes awards at Imperial and other universities or institutions. You would need to de-register from your current course before starting. Read more about this in the  Imperial College General Academic Regulations (Section 5.5) .

ATAS certificate

An ATAS certificate  is not  required for students applying for this course.

Tuition fees

Overseas fee, £17,175 per year, inflationary increases.

You should expect and budget for your fees to increase each year.

Your fee is based on the year you enter the College, not your year of study. This means that if you repeat a year or resume your studies after an interruption, your fees will only increase by the amount linked to inflation.

Find out more about our  tuition fees payment terms , including how inflationary increases are applied to your tuition fees in subsequent years of study.

Which fee you pay

Whether you pay the Home or Overseas fee depends on your fee status. This is assessed based on UK Government legislation and includes things like where you live and your nationality or residency status. Find out  how we assess your fee status .

Postgraduate Master's Loan

If you're a UK national, or EU national with settled or pre-settled status under the EU Settlement Scheme, you may be able to apply for a  Postgraduate Master’s Loan  from the UK government, if you meet certain criteria.

The government has not yet published the loan amount for students starting courses in Autumn 2024. As a guide, the maximum value of the loan was £12,167 for courses starting on or after 1 August 2023. 

The loan is not means-tested and you can choose whether to put it towards your tuition fees or living costs.

Goods and Services Tax (GST) for online courses

If you live in a country that imposes a GST for online courses, you may incur an additional tax charge on your tuition fees.

Currently, the countries that charge a GST are:

Find out more how to pay GST and how much it is .

How will studying at Imperial help my career?

Prepare for advanced engineering roles in areas such as AI, data science and machine learning.

With specialised knowledge, you'll be highly sought after in a range of sectors.

These include data scientists, machine learning engineers or computational statisticians.

Further links

Contact the department.

• Email:  [email protected]

Course Director:  Professor Nick Heard .

Visit the  Department of Mathematics website.

Request info

Find out more about studying at Imperial. Receive updates about life in our community, including event invites and download our latest Study guide.

Events, tasters and talks

Meet us and find out more about studying at Imperial.

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Terms and conditions

There are some important pieces of information you should be aware of when applying to Imperial. These include key information about your tuition fees, funding, visas, accommodation and more.

Read our terms and conditions

You can find further information about your course, including degree classifications, regulations, progression and awards in the programme specification for your course.

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• » DOC PhD Regulations & Resources
• » Current Studentships Available
• » Accomodation

PhD/MPhil Research Degrees

Introduction.

The Department of Computing at Imperial College is an internationally leading research institution which offers an exciting research environment for prospective postgraduate students. It has consistently been awarded the highest research rating (5*) in the UK Research Assessment Exercises, including the most recent one held in 2001, and was rated as "Excellent" in the most recent national assessment of teaching quality. The Times Higher Education Supplement recently rated Imperial as the best in Europe and fourth best in the world for technology (see extract ).

Financial Support

The funding details are different for UK, European and Overseas students.

UK Students and Students Eligible for EPSRC Funding

Each year the Department has a number of EPSRC funded DTA studentships which are awarded to suitably qualified research students. These pay for the College fees and provide a bursary for the student's living expenses:

Fees: £3,168 per year Bursary: £13,200 per year

European Students

The current situation is that the Department has some scholarships to fund the fees for suitably qualified European students. The student or supervisor has to find the funding for the bursary. It is not likely that this situation will change in the foreseeable future.

Fees: £3,168 per year Bursary: £13,200 per year (for EU students that fulfill "residency" requirements)

Overseas Students

Scholarships.

Please see Scholarships and Awards for other funding opportunities, in particular the Dorothy Hodgkin Postgraduate Awards. Through its Industrial Liaison Unit, the Department is able to offer enhanced PhD scholarships to selected students. These are supported through generous donations from industrial sponsors. They are allocated early in the first year of study and are tenable for up to 3 years and renewed on an annual basis. The criteria for award and financial value differ between scholarships and are publicised at the start of the selection process.

Please note that the Department does not provide funding for either tuition fees or maintenance. There are sometimes Scholarships available through the Department, College, or individual supervisors. Opportunities for Fellowships, Teaching Assistantship (TA) and Research Assistantship (RA) posts are not that frequent and you would need to refer to www.doc.ic.ac.uk/about/situationsvacant/ on a regular basis to see what positions are available.

How to Apply

Applications are welcome for 2008/ 09 session (entry 2008)

We welcome applications from all suitably qualified candidates. Competition is very strong for places, and applicants are required to have a good MSc degree or equivalent in Computer Science, Mathematics or some IT-related discipline. Candidates who have a BSc degree only will not be considered. Additionally, applicants need to demonstrate strong research potential. Where appropriate, we may encourage applicants to register first for the MSc in Advanced Computing and then, upon successful completion of this, to apply for registration for a research degree. The MSc course includes special research-oriented provisions for students intending to take this route.

To increase your chances of acceptance and to facilitate the processing of your application, please:

• indicate in your application the general research areas and topics that interest you, and potential supervisors for your research in the Department.

The Assistant Registrar (Admissions), Imperial College London, SW7 2AZ.

Contacting the Department

Before contacting the Department, we strongly advise you to read the following:

• The regulations for students: www3.imperial.ac.uk/pgprospectus/infozone/regulationsforstudents

To contact our PhD Admissions Team, please email [email protected] .

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Imperial Computing

• Women in Computing

A Clinician-PhD Candidate’s Journey as a ‘Data Science For All/ Women’ Fellow

19 January 2022

Hello,  my name is Aizaan  and  I’m  a  UKRI  AI4Health PhD Candidate in Artificial Intelligence  (AI)  and Machine Learning  (ML) .  I’m  a Malaysian-born, British-trained physician with  previous  experience in neurology, medical education,  epidemiology,  and public health. My  research  project looks at using AI to diagnose disease progression in brain  tumours  from speech data collected from a mobile app developed by The Brain  Tumour  Charity . 

In September 2021, I  was successfully  selected as a  Data Science for All/ Women (DS4A)   Fe llow  and  was invited to  participate in the ir   Fall 2021 training  programme . This free technical  programme   i s open to candidates in Europe and North America and less than 5% of ~5000 applicants usually get admitted.  The  programme   i s geared towards empowering women from underrepresented groups to pursue careers in data science  and t he Fellowship consisted of two tracks: Executives (for those planning on pursuing formal leadership roles in data-driven teams) and Practitioners (for those intending to become data scientists/ analysts/ quantitative researchers). Executives receive training using no-code cases ,  whilst Practitioners  are  trained to use Python, SQL and Tableau in real-world cases.   

Within a week of applying, however, I was informed that I had been shortlisted for an interview. I was interviewed by one of the teaching assistants on the programme and we had a relatively informal discussion about my PhD as well as my motivation and vision should I be selected as a DS4A Fellow. I thoroughly enjoyed this process as whether I got accepted or not, I found myself connecting with someone across the Atlantic who was as passionate about their research and data science as I was. In less than a week, I was informed that I was accepted! Later, I also found out that I was the only clinician in my cohort.    

The programme    

The 7-week programme consisted of data science lectures delivered on Saturdays, career development lectures on Tuesdays, meetings and career talks from different organisations, weekly meetings with mentors and weekly meetings with teaching assistants culminating in a project called Capstone which required the application of skills and knowledge taught in an academic programme. The programme was extremely well organised and there were always Correlation One staff members at hand to assist with any technical or administrative queries. We had 2-3 project and career-development deliverables to submit per week and were given lifetime access to the DS4A training and careers portal.  

Project showcase     

The fellowship culminated in a project showcase followed by a Grand Finale where the winning teams were selected. I was matched with team members from the same time zones who were able to dedicate similar amounts of time to the Capstone project. With guidance from our mentor and teaching assistants, we were tasked with solving a business problem or completing a social impact project using data-driven methods. My team was fortunate to have our mentor, Dr. Detlef Nauck, who was the Head of AI and Data Science Research at BT and a visiting professor at Bournemouth University.    

He was very supportive and involved in both our career and Capstone project development. He even linked us to the developers of  Einblick , a no-code data analytics platform, in case we needed extra support. Our teaching assistants were data scientists, PhD students, and post-doctoral fellows from Ivy League institutions who helped clarify the problem we were solving and supported us with our analytical methods.    

The team    

My team consisted of masters-degree holders and PhD students from the fields of biology, mathematics, mechanical engineering, AI, epidemiology, econometrics, and clinical medicine. We were all interested in the wealth of data on COVID-19 from freely available repositories and decided to study the impact of vaccine misinformation and uptake. Along with a project report, datafolio and PowerPoint presentation, my team also developed a  dashboard  where users can view where and when misinformation occurred and how this correlated with vaccine uptake at the same time and place.    

Winning the Crowd Favourite Project Award  

It was a challenging process to identify a problem, find suitable datasets and implement unfamiliar methods when we could not meet in person. However, my teammates, mentor and teaching assistants carved time into their schedule (on top of their day jobs!) to make our project a success. Our efforts were rewarded when we won the Crowd Favourite Project award at the Grand Finale. The DS4A/Women fellowship programme certainly was an experience I will never forget.   

I thoroughly enjoyed working with such a diverse group of women who were all keen to learn, humble, experienced, accomplished and dedicated. I will not easily forget when we attended lectures, coded, wrote our report, practised our presentation, and designed our datafolio online together. Not only did my data analytics skills immensely improve during this period, my attitude and approach to research also changed for the better. I became more comfortable with the idea of venturing into the unknown, learning along the way and trusting my abilities.

Reflection  

With my clinical medical background where I had little room for error, I initially found the fear of making mistakes in my PhD paralysing. With my mentor’s guidance, I slowly realised that in research failures are very much part of the process, and the trick is to be consistent with my work, such that the mistakes are made frequently and are then rid of early on.   

The career development exercises that we did with our mentor also helped me take ownership of my diverse experiences as well as gain better clarity of my strengths and professional direction. I am extremely grateful to have been selected for this programme and I hope that retelling my experiences will be of some assistance to others who are embarking on similar paths.   

One comment for “ A Clinician-PhD Candidate’s Journey as a ‘Data Science For All/ Women’ Fellow ”

Thanks for blogging your experience. I’m hoping to do the same once I finish my PhD Coursework and comprehensive exams.

Comments are closed.

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Imperial College London is a leading research and teaching community for AI and related fields, ranked top in the UK for computing and engineering in the REF 2021. We offer doctoral training programmes, master’s degree courses, and executive education options in AI, machine learning, and data science. Our interdisciplinary approach allows us to tackle real-world challenges, and the creation of I-X provides the infrastructure to expand collaboration with companies and startups.

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DiscoverDataScience.org

PhD in Data Science – Your Guide to Choosing a Doctorate Degree Program

Created by aasif.faizal

Professional opportunities in data science are growing incredibly fast. That’s great news for students looking to pursue a career as a data scientist. But it also means that there are a lot more options out there to investigate and understand before developing the best educational path for you.

A PhD is the most advanced data science degree you can get, reflecting a depth of knowledge and technical expertise that will put you at the top of your field.

This means that PhD programs are the most time-intensive degree option out there, typically requiring that students complete dissertations involving rigorous research. This means that PhDs are not for everyone. Indeed, many who work in the world of big data hold master’s degrees rather than PhDs, which tend to involve the same coursework as PhD programs without a dissertation component. However, for the right candidate, a PhD program is the perfect choice to become a true expert on your area of focus.

If you’ve concluded that a data science PhD is the right path for you, this guide is intended to help you choose the best program to suit your needs. It will walk through some of the key considerations while picking graduate data science programs and some of the nuts and bolts (like course load and tuition costs) that are part of the data science PhD decision-making process.

Data Science PhD vs. Masters: Choosing the right option for you

If you’re considering pursuing a data science PhD, it’s worth knowing that such an advanced degree isn’t strictly necessary in order to get good work opportunities. Many who work in the field of big data only hold master’s degrees, which is the level of education expected to be a competitive candidate for data science positions.

So why pursue a data science PhD?

Simply put, a PhD in data science will leave you qualified to enter the big data industry at a high level from the outset.

You’ll be eligible for advanced positions within companies, holding greater responsibilities, keeping more direct communication with leadership, and having more influence on important data-driven decisions. You’re also likely to receive greater compensation to match your rank.

However, PhDs are not for everyone. Dissertations require a great deal of time and an interest in intensive research. If you are eager to jumpstart a career quickly, a master’s program will give you the preparation you need to hit the ground running. PhDs are appropriate for those who want to commit their time and effort to schooling as a long-term investment in their professional trajectory.

For more information on the difference between data science PhD’s and master’s programs, take a look at our guide here.

Topics include:

• Can I get an Online Ph.D in Data Science?
• Overview of Ph.d Coursework

Preparing for a Doctorate Program

Building a solid track record of professional experience, things to consider when choosing a school.

• What Does it Cost to Get a Ph.D in Data Science?
• School Listings

Data Science PhD Programs, Historically

Historically, data science PhD programs were one of the main avenues to get a good data-related position in academia or industry. But, PhD programs are heavily research oriented and require a somewhat long term investment of time, money, and energy to obtain. The issue that some data science PhD holders are reporting, especially in industry settings, is that that the state of the art is moving so quickly, and that the data science industry is evolving so rapidly, that an abundance of research oriented expertise is not always what’s heavily sought after.

Instead, many companies are looking for candidates who are up to date with the latest data science techniques and technologies, and are willing to pivot to match emerging trends and practices.

One recent development that is making the data science graduate school decisions more complex is the introduction of specialty master’s degrees, that focus on rigorous but compact, professional training. Both students and companies are realizing the value of an intensive, more industry-focused degree that can provide sufficient enough training to manage complex projects and that are more client oriented, opposed to research oriented.

However, not all prospective data science PhD students are looking for jobs in industry. There are some pretty amazing research opportunities opening up across a variety of academic fields that are making use of new data collection and analysis tools. Experts that understand how to leverage data systems including statistics and computer science to analyze trends and build models will be in high demand.

Can You Get a PhD in Data Science Online?

While it is not common to get a data science Ph.D. online, there are currently two options for those looking to take advantage of the flexibility of an online program.

Indiana University Bloomington and Northcentral University both offer online Ph.D. programs with either a minor or specialization in data science.

Given the trend for schools to continue increasing online offerings, expect to see additional schools adding this option in the near future.

Overview of PhD Coursework

A PhD requires a lot of academic work, which generally requires between four and five years (sometimes longer) to complete.

Here are some of the high level factors to consider and evaluate when comparing data science graduate programs.

How many credits are required for a PhD in data science?

On average, it takes 71 credits to graduate with a PhD in data science — far longer (almost double) than traditional master’s degree programs. In addition to coursework, most PhD students also have research and teaching responsibilities that can be simultaneously demanding and really great career preparation.

What’s the core curriculum like?

In a data science doctoral program, you’ll be expected to learn many skills and also how to apply them across domains and disciplines. Core curriculums will vary from program to program, but almost all will have a core foundation of statistics.

All PhD candidates will have to take a qualifying exam. This can vary from university to university, but to give you some insight, it is broken up into three phases at Yale. They have a practical exam, a theory exam and an oral exam. The goal is to make sure doctoral students are developing the appropriate level of expertise.

Dissertation

One of the final steps of a PhD program involves presenting original research findings in a formal document called a dissertation. These will provide background and context, as well as findings and analysis, and can contribute to the understanding and evolution of data science. A dissertation idea most often provides the framework for how a PhD candidate’s graduate school experience will unfold, so it’s important to be thoughtful and deliberate while considering research opportunities.

Since data science is such a rapidly evolving field and because choosing the right PhD program is such an important factor in developing a successful career path, there are some steps that prospective doctoral students can take in advance to find the best-fitting opportunity.

Join professional associations

Even before being fully credentials, joining professional associations and organizations such as the Data Science Association and the American Association of Big Data Professionals is a good way to get exposure to the field. Many professional societies are welcoming to new members and even encourage student participation with things like discounted membership fees and awards and contest categories for student researchers. One of the biggest advantages to joining is that these professional associations bring together other data scientists for conference events, research-sharing opportunities, networking and continuing education opportunities.

Leverage your social network

Be on the lookout to make professional connections with professors, peers, and members of industry. There are a number of LinkedIn groups dedicated to data science. A well-maintained professional network is always useful to have when looking for advice or letters of recommendation while applying to graduate school and then later while applying for jobs and other career-related opportunities.

Kaggle competitions

Kaggle competitions provide the opportunity to solve real-world data science problems and win prizes. A list of data science problems can be found at Kaggle.com . Winning one of these competitions is a good way to demonstrate professional interest and experience.

Internships

Internships are a great way to get real-world experience in data science while also getting to work for top names in the world of business. For example, IBM offers a data science internship which would also help to stand out when applying for PhD programs, as well as in seeking employment in the future.

Demonstrating professional experience is not only important when looking for jobs, but it can also help while applying for graduate school. There are a number of ways for prospective students to gain exposure to the field and explore different facets of data science careers.

Get certified

There are a number of data-related certificate programs that are open to people with a variety of academic and professional experience. DeZyre has an excellent guide to different certifications, some of which might help provide good background for graduate school applications.

Conferences

Conferences are a great place to meet people presenting new and exciting research in the data science field and bounce ideas off of newfound connections. Like professional societies and organizations, discounted student rates are available to encourage student participation. In addition, some conferences will waive fees if you are presenting a poster or research at the conference, which is an extra incentive to present.

It can be hard to quantify what makes a good-fit when it comes to data science graduate school programs. There are easy to evaluate factors, such as cost and location, and then there are harder to evaluate criteria such as networking opportunities, accessibility to professors, and the up-to-dateness of the program’s curriculum.

Nevertheless, there are some key relevant considerations when applying to almost any data science graduate program.

What most schools will require when applying:

• All undergraduate and graduate transcripts
• A statement of intent for the program (reason for applying and future plans)
• Letters of reference
• Application fee
• Online application
• A curriculum vitae (outlining all of your academic and professional accomplishments)

What Does it Cost to Get a PhD in Data Science?

The great news is that many PhD data science programs are supported by fellowships and stipends. Some are completely funded, meaning the school will pay tuition and basic living expenses. Here are several examples of fully funded programs:

• University of Southern California
• University of Nevada, Reno
• Kennesaw State University
• Worcester Polytechnic Institute
• University of Maryland

For all other programs, the average range of tuition, depending on the school can range anywhere from $1,300 per credit hour to $2,000 amount per credit hour. Remember, typical PhD programs in data science are between 60 and 75 credit hours, meaning you could spend up to $150,000 over several years.

That’s why the financial aspects are so important to evaluate when assessing PhD programs, because some schools offer full stipends so that you are able to attend without having to find supplemental scholarships or tuition assistance.

Can I become a professor of data science with a PhD.? Yes! If you are interested in teaching at the college or graduate level, a PhD is the degree needed to establish the full expertise expected to be a professor. Some data scientists who hold PhDs start by entering the field of big data and pivot over to teaching after gaining a significant amount of work experience. If you’re driven to teach others or to pursue advanced research in data science, a PhD is the right degree for you.

Do I need a master’s in order to pursue a PhD.? No. Many who pursue PhDs in Data Science do not already hold advanced degrees, and many PhD programs include all the coursework of a master’s program in the first two years of school. For many students, this is the most time-effective option, allowing you to complete your education in a single pass rather than interrupting your studies after your master’s program.

Can I choose to pursue a PhD after already receiving my master’s? Yes. A master’s program can be an opportunity to get the lay of the land and determine the specific career path you’d like to forge in the world of big data. Some schools may allow you to simply extend your academic timeline after receiving your master’s degree, and it is also possible to return to school to receive a PhD if you have been working in the field for some time.

If a PhD. isn’t necessary, is it a waste of time? While not all students are candidates for PhDs, for the right students – who are keen on doing in-depth research, have the time to devote to many years of school, and potentially have an interest in continuing to work in academia – a PhD is a great choice. For more information on this question, take a look at our article Is a Data Science PhD. Worth It?

Complete List of Data Science PhD Programs

Below you will find the most comprehensive list of schools offering a doctorate in data science. Each school listing contains a link to the program specific page, GRE or a master’s degree requirements, and a link to a page with detailed course information.

Note that the listing only contains true data science programs. Other similar programs are often lumped together on other sites, but we have chosen to list programs such as data analytics and business intelligence on a separate section of the website.

Boise State University  – Boise, Idaho PhD in Computing – Data Science Concentration

The Data Science emphasis focuses on the development of mathematical and statistical algorithms, software, and computing systems to extract knowledge or insights from data.  

In 60 credits, students complete an Introduction to Graduate Studies, 12 credits of core courses, 6 credits of data science elective courses, 10 credits of other elective courses, a Doctoral Comprehensive Examination worth 1 credit, and a 30-credit dissertation.

Electives can be taken in focus areas such as Anthropology, Biometry, Ecology/Evolution and Behavior, Econometrics, Electrical Engineering, Earth Dynamics and Informatics, Geoscience, Geostatistics, Hydrology and Hydrogeology, Materials Science, and Transportation Science.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $7,236 total (Resident), $24,573 total (Non-resident)

View Course Offerings

Bowling Green State University  – Bowling Green, Ohio Ph.D. in Data Science

Data Science students at Bowling Green intertwine knowledge of computer science with statistics.

Students learn techniques in analyzing structured, unstructured, and dynamic datasets.

Courses train students to understand the principles of analytic methods and articulating the strengths and limitations of analytical methods.

The program requires 60 credit hours in the studies of Computer Science (6 credit hours), Statistics (6 credit hours), Data Science Exploration and Communication, Ethical Issues, Advanced Data Mining, and Applied Data Science Experience.

Students must also complete 21 credit hours of elective courses, a qualifying exam, a preliminary exam, and a dissertation.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $8,418 (Resident), $14,410 (Non-resident)

Brown University  – Providence, Rhode Island PhD in Computer Science – Concentration in Data Science

Brown University’s database group is a world leader in systems-oriented database research; they seek PhD candidates with strong system-building skills who are interested in researching TupleWare, MLbase, MDCC, Crowd DB, or PIQL.

In order to gain entrance, applicants should consider first doing a research internship at Brown with this group. Other ways to boost an application are to take and do well at massive open online courses, do an internship at a large company, and get involved in a large open-source software project.

Coding well in C++ is preferred.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $62,680 total

Chapman University  – Irvine, California Doctorate in Computational and Data Sciences

Candidates for the doctorate in computational and data science at Chapman University begin by completing 13 core credits in basic methodologies and techniques of computational science.

Students complete 45 credits of electives, which are personalized to match the specific interests and research topics of the student.

Finally, students complete up to 12 credits in dissertation research.

Applicants must have completed courses in differential equations, data structures, and probability and statistics, or take specific foundation courses, before beginning coursework toward the PhD.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $37,538 per year

Clemson University / Medical University of South Carolina (MUSC) – Joint Program – Clemson, South Carolina & Charleston, South Carolina Doctor of Philosophy in Biomedical Data Science and Informatics – Clemson

The PhD in biomedical data science and informatics is a joint program co-authored by Clemson University and the Medical University of South Carolina (MUSC).

Students choose one of three tracks to pursue: precision medicine, population health, and clinical and translational informatics. Students complete 65-68 credit hours, and take courses in each of 5 areas: biomedical informatics foundations and applications; computing/math/statistics/engineering; population health, health systems, and policy; biomedical/medical domain; and lab rotations, seminars, and doctoral research.

Applicants must have a bachelor’s in health science, computing, mathematics, statistics, engineering, or a related field, and it is recommended to also have competency in a second of these areas.

Program requirements include a year of calculus and college biology, as well as experience in computer programming.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $10,858 total (South Carolina Resident), $22,566 total (Non-resident)

View Course Offerings – Clemson

George Mason University  – Fairfax, Virginia Doctor of Philosophy in Computational Sciences and Informatics – Emphasis in Data Science

George Mason’s PhD in computational sciences and informatics requires a minimum of 72 credit hours, though this can be reduced if a student has already completed a master’s. 48 credits are toward graduate coursework, and an additional 24 are for dissertation research.

Students choose an area of emphasis—either computer modeling and simulation or data science—and completed 18 credits of the coursework in this area. Students are expected to completed the coursework in 4-5 years.

Applicants to this program must have a bachelor’s degree in a natural science, mathematics, engineering, or computer science, and must have knowledge and experience with differential equations and computer programming.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $13,426 total (Virginia Resident), $35,377 total (Non-resident)

Harrisburg University of Science and Technology  – Harrisburg, Pennsylvania Doctor of Philosophy in Data Sciences

Harrisburg University’s PhD in data science is a 4-5 year program, the first 2 of which make up the Harrisburg master’s in analytics.

Beyond this, PhD candidates complete six milestones to obtain the degree, including 18 semester hours in doctoral-level courses, such as multivariate data analysis, graph theory, machine learning.

Following the completion of ANLY 760 Doctoral Research Seminar, students in the program complete their 12 hours of dissertation research bringing the total program hours to 36.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $14,940 total

Icahn School of Medicine at Mount Sinai  – New York, New York Genetics and Data Science, PhD

As part of the Biomedical Science PhD program, the Genetics and Data Science multidisciplinary training offers research opportunities that expand on genetic research and modern genomics. The training also integrates several disciplines of biomedical sciences with machine learning, network modeling, and big data analysis.

Students in the Genetics and Data Science program complete a predetermined course schedule with a total of 64 credits and 3 years of study.

Additional course requirements and electives include laboratory rotations, a thesis proposal exam and thesis defense, Computer Systems, Intro to Algorithms, Machine Learning for Biomedical Data Science, Translational Genomics, and Practical Analysis of a Personal Genome.

Delivery Method: Campus GRE: Not Required 2022-2023 Tuition: $31,303 total

Indiana University-Purdue University Indianapolis  – Indianapolis, Indiana PhD in Data Science PhD Minor in Applied Data Science

Doctoral candidates pursuing the PhD in data science at Indiana University-Purdue must display competency in research, data analytics, and at management and infrastructure to earn the degree.

The PhD is comprised of 24 credits of a data science core, 18 credits of methods courses, 18 credits of a specialization, written and oral qualifying exams, and 30 credits of dissertation research. All requirements must be completed within 7 years.

Applicants are generally expected to have a master’s in social science, health, data science, or computer science. 

Currently a majority of the PhD students at IUPUI are funded by faculty grants and two are funded by the federal government. None of the students are self funded.

IUPUI also offers a PhD Minor in Applied Data Science that is 12-18 credits. The minor is open to students enrolled at IUPUI or IU Bloomington in a doctoral program other than Data Science.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $9,228 per year (Indiana Resident), $25,368 per year (Non-resident)

Jackson State University – Jackson, Mississippi PhD Computational and Data-Enabled Science and Engineering

Jackson State University offers a PhD in computational and data-enabled science and engineering with 5 concentration areas: computational biology and bioinformatics, computational science and engineering, computational physical science, computation public health, and computational mathematics and social science.

Students complete 12 credits of common core courses, 12 credits in the specialization, 24 credits of electives, and 24 credits in dissertation research.

Students may complete the doctoral program in as little as 5 years and no more than 8 years.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $8,270 total

Kennesaw State University  – Kennesaw, Georgia PhD in Analytics and Data Science

Students pursuing a PhD in analytics and data science at Kennesaw State University must complete 78 credit hours: 48 course hours and 6 electives (spread over 4 years of study), a minimum 12 credit hours for dissertation research, and a minimum 12 credit-hour internship.

Prior to dissertation research, the comprehensive examination will cover material from the three areas of study: computer science, mathematics, and statistics.

Successful applicants will have a master’s degree in a computational field, calculus I and II, programming experience, modeling experience, and are encouraged to have a base SAS certification.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $5,328 total (Georgia Resident), $19,188 total (Non-resident)

New Jersey Institute of Technology  – Newark, New Jersey PhD in Business Data Science

Students may enter the PhD program in business data science at the New Jersey Institute of Technology with either a relevant bachelor’s or master’s degree. Students with bachelor’s degrees begin with 36 credits of advanced courses, and those with master’s take 18 credits before moving on to credits in dissertation research.

Core courses include business research methods, data mining and analysis, data management system design, statistical computing with SAS and R, and regression analysis.

Students take qualifying examinations at the end of years 1 and 2, and must defend their dissertations successfully by the end of year 6.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $21,932 total (New Jersey Resident), $32,426 total (Non-resident)

New York University  – New York, New York PhD in Data Science

Doctoral candidates in data science at New York University must complete 72 credit hours, pass a comprehensive and qualifying exam, and defend a dissertation with 10 years of entering the program.

Required courses include an introduction to data science, probability and statistics for data science, machine learning and computational statistics, big data, and inference and representation.

Applicants must have an undergraduate or master’s degree in fields such as mathematics, statistics, computer science, engineering, or other scientific disciplines. Experience with calculus, probability, statistics, and computer programming is also required.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $37,332 per year

View Course Offering

Northcentral University  – San Diego, California PhD in Data Science-TIM

Northcentral University offers a PhD in technology and innovation management with a specialization in data science.

The program requires 60 credit hours, including 6-7 core courses, 3 in research, a PhD portfolio, and 4 dissertation courses.

The data science specialization requires 6 courses: data mining, knowledge management, quantitative methods for data analytics and business intelligence, data visualization, predicting the future, and big data integration.

Applicants must have a master’s already.

Delivery Method: Online GRE: Required 2022-2023 Tuition: $16,794 total

Stevens Institute of Technology – Hoboken, New Jersey Ph.D. in Data Science

Stevens Institute of Technology has developed a data science Ph.D. program geared to help graduates become innovators in the space.

The rigorous curriculum emphasizes mathematical and statistical modeling, machine learning, computational systems and data management.

The program is directed by Dr. Ted Stohr, a recognized thought leader in the information systems, operations and business process management arenas.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $39,408 per year

University at Buffalo – Buffalo, New York PhD Computational and Data-Enabled Science and Engineering

The curriculum for the University of Buffalo’s PhD in computational and data-enabled science and engineering centers around three areas: data science, applied mathematics and numerical methods, and high performance and data intensive computing. 9 credit course of courses must be completed in each of these three areas. Altogether, the program consists of 72 credit hours, and should be completed in 4-5 years. A master’s degree is required for admission; courses taken during the master’s may be able to count toward some of the core coursework requirements.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $11,310 per year (New York Resident), $23,100 per year (Non-resident)

University of Colorado Denver – Denver, Colorado PhD in Big Data Science and Engineering

The University of Colorado – Denver offers a unique program for those students who have already received admission to the computer science and information systems PhD program.

The Big Data Science and Engineering (BDSE) program is a PhD fellowship program that allows selected students to pursue research in the area of big data science and engineering. This new fellowship program was created to train more computer scientists in data science application fields such as health informatics, geosciences, precision and personalized medicine, business analytics, and smart cities and cybersecurity.

Students in the doctoral program must complete 30 credit hours of computer science classes beyond a master’s level, and 30 credit hours of dissertation research.

The BDSE fellowship requires students to have an advisor both in the core disciplines (either computer science or mathematics and statistics) as well as an advisor in the application discipline (medicine and public health, business, or geosciences).

In addition, the fellowship covers full stipend, tuition, and fees up to ~50k for BDSE fellows annually. Important eligibility requirements can be found here.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $55,260 total

University of Marylan d  – College Park, Maryland PhD in Information Studies

Data science is a potential research area for doctoral candidates in information studies at the University of Maryland – College Park. This includes big data, data analytics, and data mining.

Applicants for the PhD must have taken the following courses in undergraduate studies: programming languages, data structures, design and analysis of computer algorithms, calculus I and II, and linear algebra.

Students must complete 6 qualifying courses, 2 elective graduate courses, and at least 12 credit hours of dissertation research.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $16,238 total (Maryland Resident), $35,388 total (Non-resident)

University of Massachusetts Boston  – Boston, Massachusetts PhD in Business Administration – Information Systems for Data Science Track

The University of Massachusetts – Boston offers a PhD in information systems for data science. As this is a business degree, students must complete coursework in their first two years with a focus on data for business; for example, taking courses such as business in context: markets, technologies, and societies.

Students must take and pass qualifying exams at the end of year 1, comprehensive exams at the end of year 2, and defend their theses at the end of year 4.

Those with a degree in statistics, economics, math, computer science, management sciences, information systems, and other related fields are especially encouraged, though a quantitative degree is not necessary.

Students accepted by the program are ordinarily offered full tuition credits and a stipend ($25,000 per year) to cover educational expenses and help defray living costs for up to three years of study.

During the first two years of coursework, they are assigned to a faculty member as a research assistant; for the third year students will be engaged in instructional activities. Funding for the fourth year is merit-based from a limited pool of program funds

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $18,894 total (in-state), $36,879 (out-of-state)

University of Nevada Reno – Reno, Nevada PhD in Statistics and Data Science

The University of Nevada – Reno’s doctoral program in statistics and data science is comprised of 72 credit hours to be completed over the course of 4-5 years. Coursework is all within the scope of statistics, with titles such as statistical theory, probability theory, linear models, multivariate analysis, statistical learning, statistical computing, time series analysis.

The completion of a Master’s degree in mathematics or statistics prior to enrollment in the doctoral program is strongly recommended, but not required.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $5,814 total (in-state), $22,356 (out-of-state)

University of Southern California – Los Angles, California PhD in Data Sciences & Operations

USC Marshall School of Business offers a PhD in data sciences and operations to be completed in 5 years.

Students can choose either a track in operations management or in statistics. Both tracks require 4 courses in fall and spring of the first 2 years, as well as a research paper and courses during the summers. Year 3 is devoted to dissertation preparation and year 4 and/or 5 to dissertation defense.

A bachelor’s degree is necessary for application, but no field or further experience is required.

Students should complete 60 units of coursework. If the students are admitted with Advanced Standing (e.g., Master’s Degree in appropriate field), this requirement may be reduced to 40 credits.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $63,468 total

University of Tennessee-Knoxville  – Knoxville, Tennessee The Data Science and Engineering PhD

The data science and engineering PhD at the University of Tennessee – Knoxville requires 36 hours of coursework and 36 hours of dissertation research. For those entering with an MS degree, only 24 hours of course work is required.

The core curriculum includes work in statistics, machine learning, and scripting languages and is enhanced by 6 hours in courses that focus either on policy issues related to data, or technology entrepreneurship.

Students must also choose a knowledge specialization in one of these fields: health and biological sciences, advanced manufacturing, materials science, environmental and climate science, transportation science, national security, urban systems science, and advanced data science.

Applicants must have a bachelor’s or master’s degree in engineering or a scientific field. 

All students that are admitted will be supported by a research fellowship and tuition will be included.

Many students will perform research with scientists from Oak Ridge national lab, which is located about 30 minutes drive from campus.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $11,468 total (Tennessee Resident), $29,656 total (Non-resident)

University of Vermont – Burlington, Vermont Complex Systems and Data Science (CSDS), PhD

Through the College of Engineering and Mathematical Sciences, the Complex Systems and Data Science (CSDS) PhD program is pan-disciplinary and provides computational and theoretical training. Students may customize the program depending on their chosen area of focus.

Students in this program work in research groups across campus.

Core courses include Data Science, Principles of Complex Systems and Modeling Complex Systems. Elective courses include Machine Learning, Complex Networks, Evolutionary Computation, Human/Computer Interaction, and Data Mining.

The program requires at least 75 credits to graduate with approval by the student graduate studies committee.

Delivery Method: Campus GRE: Not Required 2022-2023 Tuition: $12,204 total (Vermont Resident), $30,960 total (Non-resident)

University of Washington Seattle Campus – Seattle, Washington PhD in Big Data and Data Science

The University of Washington’s PhD program in data science has 2 key goals: training of new data scientists and cyberinfrastructure development, i.e., development of open-source tools and services that scientists around the world can use for big data analysis.

Students must take core courses in data management, machine learning, data visualization, and statistics.

Students are also required to complete at least one internship that covers practical work in big data.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $17,004 per year (Washington resident), $30,477 (non-resident)

University of Wisconsin-Madison – Madison, Wisconsin PhD in Biomedical Data Science

The PhD program in Biomedical Data Science offered by the Department of Biostatistics and Medical Informatics at UW-Madison is unique, in blending the best of statistics and computer science, biostatistics and biomedical informatics. 

Students complete three year-long course sequences in biostatistics theory and methods, computer science/informatics, and a specialized sequence to fit their interests.

Students also complete three research rotations within their first two years in the program, to both expand their breadth of knowledge and assist in identifying a research advisor.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $10,728 total (in-state), $24,054 total (out-of-state)

Vanderbilt University – Nashville, Tennessee Data Science Track of the BMI PhD Program

The PhD in biomedical informatics at Vanderbilt has the option of a data science track.

Students complete courses in the areas of biomedical informatics (3 courses), computer science (4 courses), statistical methods (4 courses), and biomedical science (2 courses). Students are expected to complete core courses and defend their dissertations within 5 years of beginning the program.

Applicants must have a bachelor’s degree in computer science, engineering, biology, biochemistry, nursing, mathematics, statistics, physics, information management, or some other health-related field.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $53,160 per year

Washington University in St. Louis – St. Louis, Missouri Doctorate in Computational & Data Sciences

Washington University now offers an interdisciplinary Ph.D. in Computational & Data Sciences where students can choose from one of four tracks (Computational Methodologies, Political Science, Psychological & Brain Sciences, or Social Work & Public Health).

Students are fully funded and will receive a stipend for at least five years contingent on making sufficient progress in the program.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $59,420 total

Worcester Polytechnic Institute – Worcester, Massachusetts PhD in Data Science

The PhD in data science at Worcester Polytechnic Institute focuses on 5 areas: integrative data science, business intelligence and case studies, data access and management, data analytics and mining, and mathematical analysis.

Students first complete a master’s in data science, and then complete 60 credit hours beyond the master’s, including 30 credit hours of research.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $28,980 per year

Yale University – New Haven, Connecticut PhD Program – Department of Stats and Data Science

The PhD in statistics and data science at Yale University offers broad training in the areas of statistical theory, probability theory, stochastic processes, asymptotics, information theory, machine learning, data analysis, statistical computing, and graphical methods. Students complete 12 courses in the first year in these topics.

Students are required to teach one course each semester of their third and fourth years.

Most students complete and defend their dissertations in their fifth year.

Applicants should have an educational background in statistics, with an undergraduate major in statistics, mathematics, computer science, or similar field.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $46,900 total

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Sure you can. Many of our students successfully switch careers to programming after completing our bootcamps, even if they were absolutely new to programming when starting out. If your aim is to change careers, full or part-time bootcamps are designed to help you achieve that. To enrol with us, you don’t need to have a degree or prior technical background - all you need is discipline, persistence, and a willingness to work hard and learn to code.

Dr Mark Thomas Kennedy Director of Imperial Business Analytics and Co-director of the DSI

Dr. Kennedy studies how innovations become real. He's Co-Director of Data Science Institute at Imperial and holds degrees from Northwestern and Stanford. His work has been published in top journals including American Sociological Review and Academy of Management Journal.

Dr. Arcucci is a lecturer in Data Science and Machine Learning at Imperial College London. She is an elected member of the WMO and speaker of the AI Network of Excellence at Imperial. Her work involves developing AI models for climate and environmental impact and she also collaborates with the Leonardo Centre at Imperial.

Dr Rossella Arcucci Lecturer in Data Science and Machine Learning at Imperial College London

Dr. Șerban is a Research Fellow at Imperial's Data Science Institute. He specializes in Large Scale Data Visualisation, Natural Language Processing, Machine Learning, Affective Computing, and Interactive System Design. Dr. Șerban holds a joint PhD from INSA de Rouen Normandy (France) and "Babeș-Bolyai" University (Romania).

Dr Ovidiu Șerban Research Fellow at the DSI, Imperial College London

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MSc Machine Learning and Data Science (Online)

Imperial college london, different course options.

• Key information

Course Summary

Tuition fees, entry requirements, similar courses at different universities, key information data source : idp connect, qualification type.

MSc - Master of Science

Subject areas

Artificial Intelligence (Ai) Data Science

Course type

Course overview

Accelerate your career in engineering or data science on this online and part-time Master's course.

Via hands-on projects, you'll build a portfolio in everything from probabilistic modelling and deep learning to unstructured data processing and anomaly detection.

This programme will enhance your analytical abilities in relation to mathematics and statistics. You'll gain expertise in tackling complex data by implementing scalable solutions using industry-standard tools, including PySpark.

You'll also consider the ethics and limitations of machine learning, and learn how to ethically apply these techniques to your work.

All learning is delivered online.

UK fees Course fees for UK students

For this course (per year)

International fees Course fees for EU and international students

Students need to have a 2:1 in statistics, mathematics, engineering, physics or computer science.

MSc Human Computer Interaction

University for the creative arts, robotics msc, middlesex university, bristol, university of the west of england, artificial intelligence and intelligent agents phd, bangor university, artificial intelligence and data science msc.

Statistics and Machine Learning (EPSRC CDT)

• Entry requirements
• Funding and costs

College preference

• How to Apply

About the course

The Statistics and Machine Learning (StatML) Centre for Doctoral Training (CDT) is a four-year DPhil research programme (or eight years if studying part-time). It will train the next generation of researchers in statistics and machine learning, who will develop widely-applicable novel methodology and theory and create application-specific methods, leading to breakthroughs in real-world problems in government, medicine, industry and science. 

This is the Oxford component of StatML, a CDT in Statistics and Machine Learning, co-hosted by Imperial College London and the University of Oxford. The programme will provide you with training in both cutting-edge research methodologies and the development of business and transferable skills – essential elements required by employers in industry and business.

You will undertake a significant, challenging and original research project, leading to the award of a DPhil. Given the breadth and depth of the research teams at Imperial College and at the University of Oxford, the proposed projects will range from theoretical to computational and applied aspects of statistics and machine learning, with a large number of projects involving strong methodological/theoretical developments together with a challenging real problem. A significant number of projects will be co-supervised with industry.

You will pursue two mini-projects during your first year (specific timings may vary for part-time students), with the expectation that one of them will lead to your main research project. At the admissions stage you will choose a mini-project. These mini-projects are proposed by the department's supervisory pool and industrial partners. You will be based at the home institution of your main supervisor of the first mini-project.

If your studentship is funded or co-funded by an external partner, the second mini-project will be with the same external partner but will explore a different question.

Alongside your research projects you will engage with taught courses each lasting for two weeks. Core topics will be taught during at the beginning of your first year (specific timings may vary for part-time students) and are:

• Modern Statistical Theory
• Statistical Machine Learning;
• Causality; and
• Bayesian methods and computation.

You will then begin your main DPhil project at the beginning of the third term (at the beginning of the fourth term for part-time students), which can be based on one of the two mini-projects. Where appropriate for the research, your project will be run jointly with the CDT's leading industrial partners, and you will have the chance to undertake a placement in data-intensive statistics with some of the strongest statistics groups in the USA, Europe and Asia.

If you are studying full-time, starting in the second year, you will teach approximately twelve contact hours per year in undergraduate and graduate courses in your host department. If you are studying part-time, teaching will begin in the third year and you will teach approximately six hours per year. This is mentored teaching, beginning with simple marking, to reach a point where individual students are leading whole classes of ten or twelve undergraduate students. Students will have the support of a mentor and get written feedback at the end of each block of teaching.

You will also be required to take a number of optional courses throughout the four years of the course, which could be made up of choices from the following list: Bayesian nonparametrics; high-dimensional statistics; advanced optimisation; networks; reinforcement learning; large language models; conformal inference, variational Bayes and advance Bayesian computations, dynamical and graphical modelling of multivariate time series, modelling events; and deep learning. Optional modules last two weeks and are delivered in a similar format to the core modules.

Many events bring StatML students and staff together across different peer groups and research groups, ranging from full cohort days and group research skills sessions to summer schools. These events support research and involve staff and students from both Oxford and Imperial coming together at both locations.

The Department of Statistics runs a seminar series in statistics and probability, and a graduate lecture series involving snapshots of the research interests of the department. Several journal-clubs run each term, reading and discussing new research papers as they emerge. These events bring research students together with academic and other research staff in the department to hear about on-going research, and provide an opportunity for networking and socialising.

Further information about part-time study

As a part-time student you will be required to attend modules and other cohort activities in Oxford (or sometimes London) for a minimum of 30 days each year. There will be no flexibility in the dates of modules or cohort events, though it is possible to spread your attendance at modules over the course of the four year programme (with agreement of your supervisor and the programme Directors). Attendance will be required during term-time (on a pro-rata basis) for cohort activities. These often take place on Mondays and Thursdays. Attendance will occasionally be required outside of term-time for cohort activities. 

You will have the opportunity to tailor your part-time study and skills training in liaison with your supervisor and programme Directors, and agree your pattern of attendance.

Supervision

The allocation of graduate supervision for this course is the responsibility of the Department of Statistics (Oxford) and/or the Department of Mathematics (Imperial). It is not always possible to accommodate the preferences of incoming graduate students to work with a particular member of staff. A supervisor may be found outside these departments.

You are matched to your supervisor for the first mini-project at the start of the course. Within the first year of the course, the student will have the opportunity to work with an alternative supervisor for a second mini-project. It is normal for one of these mini-projects to lead to the full DPhil project with the same supervisory team as was in place for the mini-project chosen. 

Typically, as a research student, you should expect to have meetings with your supervisor or a member of the supervisory team with a frequency of at least once every two weeks averaged across the year. The regularity of these meetings may be subject to variations according to the time of the year, and the stage that you are at in your research programme.

Each mini-project will be assessed on the basis of a report written by the student, by researchers from Imperial and Oxford.

Modules are assessed by a presentation in small groups on some material studied during the two-week module (known as micro-projects within the programme).

All students will be initially admitted to the status of Probationer Research Student (PRS). Within a maximum of six terms as a full-time PRS student or twelve terms as a part-time PRS student, you will be expected to apply for transfer of status from Probationer Research Student to DPhil status. This application is normally made by the fourth term for full-time students and by the eighth term for part-time students.

A successful transfer of status from PRS to DPhil status will require the submission of a thesis outline. Students who are successful at transfer will also be expected to apply for and gain confirmation of DPhil status to show that your work continues to be on track. This will need to done within nine terms of admission for full-time students and eighteen terms of admission for part-time students.

Both milestones normally involve an interview with two assessors (other than your supervisor) and therefore provide important experience for the final oral examination.

Full-time students will be expected to submit a thesis at four years from the date of admission. If you are studying part-time, you be required to submit your thesis after six or, at most, eight years from the date of admission. To be successfully awarded a DPhil in Statistics you will need to defend your thesis orally (viva voce) in front of two appointed examiners.

The final thesis is normally submitted for examination during the fourth year (or eighth year if studying part-time) and is followed by the viva examination. The final award for Oxford based students will be a DPhil awarded by the University of Oxford.

Graduate destinations

This is a new course and there are no alumni yet. StatML is dedicated to providing the organisation, environment and personnel needed to develop the future industrial and academic individuals doing world-leading research in statistics for modern day science, engineering and commerce, all exemplified by ‘big data’.

Changes to this course and your supervision

The University will seek to deliver this course in accordance with the description set out in this course page. However, there may be situations in which it is desirable or necessary for the University to make changes in course provision, either before or after registration. The safety of students, staff and visitors is paramount and major changes to delivery or services may have to be made in circumstances of a pandemic, epidemic or local health emergency. In addition, in certain circumstances, for example due to visa difficulties or because the health needs of students cannot be met, it may be necessary to make adjustments to course requirements for international study.

Where possible your academic supervisor will not change for the duration of your course. However, it may be necessary to assign a new academic supervisor during the course of study or before registration for reasons which might include illness, sabbatical leave, parental leave or change in employment.

For further information please see our page on changes to courses and the provisions of the student contract regarding changes to courses.

Entry requirements for entry in 2024-25

Proven and potential academic excellence.

The requirements described below are specific to this course and apply only in the year of entry that is shown. You can use our interactive tool to help you  evaluate whether your application is likely to be competitive .

Please be aware that any studentships that are linked to this course may have different or additional requirements and you should read any studentship information carefully before applying. 

Degree-level qualifications

As a minimum, applicants should hold or be predicted to achieve the following UK qualifications or their equivalent:

• a first-class or strong upper second-class undergraduate degree with honours in mathematics, statistics, physics, computer science, engineering or a closely related subject. 

However, entrance is very competitive and most successful applicants have a first-class degree or the equivalent.

For applicants with a degree from the USA, the minimum GPA sought is 3.6 out of 4.0.

If your degree is not from the UK or another country specified above, visit our International Qualifications page for guidance on the qualifications and grades that would usually be considered to meet the University’s minimum entry requirements.

GRE General Test scores

No Graduate Record Examination (GRE) or GMAT scores are sought.

Other qualifications, evidence of excellence and relevant experience 

Publications are not expected but details of any publications may be included with the application.

English language proficiency

This course requires proficiency in English at the University's  standard level . If your first language is not English, you may need to provide evidence that you meet this requirement. The minimum scores required to meet the University's standard level are detailed in the table below.

*Previously known as the Cambridge Certificate of Advanced English or Cambridge English: Advanced (CAE) † Previously known as the Cambridge Certificate of Proficiency in English or Cambridge English: Proficiency (CPE)

Your test must have been taken no more than two years before the start date of your course. Our Application Guide provides further information about the English language test requirement .

Declaring extenuating circumstances

If your ability to meet the entry requirements has been affected by the COVID-19 pandemic (eg you were awarded an unclassified/ungraded degree) or any other exceptional personal circumstance (eg other illness or bereavement), please refer to the guidance on extenuating circumstances in the Application Guide for information about how to declare this so that your application can be considered appropriately.

You will need to register three referees who can give an informed view of your academic ability and suitability for the course. The  How to apply  section of this page provides details of the types of reference that are required in support of your application for this course and how these will be assessed.

Supporting documents

You will be required to supply supporting documents with your application. The  How to apply  section of this page provides details of the supporting documents that are required as part of your application for this course and how these will be assessed.

Performance at interview

Interviews are held as part of the admissions process for applicants who, on the basis of their written application, best meet the selection criteria.

Interviews may be held in person or over video link such as Zoom, normally with at least two interviewers. Interviews will include some technical questions on statistical topics relating to the StatML CDT. These questions will be adapted as far as possible to the applicant's own background training in statistics or machine learning.

How your application is assessed

Your application will be assessed purely on your proven and potential academic excellence and other entry requirements described under that heading.

References  and  supporting documents  submitted as part of your application, and your performance at interview (if interviews are held) will be considered as part of the assessment process. Whether or not you have secured funding will not be taken into consideration when your application is assessed.

An overview of the shortlisting and selection process is provided below. Our ' After you apply ' pages provide  more information about how applications are assessed . 

Shortlisting and selection

Students are considered for shortlisting and selected for admission without regard to age, disability, gender reassignment, marital or civil partnership status, pregnancy and maternity, race (including colour, nationality and ethnic or national origins), religion or belief (including lack of belief), sex, sexual orientation, as well as other relevant circumstances including parental or caring responsibilities or social background. However, please note the following:

• socio-economic information may be taken into account in the selection of applicants and award of scholarships for courses that are part of  the University’s pilot selection procedure  and for  scholarships aimed at under-represented groups ;
• country of ordinary residence may be taken into account in the awarding of certain scholarships; and
• protected characteristics may be taken into account during shortlisting for interview or the award of scholarships where the University has approved a positive action case under the Equality Act 2010.

Processing your data for shortlisting and selection

Information about  processing special category data for the purposes of positive action  and  using your data to assess your eligibility for funding , can be found in our Postgraduate Applicant Privacy Policy.

Admissions panels and assessors

All recommendations to admit a student involve the judgement of at least two members of the academic staff with relevant experience and expertise, and must also be approved by the Director of Graduate Studies or Admissions Committee (or equivalent within the department).

Admissions panels or committees will always include at least one member of academic staff who has undertaken appropriate training.

Other factors governing whether places can be offered

The following factors will also govern whether candidates can be offered places:

• the ability of the University to provide the appropriate supervision for your studies, as outlined under the 'Supervision' heading in the  About  section of this page;
• the ability of the University to provide appropriate support for your studies (eg through the provision of facilities, resources, teaching and/or research opportunities); and
• minimum and maximum limits to the numbers of students who may be admitted to the University's taught and research programmes.

Offer conditions for successful applications

If you receive an offer of a place at Oxford, your offer will outline any conditions that you need to satisfy and any actions you need to take, together with any associated deadlines. These may include academic conditions, such as achieving a specific final grade in your current degree course. These conditions will usually depend on your individual academic circumstances and may vary between applicants. Our ' After you apply ' pages provide more information about offers and conditions . 

In addition to any academic conditions which are set, you will also be required to meet the following requirements:

Financial Declaration

If you are offered a place, you will be required to complete a  Financial Declaration  in order to meet your financial condition of admission.

Disclosure of criminal convictions

In accordance with the University’s obligations towards students and staff, we will ask you to declare any  relevant, unspent criminal convictions  before you can take up a place at Oxford.

In January 2016 the Department of Statistics moved to occupy a newly-refurbished building in St Giles, near the centre of Oxford. The building has spaces for study and collaborative learning, including the library and large interaction and social area on the ground floor, as well as an open research zone on the second floor.

You will be provided with a computer and desk space in a shared office. You will have access to the Department of Statistics computing facilities and support, the department’s library, the Radcliffe Science Library and other University libraries, centrally-provided electronic resources and other facilities appropriate to your research topic. The provision of other resources specific to your DPhil project should be agreed with your supervisor as a part of the planning stages of the agreed project.

Tea and coffee facilities are provided in the Department. There are also opportunities for sporting interaction such as football and cricket.

The University's Department of Statistics is a world leader in research in probability, bioinformatics, mathematical genetics and statistical methodology, including computational statistics, machine learning and data science. 

You will be actively involved in a vibrant academic community by means of seminars, lectures, journal clubs, and social events. Research students are offered training in modern probability, stochastic processes, statistical methodology, computational methods and transferable skills, in addition to specialised topics relevant to specific application areas.

Much of the research in the Department of Statistics is either explicitly interdisciplinary or draws motivation from application areas, ranging from genetics, immunoinformatics, bioinformatics and cheminformatics, to finance and the social sciences.

The department is located on St Giles, in a building providing excellent teaching facilities and creating a highly visible centre for statistics in Oxford. Oxford’s Mathematical Sciences submission came first in the UK on all criteria in the 2021 Research Excellence Framework (REF).

View all courses   View taught courses View research courses

We expect that the majority of applicants who are offered a place on this course will also be offered a fully-funded scholarship specific to this course, covering course fees for the duration of their course and a living stipend.

For further details about searching for funding as a graduate student visit our dedicated Funding pages, which contain information about how to apply for Oxford scholarships requiring an additional application, details of external funding, loan schemes and other funding sources.

Please ensure that you visit individual college websites for details of any college-specific funding opportunities using the links provided on our college pages or below:

Please note that not all the colleges listed above may accept students on this course. For details of those which do, please refer to the College preference section of this page.

Annual fees for entry in 2024-25

Full-time study.

Further details about fee status eligibility can be found on the fee status webpage.

Part-time study

Information about course fees.

Course fees are payable each year, for the duration of your fee liability (your fee liability is the length of time for which you are required to pay course fees). For courses lasting longer than one year, please be aware that fees will usually increase annually. For details, please see our guidance on changes to fees and charges .

Course fees cover your teaching as well as other academic services and facilities provided to support your studies. Unless specified in the additional information section below, course fees do not cover your accommodation, residential costs or other living costs. They also don’t cover any additional costs and charges that are outlined in the additional information below.

Continuation charges

Following the period of fee liability , you may also be required to pay a University continuation charge and a college continuation charge. The University and college continuation charges are shown on the Continuation charges page.

Where can I find further information about fees?

The Fees and Funding  section of this website provides further information about course fees , including information about fee status and eligibility  and your length of fee liability .

Additional information

There are no compulsory elements of this course that entail additional costs beyond fees (or, after fee liability ends, continuation charges) and living costs. However, please note that, depending on your choice of research topic and the research required to complete it, you may incur additional expenses, such as travel expenses, research expenses, and field trips. You will need to meet these additional costs, although you may be able to apply for small grants from your department and/or college to help you cover some of these expenses.

Please note that you are required to attend in Oxford for a minimum of 30 days each year, and you may incur additional travel and accommodation expenses for this. Also, depending on your choice of research topic and the research required to complete it, you may incur further additional expenses, such as travel expenses, research expenses, and field trips. You will need to meet these additional costs, although you may be able to apply for small grants from your department and/or college to help you cover some of these expenses.

Living costs

In addition to your course fees, you will need to ensure that you have adequate funds to support your living costs for the duration of your course.

For the 2024-25 academic year, the range of likely living costs for full-time study is between c. £1,345 and £1,955 for each month spent in Oxford. Full information, including a breakdown of likely living costs in Oxford for items such as food, accommodation and study costs, is available on our living costs page. The current economic climate and high national rate of inflation make it very hard to estimate potential changes to the cost of living over the next few years. When planning your finances for any future years of study in Oxford beyond 2024-25, it is suggested that you allow for potential increases in living expenses of around 5% each year – although this rate may vary depending on the national economic situation. UK inflationary increases will be kept under review and this page updated.

If you are studying part-time your living costs may vary depending on your personal circumstances but you must still ensure that you will have sufficient funding to meet these costs for the duration of your course.

Students enrolled on this course will belong to both a department/faculty and a college. Please note that ‘college’ and ‘colleges’ refers to all 43 of the University’s colleges, including those designated as societies and permanent private halls (PPHs). 

If you apply for a place on this course you will have the option to express a preference for one of the colleges listed below, or you can ask us to find a college for you. Before deciding, we suggest that you read our brief  introduction to the college system at Oxford  and our  advice about expressing a college preference . For some courses, the department may have provided some additional advice below to help you decide.

The following colleges accept students for full-time study on this course:

• Balliol College
• Corpus Christi College
• Exeter College
• Hertford College
• Jesus College
• Keble College
• Kellogg College
• Lady Margaret Hall
• Linacre College
• Mansfield College
• New College
• Reuben College
• St Cross College
• St Edmund Hall
• Worcester College

The following colleges accept students for part-time study on this course:

Before you apply

Our  guide to getting started  provides general advice on how to prepare for and start your application. You can use our interactive tool to help you  evaluate whether your application is likely to be competitive .

If it's important for you to have your application considered under a particular deadline – eg under a December or January deadline in order to be considered for Oxford scholarships – we recommend that you aim to complete and submit your application at least two weeks in advance . Check the deadlines on this page and the  information about deadlines and when to apply  in our Application Guide.

Application fee waivers

An application fee of £75 is payable per course application. Application fee waivers are available for the following applicants who meet the eligibility criteria:

• applicants from low-income countries;
• refugees and displaced persons; 
• UK applicants from low-income backgrounds; and 
• applicants who applied for our Graduate Access Programmes in the past two years and met the eligibility criteria.

You are encouraged to  check whether you're eligible for an application fee waiver  before you apply.

Readmission for current Oxford graduate taught students

If you're currently studying for an Oxford graduate taught course and apply to this course with no break in your studies, you may be eligible to apply to this course as a readmission applicant. The application fee will be waived for an eligible application of this type. Check whether you're eligible to apply for readmission .

Application fee waivers for eligible associated courses

If you apply to this course and up to two eligible associated courses from our predefined list during the same cycle, you can request an application fee waiver so that you only need to pay one application fee.

The list of eligible associated courses may be updated as new courses are opened. Please check the list regularly, especially if you are applying to a course that has recently opened to accept applications.

Do I need to contact anyone before I apply?

Before submitting an application, you may find it helpful to contact a potential supervisor or supervisors from among the online profile of StatML academics based in Oxford. This will allow you to discuss the matching of your interests with those of the centre, although there is no guarantee that this specific individual will become your supervisor if you are accepted. Please ensure that you have researched the specialisms of the department and those of your potential supervisor(s) before making contact. More information can be found on the  StatML website .

You can either contact the academic staff member directly or route your enquiry via the Admissions Administrator using the contact details provided on this page.

Completing your application

You should refer to the information below when completing the application form, paying attention to the specific requirements for the supporting documents .

For this course, the application form will include questions that collect information that would usually be included in a CV/résumé. You should not upload a separate document. If a separate CV/résumé is uploaded, it will be removed from your application .

If any document does not meet the specification, including the stipulated word count, your application may be considered incomplete and not assessed by the academic department. Expand each section to show further details.

You will also need to  complete the declaration form  once you have applied for this course.  

Proposed field and title of research project

Proposed supervisor.

Under 'Proposed supervisor name' enter the name of the academic(s) who you would like to supervise your research. 

Referees: Three overall, academic preferred

Whilst you must register three referees, the department may start the assessment of your application if two of the three references are submitted by the course deadline and your application is otherwise complete. Please note that you may still be required to ensure your third referee supplies a reference for consideration.

Your references should generally be academic, though up to one professional reference will be accepted.

Your references will support intellectual ability, academic achievement, motivation and your ability to work in a group.

Official transcript(s)

Your transcripts should give detailed information of the individual grades received in your university-level qualifications to date. You should only upload official documents issued by your institution and any transcript not in English should be accompanied by a certified translation.

More information about the transcript requirement is available in the Application Guide.

Statement of purpose/personal statement: A maximum of 1,100 words

Your statement should be written in English and should specify the broad areas in which your research interests lie -- what motivates your interest in these fields, and why do you think you will succeed in the programme?

The personal statement should describe your academic and career plans, as well your motivation and your scientific interests. When writing your personal statement, please make sure it answers the following questions:

• What are your machine learning/statistical interests?
• Why do you think the Statistics and  Machine Learning CDT is the right choice for you?

If possible, please ensure that the word count is clearly displayed on the document.

Your statement will be assessed for:

• your reasons for applying
• evidence of understanding of the proposed area of study
• your ability to present a coherent case in proficient English
• your commitment to the subject, beyond the requirements of the degree course
• your preliminary knowledge of the subject area and research techniques
• your capacity for sustained and intense work
• your reasoning ability
• your ability to absorb new ideas, often presented abstractly, at a rapid pace.

Start or continue your application

You can start or return to an application using the relevant link below. As you complete the form, please  refer to the requirements above  and  consult our Application Guide for advice . You'll find the answers to most common queries in our FAQs.

As the admissions process for StatML will be run in parallel with Imperial College London, we ask that you please  complete the declaration form once you have applied to one or both of the institutions.

Application Guide   Apply - FT   Apply - PT   Declaration Form

ADMISSION STATUS

Open - applications are still being accepted

Up to a week's notice of closure will be provided on this page - no other notification will be given

12:00 midday UK time on:

Friday 1 March 2024 Applications may remain open after this deadline if places are still available - see below

A later deadline shown under 'Admission status' If places are still available,  applications may be accepted after 1 March . The 'Admissions status' (above) will provide notice of any later deadline.

*Three-year average (applications for entry in 2021-22 to 2023-24)

This course was previously known as Modern Statistics and Statistical Machine Learning 

Further information and enquiries

This course is offered by the University's Department of Statistics , in partnership with Imperial College London

• Course page on the centre's website
• Funding information from the centre
• Academic and research staff  (incl. Imperial)
• Departmental research in Oxford
• Mathematical, Physical and Life Sciences
• Residence requirements for full-time courses
• Postgraduate applicant privacy policy

Course-related enquiries

Advice about contacting the department can be found in the How to apply section of this page

✉ [email protected] ☎ +44 (0)1865 272876  (Oxford)

Application-process enquiries

See the application guide

Visa eligibility for part-time study

We are unable to sponsor student visas for part-time study on this course. Part-time students may be able to attend on a visitor visa for short blocks of time only (and leave after each visit) and will need to remain based outside the UK.

Report a problem

Thank you, your report has been submitted. We will deal with the issue as soon as possible. If you have any other questions, please send an email to [email protected] .

Your Programmes

Imperial college london, undergraduate  .

1 in 4 undergraduate applicants received an offer in 2021/22.

Postgraduate taught

1 in 4 postgraduate taught applicants received an offer in 2021/22.

Undergraduate Programs with at least 15 applications

Most competitive among programs with at least 5 offers, least competitive  , most applications  , fewest applications  , postgraduate taught programs with at least 15 applications, postgraduate research programs with at least 15 applications, data sources.

• FOI Request by B.H. Crozier. January 2018.
• Transparency information . Imperial College London. October 2022.
• FOI Request by L. Zhao. December 2021.

The acceptance rate , or offer rate, represents the fraction of applicants who received an offer. Note that this will be generally lower the acceptances rates (acceptances divided by applicants) published by many other sources. This article explains it in more detail. The acceptances generally indicate the number of offer holders who accepted the offer and fulfilled its conditions. For some universities, however, it denotes the number of applicants who accepted the offer, regardless of whether they subsequently met its conditions.

Data Reliability

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Imperial Launches First-Of-Its-Kind Degree In Economics, Finance & Data Science

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Imperial College of London will launch a new undergraduate degree combining finance, economics, and data science in October 2023. Courtesy photo

In an era when Big Data is getting, well, much bigger, demand is soaring for professionals who can make sense of all that information while applying insights to increasingly complex business problems. The Graduate Management Admission Council reports that 62% of corporate recruiters plan to hire MSBA graduates in 2021, up from 47% in 2020.

Business schools are responding by offering new programs to teach students not only how to dissect the data but to extract meaning and propose solutions from it. Imperial College of London is the latest B-school to jump into the fray.

Earlier this month, Imperial announced a new bachelor’s degree allowing students to study economics and finance with a focus on data science. The degree, billed as the first undergraduate degree of its kind in the United Kingdom, also offers a new approach to leadership to prepare the next generation of economists, policy experts, and business leaders, the school says. The college’s new BSc in Economics, Finance and Data Science will enroll its first cohort in October 2023.

HOPPING ON THE ANALYTICS BANDWAGON

Over the last few years, business analytics has been among the hottest trends in business education with several schools jumping on the bandwagon including Notre Dame’s Mendoza College of Business . This fall, the Tepper School of Business at Carnegie Mellon will launch its first full-time Master’s of Science in Business Analytics for new college graduates who want to supercharge their data science skills and kickstart their careers. And, University of Pennsylvania’s Wharton School , long known as a finance powerhouse, reported that data analytics has become one of its most popular majors in both undergraduate and graduate programs, and has built data science into the core curriculums.

Pedro Rosa Dias

Imperial College of London created its new undergraduate degree in response to employer demand.

“We live in the age of big data and this has revolutionized the workplace and transformed our way of understanding the world’s complexities,” says Pedro Rosa Dias, academic director of the degree.

“In developing this program, the feedback from employers has been clear: we need a new generation of graduates in economics and finance who are able to use data science to guide businesses, public bodies and international organizations in today’s digital economy.”

BIG DATA AND CODING SKILLS EMBEDDED INTO CURRICULUM

In its announcement, Imperial says the new degree reimagines the traditional study of economics and finance by incorporating data science, analytics, and coding skills directly into the curriculum. “Students will develop the skills needed to succeed in a range of roles in industries such as technology, finance, consulting and the public sector, including central banks, regulatory bodies, think-tanks and international organizations,” the school says. “The program embeds societal impact, diversity and sustainability in its design.”

Students who graduate with the degree will do so having a deep, multidisciplinary understanding of how do use data to inform their decision making, says Emma McCoy, vice provost for education and student experience.

“Education will be the most powerful instigator of our global economic recovery from the pandemic and in addressing global disruption, and the university students of today will be the thought leaders of the future,” McCoy says.

“In launching this degree, Imperial is supporting that next generation to influence the debates that will shape our society for many years to come.” The program was developed by Imperial’s academics at the forefront of each discipline with input from industry and public policy leaders.

FLEXIBILITY BUILT IN

The degree is designed to take just three years. Students will learn to analyze issues such as climate change, global inequality, financial market dynamics, and drivers of technological innovation through the study of economics and finance. Through data science, students will learn how to use statistics, econometrics, and real-world data to explore the causes of such issues and to propose solutions, according the course website .

Besides the three core competencies–economics, finance, and data science–students will also be required to take required skills courses to prepare them for a career in business. This includes effective communication, emotional intelligence, and other skills employers have identified as essential. Flexibility has been built into the curriculum. Students are immersed in all core subjects during the first two years with the chance to further specialize in the third year. Electives across all three disciplines will be offered alongside career guidance and mentorship.

Applicants are required to possess strong skills in mathematics, show good analytical and language skills and hold an interest in addressing global issues. Learn more about the new degree at the Imperial College Business School website.

Questions about this article? Email us or leave a comment below.

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Course closed:

Data Intensive Science is no longer accepting new applications.

The course responds to the growing:

• demand for highly trained research scientists to design and implement data analysis pipelines for the increasingly large and complex data sets produced by the next generation of scientific experiments;
• societal demand for data science and data analysis skills in the industry, especially when applied in strategic domains (science, health) and economic areas (finance, e-commerce);
• need to train postgraduate students with a deep understanding of data science techniques and algorithm building for modern computer architectures and utilising industry best practices for software development;
• importance of open science in research, specifically reproducibility of scientific results and the creation of public data analytic codes.

Learning Outcomes

By the end of this course, students will have: 

• thorough knowledge of statistical analysis including its application to research and how it underpins modern machine learning methods;
• comprehensive understanding of data science and machine learning techniques and packages and their application to several practical research domains;
• developed advanced skills in computer programming utilising modern software development best practices created in accordance with Open Science standards;
• demonstrated abilities in the critical evaluation of data science tools and methodologies for their real-world application to scientific research problems.

Students wishing to progress to PhD study after passing the Masters degree should apply for admission to a PhD through the University admissions website, taking the funding and application deadlines into consideration.

The Department of Physics and other MPhil participating Departments contribute to the University of Cambridge's Postgraduate Open Day.

The Postgraduate Virtual Open Day usually takes place at the end of October. It is a great opportunity to ask questions to admissions staff and academics, explore the Colleges virtually, and to find out more about courses, the application process and funding opportunities. Visit the  Postgraduate Open Day  page for more details.

Departments

This course is advertised in the following departments:

• Department of Applied Mathematics and Theoretical Physics
• Institute of Astronomy
• Department of Physics

Key Information

10 months full-time, study mode : taught, master of philosophy, department of physics this course is advertised in multiple departments. please see the overview tab for more details., course - related enquiries, application - related enquiries, course on department website, dates and deadlines:, michaelmas 2024 (closed).

Some courses can close early. See the Deadlines page for guidance on when to apply.

Funding Deadlines

These deadlines apply to applications for courses starting in Michaelmas 2024, Lent 2025 and Easter 2025.

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