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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
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Qualification
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MSc
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Duration
2 years
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Start date
September 2024
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Study mode
Part-time
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Fees
£17,175 per year Home
£17,175 per year Overseas
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Delivered by
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Location
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Online
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Minimum entry standard
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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
Structure
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.
You’ll take all of these core modules
Core modules
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.
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.
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.
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.
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.
Build on your existing knowledge of ethics in data science and artificial intelligence and explore real-world issues.
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.
Examine subjective probabilities and the Bayesian paradigm for making coherent individual decisions in the presence of uncertainty.
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.
Assess the tools and techniques for solving unsupervised learning challenges, exploring topics including clustering, dimension reduction and density estimation.
Complete your studies examining the ethical implications of data science and artificial intelligence.
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.
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
Key
- Lectures and tutorials
- Independent study
Year 1
- 22% Lectures and tutorials
- 78% Independent study
Year 2
- 15% Lectures and tutorials
- 85% Independent study
Teaching and learning methods
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Virtual learning environment (Coursera)
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Lectures
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Tests
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Tutorials
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Coding exercises
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Reading
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Discussion boards and prompts
Assessment methods
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Coursework
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Multiple choice tests and online quizzes
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Research project
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Oral examination
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Written report
Entry requirements
We consider all applicants on an individual basis, welcoming students from all over the world.
How to apply
Applications are now closed for 2024 entry.
The deadline for completed applications and references is Friday 31 May 2024, to be considered for September 2024.
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.
Find out more about how to apply for a Master's course, including references and personal statements.
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: ml-online-msc@imperial.ac.uk
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).
An ATAS certificate is not required for students applying for this course.
Tuition fees
Home fee
2024 entry
£17,175 per year
You should expect and budget for your fees to increase each year.
Your fee is based on the year you enter the university, 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.
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.
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.
For courses starting on or after 1 August 2024, the maximum amount is £12,471. The loan is not means-tested and you can choose whether to put it towards your tuition fees or living costs.
The loan is not means-tested and you can choose whether to put it towards your tuition fees or living costs.
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:
- Singapore
Find out more how to pay GST and how much it is.
Overseas fee
2024 entry
£17,175 per year
You should expect and budget for your fees to increase each year.
Your fee is based on the year you enter the university, 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.
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.
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.
For courses starting on or after 1 August 2024, the maximum amount is £12,471. The loan is not means-tested and you can choose whether to put it towards your tuition fees or living costs.
The loan is not means-tested and you can choose whether to put it towards your tuition fees or living costs.
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:
- Singapore
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: ml-online-msc@imperial.ac.uk
Course Director: Professor Nick Heard.
Visit the Department of Mathematics website.
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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.
You can find further information about your course, including degree classifications, regulations, progression and awards in the programme specification for your course.
Programme specifications