Section outline

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    General

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    This space is designed to give important information and updates to current students of the MSc Data Analytics about their programme of study.

    Your Programme Director: Dr. Nicola Perra

    Email: n.perra@qmul.ac.uk

    Office: MB-G26

    Website: https://www.qmul.ac.uk/maths/profiles/perran.html

    This programme is divided in three parts.

    In the first term, you will complete the compulsory modules that provide a foundation in data analytics, machine learning, and the statistics of data analysis. You will build upon this knowledge working with industry-standard tools and software.

    In the second term, you will decide on your specialism. This part of the programme has been designed to prepare you for specific career paths, so what you study will depend on what you wish to do in the future.

    Our specialisms, also known as streams, can be found below. We've highlighted the skills you will develop and the potential career paths for each stream.

    Applied Machine Learning

    You will learn to use mathematical, computational and statistical techniques to design, implement, and evaluate machine learning models to solve real-world problems.
    Potential career opportunities in developing and deploying predictive tools in Finance, Healthcare, Retail, and Manufacturing sectors among others.

    Pattern Recognition and Deep Learning

    You will learn to use mathematical, computational and statistical techniques to extract and learn patterns from different types of data.
    Potential career opportunities in developing advanced data insights tools in Tech, Finance, Healthcare, Retail, Automotive, and Manufacturing sectors among others.

    Statistical Inference

    You will learn to use mathematical, computational and statistical techniques to analyze data, draw meaningful conclusions, and contribute to evidence-based decision-making across application domains.
    Potential career opportunities in developing advanced statistical models in Finance, Healthcare, Retail, and Manufacturing sectors among others.

    In the third term, over the summer, you will work alongside one of our researchers on an independent research project. This will consolidate your learning and allow you to develop strong applied data science skills.

    You do not need to be a programming expert. You will explore a variety of data analysis tools (such as R and Python).

    We offer all students the opportunity to take the Microsoft Office Specialist qualification which includes valuable expertise in Excel.

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    Student Handbook

    The SMS Student Handbook includes information on the requirements to obtain the MSc Data Analytics degree as well as procedural information, such as what to do should you miss an assessment due to circumstances beyond your control.

    The School of Mathematical Sciences (SMS) Student Handbook is available at: https://qmplus.qmul.ac.uk/mod/book/view.php?id=2210700

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    Teaching Timetable

    Once you are registered for your modules you will be able to view a personalised timetable via both your QMPlus home page and your My SIS profile space. 

    If you need to check the teaching times/locations for any modules you are not registered to you can use the QMUL Timetables website at: https://timetables.qmul.ac.uk/default.aspx.

    You are strongly advised to check your teaching timetable at the beginning of each week in case of any changes to timing and/or location information.

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    Programme

    The Program is organised in three semesters. In semester A you will take four compulsory modules. In semester B you will take two compulsory and two elective modules. Each student will have to select one of three streams: 1) Applied Machine Learning, 2) Pattern Recognition and Deep Learning, 3) Statistical Inference. As detailed in the pdf below the list of compulsory and elective modules is function of the stream selected. Finally semester C is devoted to the final project.

    Part-time students will split the effort in two years, see details in the pdf below

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    Module Registration

    The Process


    You are expected to register for your modules within the first two weeks of semester one. The module registration task is completed via your My SIS profile (https://mysis.qmul.ac.uk/urd/sits.urd/run/siw_lgn). You will select the stream and modules you wish to register for and submit your choices via MySIS. Once your choices have been submitted you will be registered to the chosen modules and they will appear on your MySIS module page, timetable and your QM Plus profile.  

    When choosing your stream and modules you should refer to the MSc Data Analytics programme structure, which can be found at: https://www.qmul.ac.uk/maths/postgraduate/taught-programmes/msc-data-analytics/, and the info in this QM+ page. 

    If during semester one you decide you need to change the choices you have made for semester two you will be given the opportunity to do so within the first two weeks of semester two.

    The Modules
    To view descriptors of the modules see description at the top of the page (coming in the next few days) or visit: https://www.qmul.ac.uk/maths/postgraduate/taught-programmes/msc-data-analytics/

    Advice
    If you have any questions regarding the module registration process you should contact your Programme Director, Dr Nicola Perra (n.perra@qmul.ac.uk) who will be happy to advise you.

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    Careers Information

    QM Careers Service
    Throughout your time as a postgraduate student at QMUL, and for up to two years after you graduate, you will have access to the QM Careers service.

    Your School's have dedicated careers consultants who will keep you informed, via email and social media, about relevant career events and opportunities taking place at QMUL and externally. Andrea Pinner  is the consultant for SMS.  To make sure you are aware of and can take advantage of all the events and opportunities happening by following QM Careers on Twitter: https://twitter.com/qmcareers. 

    Networking
    Attending the careers events gives you the chance to meet people working in your industry. We encourage you to make the most of any contact you have with professionals by asking them questions and seeking their advice about the realities of their career; how they secured their role and the steps they are taking to succeed in it.

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    Student Support and Representation

    Student Support
    If you have a problem, the most important thing to do is to talk to someone about it. It is best to arrange a meeting with your Programme Director, Dr. Nicola Perra, or the Student Support Officer, Hamida Begum. Otherwise you may wish to speak to one of your module organisers or your MSc student representative. If you prefer not to discuss your problem with a member of the School, you can obtain help and advice directly from the QMUL Advice and Counselling Service.

    More information about the support available to you can be found here https://www.qmul.ac.uk/maths/undergraduate/degree-programmes/student-support/

    Representation

    Student Staff Liaison Committee
    The MSc Student Staff Liaison Committee (SSLC) acts as the main forum for discussion between staff and students registered on an MSc programme and includes MSc students representatives as well as staff involved in the MSc programme delivery. The SSLC meets twice in an academic year. Your representative will attend meetings in SMS to raise and be aware of any respective issues affecting your studies.

    Data Analytics Course Representative
    If you wish to nominate yourself or someone else to be the Course Representative then please contact the Programme Director.


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    Student Facilities

    You have access to a number of useful facilities within SMS. Details of the facilities available and useful information regarding access can be found below:

    SMS MSc Computer Lab
    The lab, located in room MB302 of the Maths' Building contains a number of workstations for project work, programming, research, etc. The lab offers students free access to a black and white printer.

    Access Info:
    1. You'll need to use your student ID card to get in, all enrolled students are automatically set up with access to the lab.
    2. You have been issued with your log-in information at the programme induction session. If for any reason you do not have your log-in details please contact the Postgraduate Taught Programmes Officer (maths@qmul.ac.uk).


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    Events

    MSc Careers Events

    The QMUL Careers & Enterprise Service is found here: https://www.qmul.ac.uk/careers/

    The Service runs various events and workshops throughout the year, the list of which is updated periodically and can be found here:  https://www.qmul.ac.uk/careers/events/

    The service provides various opportunitis from work experience, to internships, to jobs, which can be found here: https://www.qmul.ac.uk/careers/work-experience/

    School of Mathematical Sciences Seminars
    You are welcome to attend any seminars run within the School of Mathematical Sciences. For more information about the seminars, including a schedule, please see the School's seminar webpages, found here: https://www.qmul.ac.uk/maths/research/seminars/

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    Mathematical Resources

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    Program Structure

    Semester A

    Compulsory Modules 

    • MTH794P   Probability and Statistics for Data Analytics
    • MTH786P   Machine Learning with Python
    • MTH765P   Storing, Manipulating, and Visualising Data
    • MTH766P   Programming in Python

    Semester B

    Applied Machine Learning Stream

      Compulsory modules

    • MTH769P    Forecasting with AI
    • MTH791P    Computational Statistics with R

      Choose two from

    • MTH776P    Bayesian Statistics
    • MTH782P    SAS for Business Intelligence
    • MTH784P    Optimisation for Business Processes
    • MTH792P    Financial Data Analytics
    • MTH741P    Digital and Real Asset Analytics

    Pattern Recognition and Deep Learning Stream

      Compulsory modules

    • MTH767P    Neural Networks and Deep Learning
    • MTH793P    Advanced Machine Learning

      Choose two from

    • MTH750P    Graphs and Networks
    • MTH776P    Bayesian Statistics
    • MTH769P    Forecasting with AI
    • MTH791P    Computational Statistics with R

    Statistical Inference Stream

      Compulsory modules

    • MTH776P    Bayesian Statistics
    • MTH791P    Computational Statistics with R

      Choose two from

    • MTH750P    Graphs and Networks
    • MTH767P    Neural Networks and Deep Learning
    • MTH793P    Advanced Machine Learning
    • MTH769P    Forecasting with AI

    Semester A-B-C

    • MTHM038    MSc Data Analytics Dissertation