Shaping futures ulster.ac.uk

MSc Data Science Welcome from Course Director

FACULTY OF COMPUTING ENGINEERING AND BUILT ENVIRONMENT

2019

PT-JN

Dear Student,

A very warm welcome to you as you embark on your Postgraduate studies within the School of Computing, Engineering and the Intelligent Systems at Ulster University. The staff within the school are delighted that you are joining us. Our student community at Ulster University is a large and diverse one. There are students from a very wide range of countries and backgrounds, taking courses from degree to PhD level in many different subjects. For that reason alone, I think you will find this an exciting place to be.

We hope you enjoy studying the subject area of Data Science on the Jordanstown Campus. The buildings and facilities at Jordanstown as exceptional and as the School of Computing, Engineering and the Intelligent Systems, we endeavour to ensure you will have access to the state-of-the-art computing facilities while progressing through your studies with us. Jordanstown is also in an excellent location close to Belfast city, with a huge variety of social and cultural places and opportunities to explore right on our doorstep.

Please ensure that you read through your Welcome Week webpage in detail. During Welcome Week, there will be a programme of activities arranged for you. You will complete the important formalities of registration, and you will also find out important information from the Course Induction session I will deliver so please make every effort to attend these events. In addition, there will be other events scheduled around Welcome Week such as Library Induction, Student Union Talk, etc. which are available for you to attend.

Before you arrive, you can start to get a feel for the University, find out more about our School with respect to Teaching and Research excellence, find out what Clubs and Societies are available to join, etc. by looking on our website (www.ulster.ac.uk). Further detail specific to your course is provided on the following page, for example, detail on the course structure and an initial Reading List compiled of material that will aid your studies throughout the course. Once you arrive at the University, staff will do their best to answer any questions you have, so please do not hesitate to ask.

I look forward to meeting you at Course Induction.

Kind Regards, Bryan

Dr. Bryan Gardiner Course Director MSc Data Science T: +4428 716 75081 E: b.gardiner@ulster.ac.uk W: www.ulster.ac.uk/staff/b.gardiner.html Facebook: @UlsterUniComputingEngineeringIntelligentSystems Twitter: @SceisUni

PT-JN

MSc in Data Science Full-time/Part-time Magee Campus; Part-time Jordanstown Campus

In the MSc Data Science course, all modules are compulsory and student choice is facilitated in the research project which will be a self-selected topic. The course structure for part-time students is detailed in Figure 1. The course is offered over three calendar years in part-time mode with the taught components delivered over Semesters 1 and 2 of the first two years. Each taught module is typically delivered on one evening, between 4.15pm-9.05pm per week, throughout a twelve-week semester. Part-time students will study 60 credits in each year of study. On completion of the 120 credit taught component, successful students will be eligible for the exit award of the PgDip Data Science. Students who are not successful at this stage, but have achieved a minimum of 60 credit points will be considered for the exit award of a PgCert Data Science.

In Semester 3 of year 2 and Semester 1 of year 3, part-time students will undertake an individual project worth a further 60 credits and on successful completion will be eligible for the MSc award.

Figure 1: Course Structure - MSc Data Science (Part-time)

Reading List

• VanderPlas, J. (2016). Python Data Science Handbook: Essential Tools for Working with Data. 1st Edition. Sebastopol, CA: O'Reilly Media

• Schneider, R., (2012), Hadoop for Dummies Special edition, Wiley Publishing • Sherman, R. (2014) Business Intelligence Guidebook: From Data Integration to Analytics,

Morgan Kaufmann. • Dale K., (2016), Data Visualization with Python and JavaScript, O'Reilly Media. • Bishop C.M., (2006), Pattern Recognition and Machine Learning, Springer. • Grolemund G. and Wickham H., (2016), R for Data Science, 1st Edition, Sebastopol Canada,

O'Reilly Media.