Queen Mary Computing and information systems MSC (Convserion)

Hello,

Currently taking this.

Overall, I'm very positive on it.

I'd say, if you are interested in becoming a software developer, you'll have the grounding in a lot of the main areas. You should definitely make sure you build something very substantial for your project. If you're interested in becoming a systems analyst / business analyst then it is really good training.

Term one is a crash course in computing fundamentals. Term two you'll be taking classes with students on related conversion courses (e.g. data science) and 4th year undergrads. Summer you'll be doing your project.

There's more emphasis on skills around programming than we expected. You will program in the computer programming (this year Python, previously Java), database systems (coursework was in SQL, lectures included NoSQL), security and authentication (as part of pen-testing, and in a few of the cryptography exercises), and in data analytics (optional course). You'll also need to code an Android / iOS app for the mobile networks coursework. Also, the dissertation requires some tangible output, which is usually a working system of some sort or data analytics report. The introduction to software engineering focuses how to not shoot yourself in the foot and the long-term trade-offs between different design ideas over refactoring code directly. The elective interactive system design class is fantastic - you'll spend the term designing a system for someone with a particular need, prototype like a madman, and read a lot of different analyses about how people use systems and problems with prototyping & testing. The risk class had good content, but was a bit too easy (we've pretty much all asked for it to be made more challenging). Between risk and data analytics you'll spend a fair bit of time learning about causal (Bayesian) networks, assumptions in machine learning, and how to actually get something useful from these algorithms.

There are three types of dissertation projects you can do: one you come up with and find a lecturer for, one a lecturer suggests, or one with an external company. Check out the lecturers' areas of expertise. Interests include machine learning (NLP, deep learning, computer vision), data analytics, music technology, video games (there are lectures on uses of AI in video games), functional programming, social networks, etc. etc. etc. http://www.eecs.qmul.ac.uk/people/academic/ If a particular area grabs your interest, read a couple of journal articles and talk to the lecturer about possibilities around October time. Academic-suggested project range from the broad and do-able to the very difficult. This year, there were some projects with industry partners which conversion students could apply for. I'm not sure if this is the case every year, but have the impression they try for this.

The lecturers are generally very friendly and helpful. They are keen to hear what students think about the courses.