Data Science and Financial Technologies at Goldsmiths, University of London - UCAS

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Course summary

The MSc Data Science and Financial Technology pathway will provide you with the technical and practical skills on how to use the technologies in investment analysis, algorithmic trading, risk management, and payment and fraud detection. The rate at which society is creating data is rapidly accelerating; outstripping our ability to successfully analyse and use it. The MSc in Data Science and Financial Technology will give you the skills to join the next generation of data scientists, leading the way in areas from climate change to financial forecasting. Why study MSc Data Science and Financial Technology

  • You’ll learn the fundamentals of data science, gaining foundational skills in statistics, data mining, data visualisation, programming and machine learning, giving you the skills and techniques to efficiently analyse very large data sets.
  • You’ll combine this with specialist modules in financial technology such as blockchain, to redesign many of the existing processes in banking and finance.
  • This degree is interdisciplinary, and you’ll learn from experts in the Department of Computing and the Institute of Management Studies. Many faculty members bring their knowledge from active research and consultancy in the areas of FinTech, data science, computing and financial technologies.
  • You’ll carry out original analysis on real-world financial data through your final project, which allows you to focus on your specific research interests.
  • With guidance from experts, you’ll learn to use industry-standard software such as Apache, Hadoop and R to analyse data from industries such as biomedical, financial and social media.
  • Our close links with industry mean that you’ll have the opportunity to learn from industry experts in guest lectures throughout the year.

Modules

Compulsory modules You'll take the following compulsory modules: Data Programming 15 credits Statistics and Statistical Data Mining 15 credits Financial Data Modelling 15 credits Blockchain Programming 15 credits Mathematics for Financial Markets 15 credits Big Data Applications 15 credits Final Project in Data Science 60 credits Optional modules You'll also take two optional modules from across the Department of Computing and Institute of Management Studies to the value of 30 credits. These vary from year to year, and may include the following: Econometrics 15 credits From National Statistics to Big Data 15 credits Advanced Econometrics 15 credits Marketing Strategy 15 credits Marketing Analytics 15 credits Digital Marketing and Branding 15 Credits Data Visualisation 15 Credits Artificial Intelligence 15 credits Neural Networks 15 credits Machine Learning 15 credits Data Science Research Topics 15 credits R Programming 15 credits Please note that due to staff research commitments not all of these modules may be available every year.


Entry requirements

We do not assume that you will have programmed before, but we do require a level of mathematical dexterity that is commensurate with having completed a numerate degree. You’ll require a BA or BSc Degree at 2.1 level or above in subjects like computer science, mathematics, statistics, engineering, economics or finance. We accept a wide range of international qualifications. If English isn’t your first language, you will need an IELTS score (or equivalent English language qualification) of 6.5 overall and no element lower than 6.0 to study this programme.


Fees and funding

Tuition fees

No fee information has been provided for this course

Additional fee information

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Data Science and Financial Technologies at Goldsmiths, University of London - UCAS