Course information

Department

Computing

Length

1 year full-time or 2 years part-time

Course overview

The MSc in Data Science will provide you with the technical and practical skills to analyse the big data that is the key to success in future business, digital media and science.

The rate at which we are able to create data is rapidly accelerating. According to IBM, globally, we currently produce over 2.5 quintillion bytes of data a day. This ranges from biomedical data to social media activity and climate monitoring to retail transactions. These enormous quantities of data hold the keys to success across many domains from business and marketing to treating cancer or mitigating climate change.

The pace at which we produce data is rapidly outstripping our ability to analyse and use it. Science and industry are crying out for a new generation of data scientists who combine the statistical skills of data analysis and the computational skills needed to carry out this analysis on a vast scale.

The MSc in Data Science provides you with these skills. 

Studying this Masters, you will learn the mathematical foundations of statistics, data mining and machine learning, and apply these to practical, real world data.

As well as these statistical skills, you will learn the computational techniques needed to efficiently analyse very large data sets. You will apply these skills to a range of real world data, under the guidance of experts in that domain. You will analyse trends in social media, make financial predictions and extract musical information from audio files. 

The degree will culminate in a final project in which you will you can apply your skills and follow your specialist interests. You will do a novel analysis of a real world data of your choice. 

The programme includes:

  • A firm grounding in the theory of data mining, statistics and machine learning
  • Hands-on practical real world applications such as social media, biomedical data and financial data with Hadoop (used by Yahoo!, Facebook, Google, Twitter, LinkedIn, IBM, Amazon, and many others), R and other specialised software
  • The opportunity to work with real-world software such as Apache

Talks

We have close links with industry and regularly invite leading professionals to deliver lectures and talks on Data Science. 

Staff

Contact the department

If you have specific questions about the degree, contact the Programme Director, Dr Daniel Stamate

What you'll study

You will study the following core modules:
Module title Credits
  Machine Learning & Statistical Data Mining 30 credits
  Big Data Applications 15 credits
  Data Programming 15 credits
  Data Science Research Topics 15 credits
  Final Project in Data Science 60 credits

You will also choose from an anually approved list of modules which may include:

Module title Credits
  Artificial Intelligence 15 credits
  Interaction Design 15 credits
  Neural Networks 15 credits
  Data Visualisation and the Web 15 credits
  Introduction to Natural Language Processing 15 credits
  Geometric Data Analysis 15 credits

Download the programme specification for the 2018-19 intake. If you would like an earlier version of the programme specification, please contact the Quality Office.

Please note that due to staff research commitments not all of these modules may be available every year.

Entry requirements

You should have an undergraduate degree of at least upper second class standard in computing, physics and engineering, mathematical sciences or finance, and an interest in and capability for working in interdisciplinary contexts. 

In exceptional circumstances, outstanding practitioners or individuals with strong commercial experience may be considered.

International qualifications

We accept a wide range of international qualifications. Find out more about the qualifications we accept from around the world.

If English isn’t your first language, you will need an IELTS score of 6.5 with a 6.5 in writing and no element lower than 6.0 to study this programme. If you need assistance with your English language, we offer a range of courses that can help prepare you for postgraduate-level study.

Fees, funding & scholarships

Find out more about tuition fees.

Find out more about funding opportunities for home/EU applicants, or funding for international applicants. If you're applying for funding, you may be subject to an application deadline.

The Department of Computing offers a number of scholarships for students with remarkably good applications. The scholarships will be a one-off payment of £2,000. You don't need to submit a separate application to be considered for one of these awards. You can find out more from the department.

How to apply

You apply directly to Goldsmiths using our online application system. 

Before submitting your application you'll need to have: 

  • Details of your education history, including the dates of all exams/assessments
  • The email address of your referee who we can request a reference from, or alternatively an electronic copy of your academic reference
  • personal statement – this can either be uploaded as a Word Document or PDF, or completed online

          Please see our guidance on writing a postgraduate statement

  • If available, an electronic copy of your educational transcript (this is particularly important if you have studied outside of the UK, but isn’t mandatory)

You'll be able to save your progress at any point and return to your application by logging in using your username/email and password.

When to apply 

We accept applications from October for students wanting to start the following September. 

We encourage you to complete your application as early as possible, even if you haven't finished your current programme of study. It's very common to be offered a place conditional on you achieving a particular qualification. 

If you're applying for external funding from one of the Research Councils, make sure you submit your application by the deadline they've specified. 

Late applications will only be considered if there are spaces available.

Selection process 

Admission to many programmes is by interview, unless you live outside the UK. Occasionally, we'll make candidates an offer of a place on the basis of their application and qualifications alone.

Find out more about applying.

Facilities

Facilities include a Big Data Management & Analytics dedicated cluster of servers based on some of the most recent Xeon processors and fast RAM, and lab workstations, providing access to software platforms and languages specialised in Machine Learning, Data Mining, Statistical Analysis, and Big Data including:

  • R
  • IBM SPSS
  • SAS
  • Python
  • Apache Hadoop ecosystem
  • NoSQL databases as MongoDB
  • Apache Spark
  • Apache Storm
  • RapidMiner
  • Tableau

Our facilities also include a large and up to date collection of books on the topic (a sample of which is illustrated here) available to borrow from Goldsmiths' Library. You can look under the Data Mining module to find out more about this.

Careers

Data Science is one of the fastest growing sectors of employment internationally. Big Data is an important part of modern finance, retail, marketing, science, social science, medicine and government. 

The study of a combination of long established fields such as statistics, data mining, machine learning and databases with very modern and strongly related fields as big data management and analytics, sentiment analysis and social web mining, offers graduates an excellent opportunity for getting valuable skills in advanced data processing. 

This could lead to a variety of potential jobs including: 

  • Data Scientist
  • Data Mining Analyst
  • Big Data Analyst
  • Hadoop Developer
  • NoSQL Database Developer
  • R Programmer
  • Python Programmer
  • Researcher in Data Science and Data Mining

Find out more about employability at Goldsmiths.

What our students say

Gabriel

"The leadership of this programme was amazing – probably the best supervision and mentorship throughout my higher education period."

"I studied Data Science at Goldsmiths from 2016 to 2017. Actually, I jumped into the MSc in Data Science from my PhD programme in Social Sciences. The reason for this switch was the increasing importance of data science in all walks of life and sectors. I have since been offered a job at a renowned, world-class university in London.

The programme has been an amazing learning process. I have learnt as much in one year as in ten years of social sciences. The leadership of this programme was amazing as well – probably the best supervision and mentorship throughout my higher education period. Additionally, the syllabus was created in such a way that any lay person will get the chance to learn Machine Learning, coding, Natural Language Processing and Big Data. It is indeed a valuable program, with so much mentorship alongside as well as many project opportunities.

One of the projects I have been involved in, with the Data Science laboratory the department owns, was in the health sector. Specifically, using Machine Learning in predicting the time of remission in psychotic patients. It was a valuable and meaningful experience that lead to other opportunities, such as working in the health sector as a research assistant in Data Science, at Imperial College."

Frederic

"The high quality of tuition and care for students coupled with extracurricular activities proposed by Dr Stamate, as part of the MSc Data Science, have significantly helped my success in securing my ‘dream job’."

"The MSc Data Science (on-campus programme) lead by Dr Daniel Stamate is the right balance between theory and practice, across all modules I studied. The strong point of the MSc Data Science is that students can choose the subject for their coursework, for example, finance, medical studies, transport, etc. as long as expectations set with each lecturer are met. This enables each student to directly apply the Data Science/AI/Big Data skills to real-world problems of their interests.

It then gives the opportunity for students to build their portfolio of use cases, that can then be demonstrated during interviews.

Dr Daniel Stamate also provides the opportunity for proactive students to benefit from the ‘Data Science and Soft Computing Lab’ where students can further their knowledge, practice with real data and potentially meet with professionals from the industry. This was certainly the case in my experience and I am still in contact with some of these people to date.

I worked on many projects ranging from NLP, AI and Machine Learning domains. My major project was my MSc thesis which focused on analysing the predicting power of sentiment on the XLE index and XLE index’s constituents market trend and volatility.

On the big data side, I wrote a functional and performance comparison between two Hadoop components, namely Hive and Spark, in the context of analysing airport flights’ delays/cancellations.

On the AI side, I implemented and compared the performance of two informed search algorithms, namely the AStar and Jump First Point, based on numerous scenarios involving an agent moving in a 2D grid, containing ‘road blocks’.

I am now a Data Scientist working for a major commercial bank. As a summary, the high quality of tuition and care for students coupled with extracurricular activities proposed by Dr Stamate, as part of the MSc Data Science, have significantly helped my success in securing my ‘dream job’."

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