Data Science brings together computational and statistical skills for data-driven problem solving. This rapidly expanding area includes machine learning, deep learning, large-scale data analysis and has applications in e-commerce, search/information retrieval, natural language modelling, finance, bioinformatics and related areas in artificial intelligence. The MSc provides a principled understanding of the computational and statistical underpinnings of current and emerging methods.

Data Science is an emerging, and increasingly important academic discipline. The field is supported by vocational and commercial opportunities, in particular in London, which has a growing, vibrant community of data scientists and machine learning researchers.


MSc Data Science and Machine Learning comprises 8 taught modules and a Project. Of the taught modules, 3 are core modules, a minimum of 2 optional modules from Group 1. The remaining 3 modules may be a combination of optional and elective modules.

Core Modules Term 1

COMPGI21 Introduction to Machine Learning

COMPGI21 Introduction to Machine Learning

Students will become familiar with the conceptual landscape of machine learning and have developed practical skills to solve real world problems using available software.

 

Further syllabus information can be found here.

STATG006 Introduction to Data Science

STATG006 Introduction to Data Science

Taught by Department of Statistical Science - see here for syllabus

Core Modules Term 2

COMPGI09 Applied Machine Learning

COMPGI09 Applied Machine Learning

This module aims to cover some of the issues that may arise in the practical application of machine learning in real-world problems. In addition, the course will cover some of the mathematics and techniques behind basic data analysis methods for both static and time-series data.

On completion of the module, students will have the ability to assess the effectiveness of solutions presented and to question them in an intelligent way; synthesise solutions to general open-ended problems covering material from the whole programme, tempered with information on commercial reality obtained from this course.

 

Further syllabus information can be found here.

COMPGI99 Individual Project

COMPGI99 Individual Project

Further syllabus information will be available shortly.

Core Modules Project

COMPGI99 Individual Project

COMPGI99 Individual Project

Further syllabus information will be available shortly.

Optional Modules [Group 1] Term 1

08COG29H7 Cloud Computing (Birkbeck)

08COG29H7 Cloud Computing (Birkbeck)

Students in this module will learn to understand the emerging area of cloud computing and how it relates to traditional models of computing, and gain competence in MapReduce as a programming model for distributed processing of big data.

This module aims to introduce back-end cloud computing techniques for processing "big data" (terabytes/petabytes) and developing scalable systems (with up to millions of users). We focus mostly on MapReduce, which is presently the most accessible and practical means of computing for "Web-scale" problems, but will discuss other techniques as well.

Further syllabus information can be found here

COMPGI14 Machine Vision

COMPGI14 Machine Vision

The course addresses algorithms for automated computer vision. It focuses on building mathematical models of images and objects and using these to perform inference. Students will learn how to use these models to automatically find, segment and track objects in scenes, perform face recognition and build three-dimensional models from images.

At the end of the course, students will be able to understand and apply a series of probabilistic models of images and objects in machine vision systems. To understand the principles behind face recognition, segmentation, image parsing, super-resolution, object recognition, tracking and 3D model building.

 

Further syllabus information can be found here.

COMPGI19 Statistical Natural Language Processing

COMPGI19 Statistical Natural Language Processing

The course introduced the basics of statistical natural language processing (NLP) including both linguistics concepts such as morphology and syntax and machine learning techniques relevant for NLP.

Students successfully completing the module will understand relevant linguistic concepts; relevant ML techniques, what makes NLP challenging (and exciting), how to write programs that process language and how to rigorously formulate NLP tasks as learning and inference tasks, and address the computational challenges involved.

 

Further syllabus information can be found here.

COMPGI23 Introduction to Deep learning

COMPGI23 Introduction to Deep learning

At the conclusion of this module students should understand: 

 

  1. The fundamental principles, theory and approaches for learning with deep neural networks
  2. The main variants of deep learning (such convolutional and recurrent architectures), and their typical applications
  3. The key concepts, issues and practices when training and modeling with deep architectures; as well as have hands-on experience in using deep learning frameworks for this purpose
  4. How to implement basic versions of some of the core deep network algorithms (such as backpropagation)
  5. How deep learning fits within the context of other ML approaches and what learning tasks it is considered to be suited and not well suited to perform

 

Further syllabus information can be found here.

Optional Modules [Group 1] Term 2

COMPGI15 Information Retrieval & Data Mining

COMPGI15 Information Retrieval & Data Mining

The course is aimed at an entry level study of information retrieval and data mining techniques. It is about how to find relevant information and subsequently extract meaningful patterns out of it. While the basic theories and mathematical models of information retrieval and data mining are covered, the course is primarily focused on practical algorithms of textual document indexing, relevance ranking, web usage mining, text analytics, as well as their performance evaluations. Practical retrieval and data mining applications such as web search engines, personalisation and recommender systems, business intelligence, and fraud detection will also be covered.

Students are expected to master both the theoretical and practical aspects of information retrieval and data mining.

 

Further syllabus information can be found here.

COMPGI22 Advanced Deep Learning and Reinforcement Learning

COMPGI22 Advanced Deep Learning and Reinforcement Learning

Students successfully completing the module should understand:

  1. The basics of deep learning and reinforcement learning paradigms.
  2. Architectures and optimization methods for deep neural network training.
  3. How to implement deep learning methods within a given ML framework and apply them to data.
  4. The theoretical foundations and algorithms of reinforcement learning.
  5. How to apply reinforcement learning algorithms to environments with complex dynamics.

 

Further syllabus information can be found here.

COMPGW02 Web Economics

COMPGW02 Web Economics

The course is intended to provide an introduction of the computing systems and their economics for the production, distribution, and consumption of (digital) goods and services over the Internet and web. While the basic economic principles are covered to understand the business aspects of web-based services, the course is primarily focused on the computational and statistical methods for implementing, improving and optimizing the internet-based businesses, including algorithmic mechanism design, online auctions, user behavior targeting, yield management, dynamic pricing, cloud-sourcing, social media mining and attention economics. Practical applications such as Google’s online advertising, Ebay’s online auction, and Amazon’s cloud computing will also be covered and discussed.

Students will be expected to master both the theoretical and practical aspects of web economics.

 

Further syllabus information can be found here.

You will need to choose a minimum of 30 credits from the Group 1 optional modules.

Optional Modules [Group 2] Term 1

STATG002 Statistical Design of Investigations

STATG002 Statistical Design of Investigations

Taught by Statistics - see here for syllabus.

Optional Modules [Group 2] Term 2

STATG004 Applied Bayesian Methods

STATG004 Applied Bayesian Methods

Taught by Department of Statistical Science - see here for syllabus

STATG009 Decision and Risk

STATG009 Decision and Risk

Taught by Statistics - see here for syllabus.

STATG011 Forecasting

STATG011 Forecasting

To introduce methods of finding and extrapolating patterns in time-ordered data.

Further syllabus information can be found here (page 33).

You can choose up to 30 credits from the Group 2 optional modules.

Elective Modules Term 1

COMPGI01 Supervised Learning

COMPGI01 Supervised Learning

This module covers supervised approaches to machine learning. It starts by reviewing fundamentals of statistical decision theory and probabilistic pattern recognition followed by an in-depth introduction to various supervised learning algorithms such as Perceptron, Backpropagation algorithm, Decision trees, instance-based learning, support vector machines. Algorithmic-independent principles such as inductive bias, side information, approximation and estimation errors. Assessment of algorithms by jackknife and bootstrap error estimation, improvement of algorithms by voting methods such as boosting. Introduction to statistical learning theory, hypothesis classes, PAC learning model, VC-dimension, growth functions, empirical risk minimization, structural risk minimization.

Students will gain an in-depth familiarity with various classical and contemporary supervised learning algorithms, understand the underlying limitations and principles that govern learning algorithms and ways of assessing and improving their performance, understand the underlying fundamentals of statistical learning theory, the complexity of learning and its relationship to generalization ability.

 

Further syllabus information can be found here.

COMPGI08 Graphical Models

COMPGI08 Graphical Models

The module provides an entry into probabilistic modeling and reasoning, primarily of discrete variable systems. Very little continuous variable calculus is required, and students more familiar with discrete mathematics should find the course digestible. The emphasis is to demonstrate the potential applications of the techniques in plausible real-world scenarios related to information retrieval and analysis. Concrete challenges include questionnaire analysis, low-density parity check error correction, and collaborative filtering of Netflix data.

 

Further syllabus information can be found here.

STATG010 Stochastic Systems

STATG010 Stochastic Systems

Taught by Department of Statistical Science - see here for syllabus

Elective Modules Term 2

COMPGI10 Bioinformatics

COMPGI10 Bioinformatics

The aim of this module is to introduce students to the new field of bioinformatics (computational biology) and how machine learning techniques can be employed in this area. The course is aimed at students who have no previous knowledge of biology and so the aim of Part 1 of the module is to give a basic introduction to molecular biology as a background for bioinformatics. Part 2 will concentrate on modern bioinformatics applications, particularly those which make good use of pattern recognition and machine learning methods.

 

Further syllabus information can be found here.

COMPGI17 Affective Computing and Human-Robot Interaction

COMPGI17 Affective Computing and Human-Robot Interaction

The module targets students who have no previous knowledge in cognitive science and emotion theory. The aim of Part 1 is to give a basic introduction to the theory of emotion from psychology and neuroscience viewpoints and to understand its importance in human decision and communication processes. Part 2 will concentrate on the application of machine learning techniques to emotion recognition by looking at current applications in entertainment, education, and health. Part 3 will focus on the challenges in designing robots that are capable of socially interacting with humans.

 

Further syllabus information can be found here.

COMPGV17 Computational Modelling for Biomedical Imaging

COMPGV17 Computational Modelling for Biomedical Imaging

This module aims to expose students to the challenges and potential of computational modelling in a key application area. It will explain how to use models to learn about the world; how to teach parameter estimation techniques through practical examples; and how to familiarize students with handling real data sets.

 

Further syllabus information can be found here. 

You may choose up to 45 credits as elective modules.

A minimum of an upper second-class Bachelor's degree (or equivalent overseas qualification) in a quantitative discipline (such as mathematics, computer science, engineering, physics or statistics) from a UK university or an overseas, qualification of an equivalent standard. Knowledge of mathematical methods including linear algebra and calculus at first-year university level is required.

Depending on the modules selected, students undertake assignments that contain programming elements and prior experience in a high-level programming language (R/matlab/python) is useful. Relevant professional experience will also be taken into consideration.

 

English Language Requirements

If your education has not been conducted in the English language, you will be expected to demonstrate evidence of an adequate level of English proficiency.

The English language level for this programme is: Good

Further information can be found on our English language requirements page.

 

International students

Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website.

UK/EU fees (FT):  £11,800 for 2017/18

Overseas fees (FT): £24,140 for 2017/18

UK/EU fees (FT):   £12,950 for 2018/19

UK/EU fees (PT):   N/A for 2018/19

Overseas fees (FT): £26,670 for 2018/19

Overseas fees (PT): N/A for 2018/19

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarship and Funding website.

Tuition Fee Deposit

This programme requires that applicants firmly accepting their offer pay a deposit. This allows UCL to effectively plan student numbers, as students are more demonstrably committed towards commencing their studies with us.

For full details about the UCL tuition fee deposit, please see the central UCL pages.

Tuition fee deposits within the Department of Computer Science are currently listed as:

UK/EUOverseas
Full-time*Part-timeFull-time*Part-time
£2000£1000£2000£1000
*where part-time is an available mode of study

The Department's graduates are particularly valued as a result of the our international reputation, strong links with industry, and ideal location close to the City of London.

Data science related graduate destinations:       

  • IBM
  • SAS
  • Dunhumby
  • Microsoft

Data science relates graduate roles:                

  • Data Analyst
  • Big Data Architect
  • SQL Developer
  • Business Analyst

Top further study destinations:

  • UCL
  • University of Cambridge
  • MIT

Average starting salary £34,120 (all data from Graduate Surveys, January 2015)

Data Science professionals will be highly sought after as the integration of statistical and computational analytical tools becomes increasingly essential in all kinds of organisations and enterprises. A solid understanding of fundamentals is to be expected from the best practitioners. For instance, in applications in marketing, the healthcare industry and banking, computational skills should go along with statistical expertise at graduate level. Data scientists will have a broad background so they will be able to adapt themselves to rapidly evolving challenges.

Programme Administrator
Abena Adi
Office 5.22, Malet Place Engineering Building
+44 (0)20 7679 7937
advancedmsc-admissions@cs.ucl.ac.uk

More information

To apply now click here.

This MSc receives many more applications than it has places available and the admissions process is competitive. It may therefore take longer than the Admissions stated 6 weeks for a decision to be made and communicated. Applicants are advised to apply as early as possible due to the competition for places.

Those applying for scholarship funding (particularly overseas applicants) should take note of application deadlines.

Deadline 29th June 2018.