Prospective Students

MSc Business Analytics provides an exciting and challenging study of how data can be utilised to solve major business and societal challenges. The programme provides students with the knowledge, technical ability and skills for leadership roles in the fields of business analytics and data science.

The programme is designed to give students multi-disciplinary skills in computing, analytics, data science and business analysis. Emphasis will be on business problem framing, leveraging data as a strategic asset, and communicating complex analytical results to stakeholders.

This programme is for the ambitious, future pioneers in industry. Only apply if you want to change the world.

The MSc Business Analytics consists of 8 taught modules and a Dissertation. Of the taught modules, 3 are core modules, with either 5 option modules, or 4 option modules and 1 elective module.

Core Modules Term 1

COMPGC27 Programming for Business Analytics

COMPGC27 Programming for Business Analytics

Computational models and concepts for business analytics need to be implemented with tools and technologies. The course will cover computational thinking, experimental methodology, preparing datasets and empirical methods for training, validation and testing models. Lab sessions will provide a general introduction to programming in Python and look at standard implementation of useful algorithms. Introductory use Matlab, R and Strata will also be covered.

 

Further syllabus information can be found here.

MSING051 Business Strategy and Analytics

MSING051 Business Strategy and Analytics

Taught by School of Management - see here for syllabus.

Core Modules Term 2

COMPG011 Data Analytics

COMPG011 Data Analytics

The course is aimed at introducing students to data analytics and to some basic data-science tools. Students will use statistical tools to individuate regularities, discover patterns and laws in complex datasets and to analyse, characterize, validate, parameterize and model complex data. Practical issues regarding business data analysis and statistics will be covered by specific case studies provided by UCL’s industrial partners.

 

Further syllabus information can be found here.

Core Modules Project

COMPGB99 Project in Business Analytics

COMPGB99 Project in Business Analytics

During the summer students usually undertake a placement with a UCL industrial partner. The research and data analysis conducted during this placement will form the basis of a 10,000-word dissertation.

Optional 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.

MSING002 Mastering Entrepreneurship

MSING002 Mastering Entrepreneurship

Taught by School of Management - see here for syllabus.

PSYCGB01 Consulting Psychology

PSYCGB01 Consulting Psychology

Taught by Department of Psychology and Language Studies – see here for syllabus (structure).

PSYCGB04 Consumer Behaviour

PSYCGB04 Consumer Behaviour

Taught by Department of Psychology and Language Studies – see here for syllabus (structure).

STATG002 Statistical Design of Investigations

STATG002 Statistical Design of Investigations

Taught by Statistics - see here for syllabus.

Optional Modules Term 2

BENVGSA1 Group Mini Project: Digital Visualisation

BENVGSA1 Group Mini Project: Digital Visualisation

Taught by the Centre for Advanced Spatial Analysis - see here for syllabus.

BENVGSA6 Data Science for Spatial Systems

BENVGSA6 Data Science for Spatial Systems

Taught by the Centre for Advanced Spatial Analysis - see here for syllabus.

 

BENVGSC5 Urban Simulation

BENVGSC5 Urban Simulation

Taught by the Centre for Advanced Spatial Analysis - see here for syllabus.

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.

MSING014 Decision and Risk Analysis

MSING014 Decision and Risk Analysis

Taught by School of Management - see here for syllabus.

PSYCGB02 Talent Management

PSYCGB02 Talent Management

Taught by Department of Psychology and Language Studies – see here for syllabus (structure).

STATG009 Decision and Risk

STATG009 Decision and Risk

Taught by Statistics - see here for syllabus.

You will need to choose a minimum of 60 and a maximum of 75 credits from the optional modules.

Elective Modules Term 1

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.

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.

COMPGL01 Introduction to Logic, Semantics and Verification

COMPGL01 Introduction to Logic, Semantics and Verification

The module aims to familiarize students with formal methods for reasoning about transition systems and programs.

 

 

Further syllabus information can be found here.

COMPGL02 Modal Logic and Transition Systems

COMPGL02 Modal Logic and Transition Systems

This course introduces various formal methods of reasoning about transition systems. The focus is on modal logic, an extension of classical logic with operators, which serves as a specification language for system properties and their verification.

 

Further syllabus information is available here.

COMPGX01 Robotic Systems Engineering

COMPGX01 Robotic Systems Engineering

Students will gain an introductory overview of robotics and autonomous systems. Technically they will gain an understanding of the concepts and principles of ROS, the underpinning software development environment for robot systems, through a number of example applications, leading to the capability of using ROS for advanced robot control, navigation, sensing and verification.

 

Further syllabus information can be found here.

COMPGX02 Robotic Control Theory and Systems

COMPGX02 Robotic Control Theory and Systems

The aim of this module is to give students an insight into robotics and autonomous systems control theory and practice, specifically:

  • Control loops. damping, feedback and stability analysis with a working understanding about how these are used for navigating a robot within an environment;
  • Insight into developing a working prototype of a control system for a robot that solves a specific task.

Further syllabus information can be found here. 

COMPGX03 Robotic Sensing, Manipulation and Interaction

COMPGX03 Robotic Sensing, Manipulation and Interaction

The aim of this module is to make sure students are familiar with various concepts in robotic sensing and manipulation and to give them a working knowledge of haptic interfaces and haptic control. These concepts will be used to teach students the principles and practical implementation of a tele-manipulation system involving a user interface, end-effector and a haptic or visual display unit.

 

Further syllabus information can be found here.

Elective 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.

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.

COMPGL03 Program Verification and Automated Reasoning

COMPGL03 Program Verification and Automated Reasoning

The module aims to familiarize students with the concepts and techniques currently used by state of the art automated theorem provers and program analysers.

 

Further syllabus information is available here.

COMPGX04 Robotic Vision and Navigation

COMPGX04 Robotic Vision and Navigation

Students will gain knowledge about robot navigation with specific focus on the use of vision as a primary sensor for mapping the environment. The module will provide students with an understanding and practical experience of recovering geometry from optical sensors and creating an environment map which a robot can use for navigation and motion planning.

 

Further syllabus information can be found here. 

Up to 15 credits as an elective.

Module Selection

The modules that make up a programme are either core, optional or elective, which reflects whether they must be taken or can optionally be taken. The programme’s curriculum (also called a programme diet) will prescribe in what combinations modules can be taken, any restrictions on doing so, and how much credit can and must be taken.

Core/compulsory modules are fundamental to the programme’s curriculum and students must take these. You will be automatically allocated a place on any core modules for your programme and will not need to select these during the module selection process. There will be no timetable clashes between your programme’s core modules.

Optional modules are strongly related to the programme and students can choose which of these they wish to take, usually from within specific groups (for example, a student may be asked to choose two optional modules from one group and three from another, etc.) Places of optional modules are strictly limited (due to spatial, resource and timetable constraints) and will be allocated on a first come first serve basis. Some optional modules have pre-requisites which students will need to meet in order to be eligible for a place.

Elective modules are not programme specific, but allow students the opportunity to explore their interests more widely. Students are usually restricted to taking one or two elective modules. There is no guarantee of being accepted onto an elective module. These modules are core or optional on other programme diets, consequently students on these programmes will be given priority. Any remaining places will then be allocated on a first come first served basis. Some elective modules have pre-requisites which students will need to meet in order to be eligible for a place.

Please note: timetable clashes between optional and elective modules from different specialisations are inevitable and this can result in limiting the available choices. It is the student’s responsibility to select modules that do not clash in order to meet UCLs minimum attendance requirements. Please speak to your Programme Director and/or Programme Administrator if you have any queries.

Non-Computer Science students should note that priority on COMP* modules will always be given to Computer Science students in the first instance.

A minimum of an upper second-class UK Bachelor's degree (or overseas equivalent) in a quantitative subject as computer science, engineering, mathematics, physics or a quantitative social science subject.

 

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):     £14,910 for 2017/18

Overseas fees (FT): £24,610 for 2017/18

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

This programme is designed to satisfy the need, both nationally and internationally, for exceptional data scientists and analysts. Graduates from this new programme will be highly employable and they will be equipped to influence strategy and decision-making, and be able to drive business performance by transforming data into a powerful and predictive strategic asset. We expect our graduates to progress to leading and influential positions in industry.

 

Graduate Destinations

Graduates from the Department of Computer Science are particularly valued as a result of the department’s international status, and strong reputation for leading research. Recent graduate destinations include:

 

  • IBM
  • Samsung
  • Microsoft
  • PwC
  • Citibank

Careeer Support

Our dedicated team works directly with students to provide tailored individual career support, facilitate connections with employers globally, and enhance employability. Additional support is provided by the Careers Service who run training sessions, workshops, networking events and careers fairs.

 

Entrepreneurship

UCL Advances aims to promote interaction among researchers, businesses, industry, investors and students. They support our entrepreneurially minded students by providing training, business support services and funding.

Programme Administrator
Samantha Bottomley
Office 5.22, Malet Place Engineering Building 
020 7679 0328
advancedmsc-admissions@cs.ucl.ac.uk
More information

 

Programme Director: Dr Daniel Hulme

To apply now click here.

Students are advised to apply as early as possible due to competition for places. Those applying for scholarship funding (particularly overseas applicants) should take note of application deadlines.

Deadline 31st May 2017.