Programmes

MSc Data Science

  • Graduate taught
  • Department of Statistics
  • Application code G3U1
  • Starting 2018

The MSc Data Science provides training in data science methods, emphasising statistical perspectives. You will receive a thorough grounding in theory, as well as the technical and practical skills of data science. 

Your theoretical learning will be at a high mathematical level, while the technical and practical skills you will gain will enable you to apply advanced methods of data science and statistics to investigate real world questions.

The compulsory courses on the MSc Data Science programme will provide you with comprehensive coverage of some fundamental aspects of data, computational techniques and statistical analysis. You will then choose courses from a range of options ranging from Distributed Computing for Big Data and Statistical Computing, to Financial Statistics and Probabilistic Methods in Risk Management and Insurance. The programme will combine traditional lectures with computer lab sessions, in which you will work with data to complete hands-on exercises using programming tools. 

The MSc Data Science capstone project provides you with a unique opportunity to apply knowledge gained from the programme by working on a real-world data science project in cooperation with a company. The capstone project company partners in the academic year 2017/18 include some of the leading companies in software industry, marketing, retail and other services. In particular, the company partners include Facebook, Google, Microsoft, Microsoft Research, Virgin Atlantic, SpecSavers, Proximity London and Tesco. The capstone projects cover a wide range of data science problems involving analysis of various types of data such as social media data, customer behaviour data, and company network data. The opportunity to work on capstone projects enables you to gain valuable hands-on experience and interact with industry.   

Programme details

Key facts

MSc Data Science
Start date 27 September 2018
Application deadline None – rolling admissions. However please note the funding deadlines
Duration 12 months full-time only
Applications 2016 New programme for 2017
Intake 2016 New programme for 2017
Availability UK/EU: Open 
Overseas: Open 
Tuition fee UK/EU: £26,976
Overseas £27,504
Financial support Graduate support scheme (deadline 26 April 2018)
Minimum entry requirement 2:1 degree or equivalent in a relevant discipline, including a substantial amount of mathematics
GRE/GMAT requirement None
English language requirements Standard (see 'assessing your application')
Location  Houghton Street, London

For more information about tuition fees and entry requirements, see the fees and funding and assessing your application sections.

Programme structure and courses

You will take five compulsory courses, including a Dissertation/Capstone Project. You will also select optional courses to the value of one full unit from a list of options.

The capstone project/dissertation will provide you with the opportunity to study in depth a topic of specific interest. The topic may be identified from a list supplied by the Department or may be proposed by you. The topic will normally relate to a specific data source or sources and will require the use of data science skills learnt on the programme. The topic for a capstone project will be similar to that for the kinds of data-based issues faced in practice by private or public sector organisations. The focus is likely to be practical and there may be the opportunity to liaise with such an organisation during the project to ensure the project has practical relevance. A dissertation will be more academic; it will refer to a research literature and address a research question, building on that literature and using the data source(s). 

(* denotes a half unit)

Computer Programming*^
Introduces students to the fundamentals of computer programming as students design, write, and debug computer programs using the programming language Python. The course will also cover the foundations of computer languages, algorithms, functions, variables, object-orientation, scoping, and assignment. 

Managing and Visualising Data*
Focuses on data structures and databases, covering methods for storing and structuring data, relational and non-relational databases and query languages. The second part focuses on visualising data, including best practices for visualising univariate, bivariate, graph and other types of data as well as visualising various statistics for predictive analytics and other tasks. 

Data Analysis and Statistical Methods*^
This course will provide an introduction to methods of statistics and data analysis. The statistical software R will constitute an integral part of the course, providing hands-on experience of data analysis. 

Machine Learning and Data Mining*
Begins with the classical statistical methodology of linear regression and then build on this framework to provide an introduction to machine learning and data mining methods from a statistical perspective. 

Dissertation/Capstone Project
An in-depth study on an approved topic of your choice. 

Optional courses to the value of one full unit from an approved list

 ^ students who can demonstrate equivalent prior knowledge of this course, via transcripts of prior qualifications, may skip this course and take a further half unit of options from the options list 


You can find the most up-to-date list of optional courses in the 
Programme Regulations section of the current School Calendar.

You must note however that while care has been taken to ensure that this information is up to date and correct, a change of circumstances since publication may cause the School to change, suspend or withdraw a course or programme of study, or change the fees that apply to it. The School will always notify the affected parties as early as practicably possible and propose any viable and relevant alternative options. Note that that the School will neither be liable for information that after publication becomes inaccurate or irrelevant, nor for changing, suspending or withdrawing a course or programme of study due to events outside of its control, which includes but is not limited to a lack of demand for a course or programme of study, industrial action, fire, flood or other environmental or physical damage to premises.

You must also note that places are limited on some courses and/or subject to specific entry requirements. The School cannot therefore guarantee you a place. Please note that changes to programmes and courses can sometimes occur after you have accepted your offer of a place. These changes are normally made in light of developments in the discipline or path-breaking research, or on the basis of student feedback. Changes can take the form of altered course content, teaching formats or assessment modes. Any such changes are intended to enhance the student learning experience. You should visit the School’s Calendar, or contact the relevant academic department, for information on the availability and/or content of courses and programmes of study. Certain substantive changes will be listed on the updated graduate course and programme information page.

Teaching and assessment

Contact hours and independent study

Within your programme you will take a number of courses, often including half unit courses and full unit courses. In half unit courses, on average, you can expect 20-30 contact hours in total and for full unit courses, on average, you can expect 40-60 contact hours in total. This includes sessions such as lectures, classes, seminars or workshops. Hours vary according to courses and you can view indicative details in the Calendar  within the Teaching section of each course guide.

You are also expected to complete independent study outside of class time. This varies depending on the programme, but requires you to manage the majority of your study time yourself, by engaging in activities such as reading, note-taking, thinking and research.

Teaching methods

LSE is internationally recognised for its teaching and research and therefore employs a rich variety of teaching staff with a range of experience and status. Courses may be taught by individual members of faculty, such as lecturers, senior lecturers, readers, associate professors and professors. Many departments now also employ guest teachers and visiting members of staff, LSE teaching fellows and graduate teaching assistants who are usually doctoral research students and in the majority of cases, teach on undergraduate courses only. You can view indicative details for the teacher responsible for each course in the relevant course guide.

Assessment

All taught courses are required to include formative coursework which is unassessed. It is designed to help prepare you for summative assessment which counts towards the course mark and to the degree award.

The programme will incorporate diverse forms of summative assessment, including some conventional assessment by written examination in summer term, but also a range of other kinds of assessment of varying size, reflecting the fundamentally computational nature of the subject matter.

There will be shorter take-home exams for which an invigilated exam would be unrealistic given the computer applications involved. There will be smaller projects, both individual-based and group-based, which enable practical problem-based learning to take place.

Finally, the capstone project/dissertation will assess your ability to take on large-scale data-based problem solving.

An indication of the formative coursework and summative assessment for each course can be found in the relevant course guide.

Academic support

You will also be assigned an academic adviser who will be available for guidance and advice on academic or personal concerns.

There are many opportunities to extend your learning outside the classroom and complement your academic studies at LSE. LSE LIFE is the School’s centre for academic, personal and professional development. Some of the services on offer include: guidance and hands-on practice of the key skills you will need to do well at LSE: effective reading, academic writing and critical thinking; workshops related to how to adapt to new or difficult situations, including development of skills for leadership, study/work/life balance and preparing for the world of work; and advice and practice on working in study groups and on cross-cultural communication and teamwork.

LSE is committed to enabling all students to achieve their full potential and the School’s Disability and Wellbeing Service provides a free, confidential service to all LSE students and is a first point of contact for all disabled students.

Careers

Data scientists are much in demand across industry, including a variety of Internet online service companies, marketers, banks, investment management, and other financial companies. 

Data scientist positions involve a wide range of responsibilities; such as conducting exploratory data analysis, applying statistical methodologies, deriving business insights from data, partnering with company executives, product and engineering teams to solve problems, identify trends and opportunities, inform, influence, support, and execute product decisions and launches.

Further information on graduate destinations for this programme

Support for your career

Many leading organisations give careers presentations at the School during the year, and LSE Careers has a wide range of resources available to assist students in their job search. Find out more about the support available to students through LSE Careers.

Assessing your application

We welcome applications from all suitable qualified prospective students and want to recruit students with the very best academic merit, potential and motivation, irrespective of their background.

We carefully consider each application on an individual basis, taking into account all the information presented on your application form, including your:

- academic achievement (including predicted and achieved grades)
- personal statement (see the note below)
- two academic references
- CV

See further information on supporting documents

You may also have to provide evidence of your English proficiency, although you do not need to provide this at the time of your application to LSE. See our English language requirements.

When to apply

Applications for this programme are considered on a rolling basis, meaning the programme will close once it becomes full. There is no fixed deadline by which you need to apply, however to be considered for any LSE funding opportunity, you must have submitted your application and all supporting documents by the funding deadline. See the fees and funding section for more details. 

Minimum entry requirements for MSc Data Science

Upper second class honours (2:1) degree or equivalent in a relevant discipline, including a substantial amount of mathematics.

Competition for places at the School is high. This means that even if you meet the minimum entry requirement, this does not guarantee you an offer of admission.

See international entry requirements

Personal statement requirements

Your personal statement should state why you want to do the programme applied for and why you have chosen LSE. Brief details of your academic background and aspirations are also useful. If your background is outside of mathematics or statistics then you should provide further explanation of how your experience is relevant to the programme applied for; as well as further details of your current studies.

Your personal statement should be concise and should not exceed 500 words.

If you are applying for more than one choice in the Department of Statistics, it is recommended that you submit two separate personal statements. If the two programmes for which you are applying are very similar and you would prefer to combine the information in one statement then you may do so; however, please ensure that your statement clearly addresses your motivations for applying for each separate programme.

 

Fees and funding

Every graduate student is charged a fee for their programme.

The fee covers registration and examination fees payable to the School, lectures, classes and individual supervision, lectures given at other colleges under intercollegiate arrangements and, under current arrangements, membership of the Students' Union. It does not cover living costs or travel or fieldwork.

Tuition fees 2018/19 for MSc Data Science

UK/EU students: £26,976
Overseas students: £27,504

Fee status

The amount of tuition fees you will need to pay, and any financial support you are eligible for, will depend on whether you are classified as a home (UK/EU) or overseas student, otherwise known as your fee status. LSE assesses your fee status based on guidelines provided by the Department of Education.

Further information

Fees and funding opportunities 

Fee reductions and rewards

Students who completed undergraduate study at LSE and are beginning taught graduate study at the School are eligible for a fee reduction of around 10 per cent of the fee.

Please refer to the Fees Office website for updates.

Scholarships and other funding

The School recognises that the cost of living in London may be higher than in your home town or country, and we provide over £11.5 million in scholarships each year to gradaute students from the UK, EU and overseas.

This programme is eligible for needs-based awards from LSE, including the Graduate Support SchemeMaster's Awards, and Anniversary Scholarships

Selection for any funding opportunity is based on receipt of an application for a place – including all ancillary documents, before the funding deadline. 

Funding deadline for needs-based awards from LSE: 26 April 2018.

In addition to our needs-based awards, LSE also makes available scholarships for students from specific regions of the world and awards for students studying specific subject areas. 

Check the latest information about scholarship opportunities

Government tuition fee loans and external funding

A postgraduate loan is available from the UK government for eligible students studying for a first master’s programme, to help with fees and living costs. Some other governments and organisations also offer tuition fee loan schemes.

Find out more about tuition fee loans
Find out more about external funding opportunities

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