Program Overview

This interdisciplinary programme will provide you with training in fundamental aspects of applied data science, computation and programming, and quantitative methods.

 

With the rise of new and big forms of data, and computation and analytics forming ever-increasingly important elements of a wide range of professions, this programme will prepare you for a variety of careers in the private, non-profit and public sectors.

 

With a background in social sciences, you will be trained to use data to answer interesting social science questions. You will take a series of project-based programming courses specifically designed for students without a formal computing or statistical background. A typical student will also have taken a prior course in quantitative methods or applied statistics at a basic level, although this is not a formal requirement. 

 

You will become fluent in a variety of programming languages and applications, particularly R and Python, and will learn to create and manipulate large databases and think creatively about how to deploy these skills in the context of specific projects.

 

You will also have the opportunity to choose substantive electives, allowing you to tailor the programme to your particular interests. The programme will culminate in a Capstone project where you will creatively apply the technical skills you have learned to a project of your own design.

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  Location

LondonUnited Kingdom

DurationIcon
  Course Duration

12 Months

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  Tuition Fee

£ 32,208

 Score

IELTS: 7 TOEFL: 100

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You will take core courses which provide training in fundamental aspects of applied data science, computation and programming, and quantitative methods. These courses together provide the foundations for the topics covered in the optional courses.

 

You will also have the opportunity to choose substantive electives, from a range of options both within the Department and across the School, allowing you to tailor the programme to your particular interests. You can choose from courses on social network analysis, quantitative text analysis, causal inference, distributed computing, deep learning, and many others. The programme will culminate in a capstone project, where you will creatively apply the technical skills you have learned to a project of your own design.

 

(*denotes 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.

 

Fundamentals of Social Science Research Design* 
Provides a basic knowledge of social research design.

Either


Data for Data Scientists*
Covers the principles of digital methods for storing and structuring data, including data types, relational and non-relational database design, and query languages. 


Or


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. 

 

One from:


Applied Regression Analysis*
Concerned with deepening the understanding of the generalized linear model and its application to social science data.

 

Applied Machine Learning for Social Science*
Uses prominent examples from social science research to cover major machine learning tasks including regression, classification, clustering, and dimensionality reduction. Students will learn to apply the algorithms to social data and to validate and evaluate different models.

 

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

 

Capstone Project
An independent research project of 10,000 words on an approved topic of your choice.

 

Courses to the value of one unit from a range of options

Minimum entry requirements for MSc Applied Social Data Science

Upper second class honours (2:1) degree or equivalent in social science, data science, statistics or a quantitative field. Work experience is advantageous but not required.
 

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

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 2022/23 for MSc Applied Social Data Science:


Home students: £31,584
Overseas students: £32,208

Quick Careers Facts for the Department of Methodology


Top 5 sectors our students work in:

  • Education
  • Advertising and market research
  • Computer programming and consultancy
  • Membership organisations

The data was collected as part of the Graduate Outcomes survey, which is administered by the Higher Education Statistics Agency (HESA). Graduates from 2017-18 were the first group to be asked to respond to Graduate Outcomes. Median salaries are calculated for respondents who are paid in UK pounds sterling.

 

This programme will prepare you for a variety of careers in the private, non-profit and public sectors.

Confused?

We can help you

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