LSE courses on GitHub
LSE introduced the free online repository and version control service GitHub to share course materials, submit summative assessment, and provide feedback for most of the computational methods courses at the Department of Methodology.
MY470 Computer Programming
This course introduces students to the fundamentals of computer programming as students design, write, and debug computer programs using the programming language Python and R. The course will also cover the foundations of computer languages, algorithms, functions, variables, object-orientation, scoping, and assignment. The course will rely on practical examples from computational social science and social data science.
ST445 Managing and Visualizing Data
The focus of the course is on the fundamental principles and best practices for data manipulation and visualisation. The course is based on using Python as the primary programming language and various software packages.
MY472 Data for Data Scientists
This course will cover the principles of digital methods for storing and structuring data, including data types, relational and nonrelational database design, and query languages. Students will learn to build, populate, manipulate and query databases based on datasets relevant to their fields of interest. The course will also cover workflow management for typical data transformation and cleaning projects, frequently the starting point and most timeconsuming part of any data science project. This course uses a project-based learning approach towards the study of online publishing and group-based collaboration, essential ingredients of modern data science projects. The coverage of data sharing will include key skills in on-line publishing, including the elements of web design, the technical elements of web technologies and web programming, as well as the use of revision-control and group collaboration tools such as GitHub. Each student will build one or more interactive website based on content relevant to his/her domain-related interests, and will use GitHub for accessing and submitting course materials and assignments.
MY459 Quantitative Text Analysis
The course surveys methods for systematically extracting quantitative information from text for social scientific purposes, starting with classical content analysis and dictionary-based methods, to classification methods, and state-of-the-art scaling methods and topic models for estimating quantities from text using statistical techniques. The course lays a theoretical foundation for text analysis but mainly takes a very practical and applied approach, so that students learn how to apply these methods in actual research. The common focus across all methods is that they can be reduced to a three-step process: first, identifying texts and units of texts for analysis; second, extracting from the texts quantitatively measured features - such as coded content categories, word counts, word types, dictionary counts, or parts of speech - and converting these into a quantitative matrix; and third, using quantitative or statistical methods to analyse this matrix in order to generate inferences about the texts or their authors. The course systematically surveys these methods in a logical progression, with a practical, hands-on approach where each technique will be applied using appropriate software to real texts.
ME314 Introduction to Data Science and Machine Learning
This course integrates prior training in quantitative methods (statistics) and coding with substantive expertise and introduces the fundamental concepts and techniques of Data Science and Big Data Analytics.