伦敦政治经济学院应用与数据科学-新东方前途出国

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    伦敦政治经济学院应用与数据科学

    2021-02-07
     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

    For the most up-to-date list of optional courses please visit the relevant School Calendar page.


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