新香港六合彩开奖结果

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新香港六合彩开奖结果Module Catalogue

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Statistical Learning for Public Policy (ECON0127)

Key information

Faculty
Faculty of Social and Historical Sciences
Teaching department
Economics
Credit value
30
Restrictions
Available only to students on the 新香港六合彩开奖结果MSc Data Science and Public Policy programme.
Timetable

Alternative credit options

There are no alternative credit options available for this module.

Description

This module introduces students to a wide range of statistical methods in data science from classical statistical methods to recent advances in machine learning. The module has a strong practical focus and avoids mathematical arguments where possible. Instead, it will focus on developing a heuristic understanding of the methods and on learning how to apply them to real world data.

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Module deliveries for 2024/25 academic year

Intended teaching term: Terms 1 and 2 听听听 Postgraduate (FHEQ Level 7)

Teaching and assessment

Mode of study
In person
Methods of assessment
50% Fixed-time remote activity
50% Coursework
Mark scheme
Numeric Marks

Other information

Number of students on module in previous year
51
Module leader
Dr Moritz Marbach
Who to contact for more information
economics.dspp@ucl.ac.uk

Last updated

This module description was last updated on 8th April 2024.

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Useful links

Sustainability

For a list of modules related to climate change as well as social and environmental sustainability at UCL, type 'climate' or 'sustainability' into the search. Visit Sustainable UCL for information on extra-curricular activity on sustainability.

Important information

The catalogue has been updated with key information about the modules that will run during the 2024/25 academic session. Current and prospective students can browse through the catalogue to consider possible module choices for the coming year.

Please note,听information in the catalogue is听subject to change as teaching and assessment arrangements for 2024/25 may need to be adjusted in line with the University's Feedback and Assessment principles and operating model.

Centrally managed exam durations will be provided on students' individual exam timetables. Arrangements for locally managed exams and in-class activity will be confirmed by the teaching department for the module.

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