Computational Pharmaceutics (PHAY0076)

Key information

Faculty: Faculty of Life Sciences
Teaching department: School of Pharmacy
Credit value: 15

Restrictions: Available for Msc Pharmaceutics, MSc Pharmaceutical Formulation and Entrepreneurship students only.
Timetable

Alternative credit options

There are no alternative credit options available for this module.

Description

This module will provide an introduction to statistical analysis and machine learning for pharmaceutics research. Students will learn the context and role of computational analysis, and will get hands on experience in interpreting data, performing analysis in python, building predictive machine learning models for pharmaceutics, and plotting and presenting data in compelling and informative ways.

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The emphasis of this course will be on students getting hands on experience with using python for implementing statistical packages, performing analysis and modelling of data, and designing and generating figures. No background in programming is required for this module 鈥� an introduction to python will be provided at the start of the module.

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Topics include:

Introduction to data analysis with python
Statistical analysis in pharmaceutics research
Introduction to machine learning with python
Machine learning methods in pharmaceutics
Incorporating machine learning into experimental workflows
Plotting and displaying data

Module deliveries for 2024/25 academic year

Intended teaching term: Term 2 听听听 Postgraduate (FHEQ Level 7)

Teaching and assessment

Mode of study: In person
Methods of assessment: 50% Coursework

50% Exam
Mark scheme: Numeric Marks

Other information

Number of students on module in previous year: 0
Module leader: Dr David Shorthouse
Who to contact for more information: sop.pgt@ucl.ac.uk

Last updated

This module description was last updated on 19th August 2024.

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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 timed assessments and in-class activity will be confirmed by the teaching department for the module.

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