The course covers statistical principles and techniques for analysing engineering data, with an emphasis on advanced least-squares estimation techniques for determining the coefficients of mathematical models from experimental data. The level of complexity and sophistication increases as the module progresses. Essential tools such as matrix algebra and calculus will be revised in the early sessions. The course then proceeds to cover basic ideas on the nature of errors, probability distributions and statistical tests, followed by the theory of error propagation and the correlation of errors in space and time. The course then moves on to least-squares estimation, including both linear and non-linear problems, constrained solutions, reliability and quality control procedures, and Kalman filtering. The examples focus mainly on geospatial engineering, but the mathematical techniques we teach are applicable to any branch of science and engineering. Please note that this module does not cover data science, data analytics or machine learning.

Learning Outcomes

Apply statistical tests to experimental data
 Understand the generic concept of least squares
 Generate quality indicators and interpret results
 Apply these techniques to a range of suitable engineering problems with an emphasis on the geospatial sciences

This course provides an overview of the antibody field. The course consists of 8 topics that provide the principles of antibody targeted therapy and underlies...

This course provides an overview of the antibody field. The course consists of 8 topics that provide the principles of antibody targeted therapy and underlies the rationale to develop therapeutic antibodies from basic knowledge of the structure and function, through to genetic and immunological considerations and development and regulatory concerns. In addition, there is a range of animation videos. Assessment is via online multiple choice assessment, as well as a written report based on a case study and literature research. The method of delivery is wholly online – a mixture of interactive and audio seminars, as well as quizzes, and case study documents to review. The student will have access to support from the tutor and other leaners via an online forum.

Upon completion of the course, students will be able to:

assimilate a broad knowledge of the antibody therapeutics area in their role as scientists and engineers
	gain knowledge on how to formulate a strategy to design, develop and apply industry regulations and best practice to ensure quality and success
	familiarize with antibody structure, drug discovery using antibodies, protein expression methods, antibody manufacturing, and applications of antibodies in industry
	understand the regulatory challenges associated with antibody therapeutic development
	understand the impact of protein structure properties on antibody manufacturing, and formulation