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Computer programming. Descriptive statistics. Monte Carlo and Bootstrapping methods. Design of experiments. Linear regression and generalized linear modelling. Optimization and linear programming.
The specific aims of the course are:- to improve students’ general programming skills, so they can develop solutions to engineering problems in Python and, potentially, in other programming languages.- to understand hypothesis testing and be able to design experiments to verify hypotheses.- to introduce Monte Carlo and bootstrapping methods for uncertainty analysis.- to introduce analytical approaches to examine dependence between different quantities for which observational data is available and use them for statistical inferences.- to understand basic concept of optimization methods and their applications in engineering.- to be able to solve numerically a range of optimization problems in engineering.
EMTH210
For further information see Civil and Natural Resources Engineering Head of Department
The assessment for this paper has four components – learn quizzes in the computer programming labs, assignments, a mid-semester test and the final exam. All of the material covered in the first component will be assessed in the mid-semester test. The second and third components will be tested in the final exam.Notes:(1) You cannot pass this course unless you achieve a mark of at least 40% in each of the midsemester test and the final exam. A student who narrowly fails to achieve 40% in either the test or exam, but who performs very well in the other, may be eligible for a pass in the course.(2) A resit exam may be held at the start of week 10 for students who do not achieve the 40% pass mark in the mid-semester test.(3) All assignments must be submitted by the due date. Late submissions will not be accepted. If a student is unable to complete and submit an assignment by the deadline due to personal circumstances beyond their control as possible.(4) Students may apply for special consideration if their performance in an assessment is affected by extenuating circumstances beyond their control, provided they have sat either the final exam, mid-semester test or both. Applications for special consideration should be submitted via the Examinations Office website http://www.canterbury.ac.nz/exams/ within 5 days of the assessment. Where an extension may be granted for an assessment, this will be decided by the course co-ordinator and an application to the Examinations Office may not be required.Special consideration is not available for items worth less than 10% of the course.Students prevented by extenuating circumstances from completing the course after the final date for withdrawing may apply for special consideration for late discontinuation of the course. Applications must be submitted to the Examinations Office within five days of the main examination period for the semester.(5) Assignment work must be done individually.
Electronic copies of all course materials will be made available through Learn.
Programming, Statistics and Optimization is a compulsory 15 point course taught in the first semester of second professional to all civil and natural resources engineering students. It builds directly on the numerical methods and programming in EMTH171 and on probability and statistical material taught in EMTH210 and EMTH118/119.The course is split into three broad components, each of which is comprised of a number of subtopics. The first component covers programming, in particular algorithm design, procedural and data abstraction, testing and debugging. These skills will be taught in a Python context but they are equally applicable to most other programming languages. The first component provides the necessary tools for solving problems that arise in the other components. The second component starts with descriptive statistics and hypothesis testing. It is followed by design of experiments, where methods such as block design and full factorial design will be covered. The Monte Carlo and bootstrapping methods will then be introduced for uncertainty analysis. You will also learn linear regression, Poisson regression, logistic regression and spatial modeling to discover knowledge from data and to make statistical inferences. The third component involves some basic principles of optimization methods and theirapplications in engineering. You will learn skills such as linear programming to solve a range of optimization problems in engineering.The concepts and techniques developed in this course will appear in a number of third professional courses, in particular all those that require the data manipulation or statistical characterization of experimental or observational data, and the use of optimization methods.In all components of the course the emphasis is on the application of the programming skills, dataanalytics and optimization methods to civil and natural resources engineering, and transportationengineering in particular.
Domestic fee $956.00
International fee $5,250.00
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see Civil and Natural Resources Engineering .