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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.
● 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 apply hypothesis test to different situations.● 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 co ncept of optimization methods and their applications in engineering.● to be able to solve numerically a range of optimization problems in engineering.
Students must attend one activity from each section.
This course is a lecture and lab -based course. There are 4 streams of labs/tutorials per week, you can view/select these via your timetable. Please note that you will have a single two hour lab per week in section 1 but, in sections 2 and 3, you will have two labs/tutorials of two hours each - see below for details.Section 1 - Computer Programming● The first lecture in week 1 is an introduction to the course – all students should attend .● All the other lectures in section 1 are provided through online videos.● Students are required to attend a two-hour computer lab each week.The aim of the computer lab will be to apply the knowledge that you have obtained from lectures and also with an opportunity to work on problems with the support of a tutor.Section 2 - Statistics (Weeks 7 to 10)● All the lectures in section 2 are provided through online videos.● Students are required to attend two tutorials each week covering different topics.Each tutorial is a two -hour computer lab, in which you will use the programming technique learnt from section 1 to solve practical statistics problems. The first 4 tutorials in the week are with topic I, and the last 4 are with topic II. Both the lecturer and tutors will be in the tutorial.Note: In week 8, there will be only one topic for the last 4 tutorials that week due to the ANZAC day. The tutorial session on 28 April will be an open session for the ones who have additional questions on the statistics lecture or tutorials.Section 3 - Statistics and Optimization (Weeks 11 & 12)● All the lectures in section 3 are provided through online videos.● Students are required to attend 1 computer-based tutorial each week, and only one topic is provided each week.● Students are required to attend 2 lecture room-based tutorials each week covering different topics.
and Andrew Bainbridge-Smith
You cannot pass this course unless you achieve a mark of at least 40% in each of the mid semester 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.A resit test may be held at the start of week 10 for students who do not achieve the 40% pass mark in the mid semester test.Your attendance and work in each tutorial will be graded for assessment. If a student is unable to attend a tutorial of a topic due to personal circumstances beyond their control they should discuss this with the lecturer involved as soon as possible.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 maybe 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.
All course materials will be made available through Learn and the CSSE quiz server.
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 $975.00
International fee $5,500.00
* Fees include New Zealand GST and do not include any programme level discount or additional course related expenses.
For further information see
Civil and Natural Resources Engineering.