ENGSCI 331 : Computational Techniques 2

Engineering

2025 Semester Two (1255) (15 POINTS)

Course Prescription

Methods for computing numerical solutions of mathematical models and data analytics problems with focus on translating algorithms to computer code. A selection of topics from numerical solution of linear and non-linear equations, eigen problems, ordinary and partial differential equations, databases, inverse problems and parameter estimation.

Course Overview

This course focuses on computational programming of numerical algorithms to help solve common mathematical modelling and data analytics problems. Programming languages are Python and SQL. Python is the primary language with a focus on improving student familiarity with procedural, functional and object-oriented programming approaches. Students will complete a number of labs throughout the course that require effective implementation of the concepts and algorithms taught in lectures. The topics taught include: eigenproblems, principal component analysis, finite difference methods for solving partial differential equations, Runge-Kutta methods for solving ordinary differential equations, databases, nonlinear equations and univariate minimisation.

Course Requirements

Prerequisite: ENGSCI 233 Corequisite: ENGSCI 311 or 313 or 314

Capabilities Developed in this Course

Capability 3: Knowledge and Practice
Capability 4: Critical Thinking
Capability 6: Communication

Learning Outcomes

By the end of this course, students will be able to:
  1. Understand the fundamental concepts for modelling databases (Capability 3.1, 3.2 and 6.1)
  2. Apply the ability to query databases using SQL (Capability 3.1, 3.2, 4.1 and 4.2)
  3. Unterstand the formulation and solution of systems of non-linear equations (Capability 3.1, 3.2, 4.1 and 4.2)
  4. Appy finite difference representations of partial differential equation problems (Capability 3.1, 3.2, 4.1 and 4.2)
  5. Create adaptive Runge-Kutta methods for numerical solution of ordinary differential equations (Capability 3.1, 3.2, 4.1 and 4.2)
  6. Understand and apply numerical methods for univariate minimisation (Capability 3.1, 3.2, 4.1 and 4.2)
  7. Understand and apply algorithms for finding the eigenvalues and eigenvectors of a system (Capability 3.1, 3.2, 4.1, 4.2 and 6.1)

Assessments

Assessment Type Percentage Classification
Laboratories 50% Individual Coursework
Test 10% Individual Test
Final Exam 40% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7
Laboratories
Test
Final Exam

  • Students must sit the exam to pass the course. Otherwise, a DNS (did not sit) result will be returned.
  • A passing mark is 50% or higher, according to University policy.
  • Assignments are penalised at 4% of the total mark for each hour.


Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For each week in this course, you can expect 3 hours of lectures, 2 hours of reading and thinking about the content, and 5 hours of work on labs and/or test preparation.

Delivery Mode

Campus Experience

Attendance is expected at scheduled activities, including labs, to complete components of the course.
Lectures will be available as recordings. Other learning activities, including labs, will not be available as recordings.
The course will not include live online events.
Attendance on campus is required for the test.
The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

There is no required course book or text book. Course materials will be made available electronically on Canvas.

Health & Safety

Students must ensure they are familiar with their Health and Safety responsibilities, as described in the university's Health and Safety policy.

Student Feedback

At the end of every semester students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback and respond with summaries and actions.

Your feedback helps teachers to improve the course and its delivery for future students.

Class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.

Based on the 2024 SET evaluation, we will continue designing the laboratories for reinforcing the course content through hands-on  implementations of real-world problems.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework, tests and examinations as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. A student's assessed work may be reviewed against electronic source material using computerised detection mechanisms. Upon reasonable request, students may be required to provide an electronic version of their work for computerised review.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website http://disability.auckland.ac.nz

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.

Disclaimer

Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.