STATS 255 : Optimisation and Data-driven Decision Making
2022 Semester One (1223) (15 POINTS)
This course covers three aspects of data-driven optimisation and decision-making:
- linear programming models, including transportation and transshipment models.
- decision trees and classification trees
- ENGSCI 391: Optimisation in Operations Research
- STATS 320: Applied Stochastic Modelling
- STATS 369: Data Science Practice
Capabilities Developed in this Course
|Capability 1:||Disciplinary Knowledge and Practice|
|Capability 2:||Critical Thinking|
|Capability 3:||Solution Seeking|
|Capability 4:||Communication and Engagement|
- Define linear or integer programs, using mathematics (including identifying decision variables, constraints, and the objective function) based on a written problem description. (Capability 1 and 2)
- Implement optimisation models in Excel, and solve them. (Capability 3)
- Interpret solution and sensitivity analysis output from optimisation models. (Capability 2)
- Model a decision problem (given as a paragraph + some tables) using a decision tree, and carry out calculations. (Capability 1 and 2)
- Apply R functions for tidying and visualising data (Capability 1 and 4)
- Apply probability distributions in the context of simulation of a situation, and in modelling inter-arrival times and service times in simulations of queues. (Capability 1)
- Interpret output from simulation and produce confidence intervals around key statistics. (Capability 2)
- Apply classification tree and random forest methods to model classification problems. (Capability 1 and 3)
|Assessment Type||Learning Outcome Addressed|
- Linear Programming (Weeks 1-4) - covers the formulation of linear programs, both algebraically and in Excel, and interpretation of the solutions. Integer programming models, transportation and transshipment problems are considered.
- Decision Making and Data Analytics (Weeks 5-8) - covers decision trees, data manipulation and visualisation, and classification trees, random forests and naive Bayes for classifying data.
- Simulation (Weeks 9-12) - covers modelling random processes in R, random number generation, with applications to queues, inventory management, revenue management and optimisation.
Some lectures and tutorials will require students to bring a laptop or tablet running RStudio or RStudio.cloud.
This course is a standard 15 point course and students are expected to spend 12.5 hours per week involved in each 15 point course that they are enrolled in.
For this course, you can expect 3 hours of lectures, a 1-hour tutorial, 4 hours of reviewing the course content and doing tutorial problems, and 4.5 hours of working on assignments, test preparation, each week.
Lectures and tutorials will also be available as recordings.
The activities for the course are scheduled as a standard weekly timetable.
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.
A coursebook is available to purchase from the Science Student Resource Centre.
During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.
At the end of the course 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.
Your feedback helps to improve the course and its delivery for all students.
The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework 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. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.
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.
The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.
You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.
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
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.
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 course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.
The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.
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.
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 students may be asked to submit 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. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.