Unit workload

The 'estimated time on task' for each assessment item is an estimate of the additional time needed to complete each assessment outside of all scheduled learning activities. These estimates assume that you actively engage with all scheduled learning activities and spend an additional 12 hours of self-led study over the course of the session. 

Hurdle tasks

The final examination is a hurdle requirement. You must obtain a mark of at least 40% in the final exam to be eligible to pass the unit. If your mark in the final examination is between 30% and 39% inclusive, you may be a given a second and final chance to attain the required level of performance; the mark awarded for the second exam towards your final unit mark will be capped at 40%, and you will be allowed to sit the second exam only if this mark would be sufficient to pass the unit overall.

Late assessment policy

There is now a General Faculty of Science and Engineering Policy for assessment submission deadlines and late submissions:

Online quizzes, in-class activities, scheduled tests and exams must be undertaken at the time indicated in the unit guide. Should these activities be missed due to illness or misadventure, students may apply for Special Consideration.

All other assessments (e.g., lab reports and assignments) must be submitted by 5:00 pm on their due date.

Should these assessments be missed due to illness or misadventure, students should apply for Special Consideration.

Late submission are allowed in this unit, and a consistent penalty will be applied for late submissions as follows:

A 12-hour grace period will be given after which the following deductions will be applied to the awarded assessment mark: 12 to 24 hours late = 10% deduction; for each day thereafter, an additional 10% per day or part thereof will be applied until five days beyond the due date. After this time, a mark of zero (0) will be given. For example, an assessment worth 20% is due 5 pm on 1 January. Student A submits the assessment at 1 pm, 3 January. The assessment received a mark of 15/20. A 20% deduction is then applied to the mark of 15, resulting in the loss of three (3) marks. Student A is then awarded a final mark of 12/20.

Supplementary examinations

If you receive special consideration for the final exam, a supplementary exam will be scheduled after the end of the normal exam period. By making a special consideration application for the final exam you are declaring yourself available for a resit during the supplementary examination period and will not be eligible for a second special consideration approval based on pre-existing commitments. Please ensure you are familiar with the policy prior to submitting an application. Approved applicants will receive an individual notification one week prior to the exam with the exact date and time of their supplementary examination.

If you are given a second opportunity to sit the final examination as a result of failing to meet the minimum mark required, you will be offered that chance during the same supplementary examination period and will be notified of the exact day and time after the publication of final results for the unit.

Your lecturers in this course are Matt Owers and Christian Schwab. Each week, there will be 2 hours of lectures that will be delivered on-campus with echo recordings provided. There will also be a one hour of lectorial-type demonstration delivered on-campus. The 2 hours of lectures will cover astronomy content related to the Milky Way galaxy (Matt Owers) and extra-solar planets (Christian Schwab), while the one hour lectorials will cover techniques used to extract information from datasets (Matt Owers).

The lab sessions run from Week 3-12 on-campus in the Physics and Astronomy computer lab, and will involve a series of computer labs completed using Python Notebooks within the Google Colab environment. Note that labs start in Week 3. Matt Owers and Lee Spitler will be your lab demonstrators.

Resources will be provided on iLearn. There is no required text, although the Milky Way Galaxy component will primarily draw content from the book "Galaxies in the Universe: An Introduction" 2nd Ed. by Sparke and Gallagher, supplemented by material from "An Introduction to Modern Astrophysics" 2nd Ed. by Carroll and Ostlie and "Galactic Astronomy" by Binney and Merrifield. Useful resources for the data science part of the course are the books "Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data" by Ivezic et al. and "Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow" by Geron.

N.B.: This schedule is flexible and subject to change.