Data Services Engineering COMP9321 26T1

Assignment3 Late penalty waiver and MyExperience participation

Posted by Morty Al-Banna Thursday 16 April 2026, 04:21:24 PM.

Dear All,

First, I would like to thank all the students who have participated in the MyExperience survey. In order to motivate the remaining students to participate we decided to tie the level of participation to waiving the late penalty for assignment3.

As we have discussed in the lecture, here is the waiver scheme based on the level of participation in the MyExperience survey:

• If we reach 40% participation, we will waive the late penalty for all the students being 1 day late.
• If we reach 50% participation, we will waive the late penalty for all the students being 2 days late.
• If we reach 60% participation, we will waive the late penalty for all the students being 3 days late.
• If we reach 70% participation, we will waive the late penalty for all the students being 4 days late.
• If we reach 85% participation, we will waive the late penalty for all the students being 5 days late.

Pay attention that we are NOT extending the deadline . No Matter which condition is met, if you submit your answers after 5 days of the original deadline (without approved extension) your submission will NOT be marked. The late penalty is still going to progress based on the days after the deadline (i.e., if 3 days waiver is approved and you submit on day4, then you will get 20% penalty)

We will make an announcement to share the participation level when a millstone is reached, so you will have an idea of what condition is met. This is an interesting case of how the community can come together to support each other. So even if you do not need the additional time, you might think of participating to help your fellow classmates. (Yes, we are being cheeky here 😊). Remember to share with us what you liked, and what you want us to improve. We really appreciate all feedback.

If you have any question or if anything is not clear, please feel free to contact us.

Best of Luck,

COMP9321 Teaching Team

Welcome to Week9

Posted by Morty Al-Banna Monday 13 April 2026, 12:15:06 PM.

Dear All,

Welcome to Week9 of COMP9321. You are so close to the finish line.

For this Week, we are going to continue with Data Analytics. We are going to continue with Clustering and look at other Machine Learning Models.

The Lab activities for Weeks 9 and 10 are now available. We encourage you to do them at your own convenience. As always, we have consultation labs for everyday of the week. Check the timetable section for the times.

If you are debating the marks awarded for assignments, please remember to reach out to the tutor who marked your submission (it is mentioned in the feedback). If after the discussion, you are still unsatisfied and you want to escalate, please copy the Course email in the email thread you have discussed the submission with the tutor.

Regarding Assignment3, we hope that you are making good progress. As mentioned in the lecture, as a general rule, for the ML problems an iterative approach is better be considered. Question everything, your selection of features, your selection of ML Model, and your initialization of any parameters. The Assignment is on the scale of a small individual project, so remember to treat it as such.

The Official due date is the 21 ^st of April. Additionally, we are going to add some flexibility by waiving late penalty depending on the MyExprience participation (you should have received the invite to participate already). We are going to discuss this scheme during the lecture today and follow-up with an announcement to keep everything crystal clear.

For Bonus marks if you have shared some community engagement during the course and tagged your post in the form with #Bonus Marks, then the post will reviewed, and you will get an email after we calculate the (assignments overall mark) of what is the bonus awarded. Supporting your classmates by answering their questions will be rewarded as well based on the report we can extract from Ed, additionally there are the DataCamp Activities.

The COMP9321 teaching team is always rooting for you and here to help. Do not hesitate to use the forum to ask as many questions or contact us via the course email.

As always, remember to have fun and enjoy the learning experience. :)

Cheers,

COMP9321 Teaching Team

Welcome to Week8

Posted by Morty Al-Banna Tuesday 07 April 2026, 11:11:34 AM.

Dear All,

Welcome to Week8 of COMP9321. I hope that you've had some time to enjoy the Easter Break and look after yourself.

For this Week, we are going to continue with Data Analytics. We are going to Clustering and more example models. We will run some exercises of how we can build some ML models using Scikit-learn. Since Monday was a public holiday and in order to keep up with the Material coverage, so I have created a recording covering the Classification and Regression. I have also included some in class exercises about the topics. Please check it out in the recording folder or through the link here ( COMP9321 Week8 Monday replacement recording-20260407_000630-Meeting Recording.mp4 ). Feel free to drop any questions you have about the lectures in ED forum (tag with Lecture) or reach out to me directly if you prefer.

The Lab activities for Week8 are now available. We encourage you to do them at your own convenience. As always, we have consultation labs for everyday of the week. Check the timetable section for the times.

In the beginning of Thursday lecture, we are going to acknowledge the learning effort done in DataCamp, and award 1 bonus mark for the top learner on the XP ranking leader board in the past month. So, keep up the good work. Also, if you have not checked out my last announcement about the challenge, please feel free to do so.

Regarding Assignment3, the specification will be visible today in ED for your viewing. We are going to do a walkthrough on Thursday to make sure we answer any question. Jump right through and start working on assighmnmnent3. As we mentioned in the lecture, working with ML is more iterative to work toward improving your model and achieving better results.

The COMP9321 teaching team is always rooting for you and here to help. Do not hesitate to use the forum to ask as many questions or contact us via the course email.

As always, remember to have fun and enjoy the learning experience. :)

Cheers,

COMP9321 Teaching Team