This can be updated at any time via your application. We'll send you an email, so it is important that we have your correct email address. Offers are released at 7.30am on each offer round date. If you apply close to or on this date, you may not have seven days to provide documents to support your application. * This is the final closing date to apply for courses starting semester 1, 2022. Offer roundĪll times indicated are based on Sydney time.
Note that you are unable to change your preferences in your application between each offer round's deadline for changing preferences and the release of offers (eg from midnight Thu to 7.30am Thu ). You can't change your preferences by phone, letter or email. (Some courses have early application closing dates so check for these as well.) Simply log in to your application using your UAC application number and UAC PIN. Once you've applied, you can change the order of your preferences – or even the courses you’ve selected – as often as you like before the closing dates for each offer round.
Provide details of your studies and qualifications (including completed secondary studies and complete or incomplete certificate, diploma and degree studies).The name on your application must match the name on your official ID, otherwise you’ll have problems when you enrol. Enter your personal details (including a personal email address - don't use a school address as it will expire).All other Year 12 applicants: Your UAC PIN is the first four digits of your date of birth – eg if your birthday is 3 October, your UAC PIN is 0310.) ( 2021 NSW HSC applicants and ACT Year 12 applicants: Your UAC PIN will be emailed to the address you registered with NESA. If you’re a 2021 Year 12 student, enter your Year 12 student number (issued through your school) and UAC PIN.The ability to explain how data science can be applied in real-world contexts to provide innovative solutions.The ability to describe machine learning and advances in machine learning.The ability to describe how data can be used to inform decision making in real-world contexts.This course will provide an opportunity for participants to engage with the following learning outcomes: This eliminates the need for domain expertise and hard-core feature extraction. Importantly, for rail managers and controllers, end-to-end timetable evaluation and delay prediction is automatically achieved by data-driven techniques. This work has demonstrated to train operating companies that they can produce highly detailed and granular information to develop targeted timetable design and real-time scheduling strategies. The proposed generic model can be easily applied to other traffic scenes, with subtle refinements. Based on the statistics, improving the on-time running rate by 1% could potentially save customer ‘lost-minute’ value by $5 million. The model will be able to assess timetables and response plans to ensure that they are operationally robust and resilient.
This enables the train operating system to meet performance metrics and pursue timely recovery from incidents. The outcome of this innovative application of the intelligent timetable evaluation technology significantly reduces delay-caused losses and increases operation efficiency.
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