Big Data at Bond

Hi All, I have an opportunity for members to compete and win money on a hackathon.
JOIN UQ AIML AND QUTBANDS FOR OUR FIRST EVER INTER-UNIVERSITY DATATHON: OPEN TO ALL AUSTRALIAN UNIVERSITY STUDENTS!

Teams of 1-3 will collaborate and use their Data Analytics or Machine Learning skills to solve a given problem statement. Teams will then pitch their solutions to a panel of industry professionals who will judge their work, with the winners taking home our awesome prizes.

If this will be your first hackathon, no worries. After the release of the problem statement, UQ AIML and QUT BANDS will be holding several information sessions to get you up to speed on what to do and answer any questions you may have. We will also provide guidance on how to prepare a great pitch and the criteria you will be judged on, so you won’t be on your own!

Students from all universities are welcome to participate!

👇👇UQ AIML x QUT BANDS DATATHON INFORMATION👇👇

The problem statement will be released on Monday September 28. Participants will then have the week to prepare their proposals and prepare to present on Friday October 2. 1st, 2nd, and 3rd place entries as determined by our judges will then take home a prize.

👇👇PRIZE INFORMATION👇👇
Thanks to our amazing sponsors Blackbook.ai, QLD AI Labs, we have some awesome prizes up for grabs.

🥇 1st Prize: $1000 cash + an internship opportunity with QLD AI Labs!!!
🥈 2nd Prize: $750 cash
🥉 3rd Prize: $500 cash
This is an excellent opportunity to test your skills in data analytics and teamwork, and benefit from the experience of industry professionals.

This event is free to participate and open to all, so grab your friends, make your teams, and sign up below!

👇👇EVENT HERE👇👇
https://www.facebook.com/events/326190945380121/

👇👇SIGN UP HERE BY MONDAY 28/09👇👇
https://forms.gle/gdyEzswxuxr5QwC98

Individuals sign-ups are encouraged as well – Individuals can choose to be assigned to a team or compete solo.

UQAIML x QUT BANDS Datathon Sign up to participate in our Datathon. Teams are of 1-3 people. Register as an individual and either be randomly allocated to a team, or work by yourself. If you already have a team, register with the number of people in your team, their names, and your team name.

Potential Project with Assoc Prof of Quant Finance - Rand Low -

Potential Project with Assoc Prof of Quant Finance - Rand Low

During Semester 201 we had a presentation on where to find data sets to train and practise with.

Here is the uploaded pdf for your enjoyment.

Sizle Editor