What we're about

This is a group for anyone interested in 'Data Science'. We are not quite sure what the exact definition of a Data Scientist is, but if you deal with something generally related to converting data into useful insight then you will hopefully benefit from joining the group.

Whether you’re in business, academia, or government, and whether you’re an analyst, data miner, programmer, student, electrical engineer, computer scientist, physicist, etc, and you work with data to generate insights, build predictive models, build optimisation models, build reports/dashboards/visualisations, automate analyses, etc, using python, R, SQL, C/C+, Java, Tableau, Excel, Hadoop, etc, and you care about doing it right, efficiently, repetitively, optimally, visually, etc, then join us!

We meet every 6 weeks or so with normally 2 talks, one being of a technical nature. The evening is also for networking over beer and pizza and afterwards we normally continue the discussions down the pub.

*due to demand, we now have morning and lunchtime meetups.

The size of the group means we are reliant on our sponsors to put our events on. Please support them in return:

James Cook University - online Master of Data Science
https://online.jcu.edu.au/online-courses/master-data-science

La Trobe University - Master of Data Science
https://www.latrobe.edu.au/courses/master-of-data-science

University of Melbourne
Bachelor of Science majoring in Data Science
https://study.unimelb.edu.au/find/courses/major/data-science/

Master of Data Science
https://study.unimelb.edu.au/find/courses/graduate/master-of-data-science/

Monash University - Master of Data Science
https://www.monash.edu/study/courses/find-a-course/2019/data-science-c6004

Upcoming events (5)

Unlocking the secrets in your DNA using Machine learning and Cloud-computing

5:30 pm Welcome networking with refreshments 6:00 pm Presentation followed by Q&A Genomic produces more data than Astronomy, twitter, and YouTube combined, having caused research in this discipline to leapfrog to the forefront of cloud technology. Dr. Denis Bauer provides an insider’s view into the development of a Spark-based machine learning framework that is able to find disease genes in the 3 billion letters of the genome. She will also cover serverless, which is pitted to become a $8 Billion market for its ability to accelerate software development, akin to how pre-fabrication has sped up the construction sector over bricklaying. Her serverless “search engine for the genome” enables researchers to use genome engineering for next-generation medicines. Dr Denis Bauer is head of cloud computing, Bioinformatics, at Australia’s government research agency. She is an internationally recognised expert in machine learning and cloud-based genomics, having presented at AWS Summit, Canberra, 2018 and Open data science conference, India, 2018. Her achievements include developing open-source machine-learning cloud services that accelerate disease research, which is used by 10,000 researchers annually. https://www.linkedin.com/in/denisbauer/ This is a joint event between Data Science Melbourne and YOW! Attendance is strictly limited, so only RSVP if you plan on attending.

Breakfast Event - 'The Pitfalls of Data Science'

KPMG Australia

At this breakfast event we'll be hearing from seasoned practitioners about their experience's on how data science can go wrong. Specifically in; a) model building b) communicating c) managing teams 7:45 - breakfast 8:00 - talks commence Els Godecharle - Data Science Communication As Data Scientists we often start our careers with a quantitative background. We are trained to solve problems by thinking logically and to communicate the details of our work in the same way. However, when we communicate with non-technical audiences this approach doesn’t always resonate. The aim of this talk is to help you increase the impact of your work through effective communication. https://www.linkedin.com/in/elsgodecharle/ Michael Brand - Pitfalls of Analytics Statistical modelling is an error-prone endeavour. Mistakes are easy to make and hard to detect. For over a decade now, Michael Brand has been running regular peer reviews for data science projects, and almost without exception these reviews uncovered serious issues that required major revision to the analysis. In this talk, Michael will recount some of the more dazzling blunders he caught over the years. More importantly, he will demonstrate that these are not one-off, unpredictable human errors; rather, they are a direct result of the standard practices of data science, standard practices that in recent years have only been getting worse. Michael will discuss what each of us can do, individually, to avoid falling into the cognitive traps, and what we can do together, as a community, to shift to better standard practices https://www.linkedin.com/in/michael-brand-b230736/ Sandra Hogan - Operationalising Data Science How can you make sure that the outputs of your hard work as a Data Scientist are valued by the business? How many times have you developed a strong predictive model but it never quite reaches the people who can extract value from it? In this chat, we will touch on some challenges organisations face in operationalising data science outputs, why and how things go wrong. We will also discuss some activities that can help to mitigate the issues and increase the likelihood of a successful outcome https://www.linkedin.com/in/sandra-hogan-9409421/

DSM is 5 years old!

Zendesk

It's our 5th birthday party!

Quantum Computing Explained

717 Bourke St

Join us for a gentle introduction to Quantum Computing. More details to come...

Past Events

Statistics with industry: demonstrating impact

Peter Hall Building

Photos (1,451)