Monthly un-hackathon + talks

Join our Data Science un-hackathon on the first Sunday every month. We host a full day of fun and working together on your data science projects!

At this event attendees will have the chance to pitch their projects, or join with others. We start the day with some fantastic industry specialists who will share their experiences operating in the data science field.

The event will be held at the South China Morning Post offices at Times Square on the first Sunday of the month.

Signup at: Eventbrite, Meetup, Facebook

Schedule of events:

9.30am – Arrive, registration
10am – Welcome
10.15am – Talks begin
11.30am – Pitch session, recruitment
12pm – Work on projects
5.30 pm – Present results of work session

Location:

SCMP: 20/f, Tower 1, Times Square, 1 Matheson St, Causeway Bay

Requirements:

Laptop / charger for those joining the coding
Prepared data, and projects pitches for the ones submitting projects
If presenting, send us your presentation slides ahead of time so we can prepare them.
HK$50 in cash for the space rental

Recommendations for project submissions:

Send us your presentation slides! Drop a link to one of the organisers on Slack or another way. We want to minimise time spent switching laptops so we will run your slides from our pc.
Prepare data in advance as much as you can; spending the day cleaning or retrieving data won’t gather crowds of DS! Contact organisers if you need a data repository to share data with all your team members.
If the project is already underway, prepare an introduction to it so that people can join. If you’re presenting slides, send them to us before you arrive, make sure the task you propose is feasible during the time of the event, and describe the skills you expect your team to have: R or Python? AWS, Spark? etc.

For final presentations:

Start writing the final presentation right from the start and add elements little-by-little all day long. Articulate the reason you want to do the project, and the solution. Make it understandable to everyone.
If you wish, your work will be published on this website with your bio, name, etc.

Other details:

50 participants max
Food/drink: Only water, coffee and tea are provided. Attendees can order their own food to the venue, take a break to find a restaurant nearby or bring their own lunch.
Price: HK$50. We charge a fee to cover organisation costs. We are a not-for-profit organisation and will aim to keep the costs of our events as low as possible to make it accessible to all.

Unhackathon #7 round-up: making sense through data

What time do people rent share bikes in San Jose? Houston and a group of data scientists has looked at bike share data in California and made some curious obvservations at our April unhackathon.

We also heard from Nick Lam-wai who is building a database on Hong Kong’s budget, the blueprint of government spending and priorities. And Chris Choy, who was working with Nick also discovered how to take historical PDFs of the budget and read the tables into Nick’s database. Expect big things from this group.

Our second meet up at Accellerate in Sheung Wan started with a discussion of the  Catboost library by Daniil Chepenko, who explains its benefits over other methods such as random forest.

Catboost is a gradient boosting library for work on decision trees, developed by the Russian search engine Yandex, building on many years of development in this field.

See his presentation video below, and follow the slides here.

Projects

Willis sought to find out what makes a Kickstarter project work. He came to the hackathon with data from 2009-2017, and a trained model with 60% accuracy, up from 30% at the beginning of his work. Knowing whether a Kickstarter will succeed is a huge investment advantage, so watch the short videos to see how well he went.

Pitch:

Conclusion:

Elizabeth Briel and Ben Davis have been seeking new ways to tell the story of global warming’s effects on arctic sea ice, and came to the hackathon with data they wanted to turn into a song. See the results below.

Slides are here.

Pitch:

Conclusion:

Nick Lam-wai created a thorough database of the Hong Kong budget, turning it from a human readable collection of documents back into one ready for machine analysis.

Slides here.

Pitch:

Conclusion:

Data science news round up

Our tight-knit community of data scientist have shared a wealth of news and inspiring projects from around the web over the past couple of months. Here is a brief round up of the more interesting articles, and remember, you can join in on our slack group.

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Millions of Chinese farmers reap benefits of huge crop experiment

An article that demonstrates the world changing potential of evidence based approaches to the world’s problems. For me, it’s also a reminder that it’s often not the latest buzzword or most glamourous topics that have the most impact.

Winning with Data Science

Next is an article examining the business and organisational side of data science. This is a topic that probably doesn’t get enough attention compared to the latest and coolest algorithm. It’s important for data scientists to take an interest in how organisations should adapt, if they don’t it will probably be decided by someone not qualified to make the decision!

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What Comes After Deep Learning?

This article examines whether deep learning is actually a blind alley and considers what new approaches might be next for data science. Also a brief examination of the question of US vs China in the AI “arms race”.

‘Who’s Leading AI’ Isn’t the Intelligent Question

Our final article explores the much talked about question of whether the US or China is winning and why it’s not the right question to ask.

If you found any of these articles interesting then do come and join the discussion on our Slack group, where you will also find details of meetups. https://datasciencehk.slack.com/