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 #10 roundup

Our first unhackathon after Typhoon Mangkhut blew out our September meetup kicked off with a talk about a practical application of machine learning, and followed up with an introduction of the benefits of the L0 norm.

Two teams attempted to satisfy their curiosity with analysis of the Hong Kong housing market and a new cryptocurrency blockchain.

Our events happen monthly on Sundays, including talks from industry leaders and practitioners of data science, followed by a pitch session and group work on projects for the afternoon. You can find out more and stay up to date with our next events on our Slack channel, our Meetup page and on Facebook.

The talks

South China Morning Post data engineer Jonathan Barone introduced us to project Dali, a tool under development at the 115 year old newspaper intended to catalogue elements in images, potentially identifying faces and places from the media company’s archive of images.

You can see his talk in the video and slides below.

Dali slides can be read here.

Robert Porsch showed us an alternative way to regularise parameters by using the L0 (L-zero) norm.
He demonstrated that this new penalisation function is able to outperform more traditional approaches, such as the L1 norm, given a large enough sample size.
He has applied this method to predict the genetic risk for health outcomes and behavioural traits.

Read his slides here.

(Apologies for not including video, we had technical difficulties trying to record it.)

The hackathon session

Two groups formed to tackle real estate and create a new blockchain recommendation system.

Take a look at the videos of their presentations below.

Blockchain recommendation engine

This team set themselves the ambitious target of creating a system that can recommend a spread of cryptocurrencies using a number of existing systems including BigQuery, Docker and others. Check out the outcome in their presentation below.

Real estate analysis

This team started with the transaction history of real estate in Hong Kong and another dataset of stock exchange information. Their results were to show which areas had higher risk of low return on investment, also showing a correlation between stock market turmoil and housing transactions.

 

Next event

We are looking at dates in early December. Stay tuned on our Slack #events and Facebook for announcements.

 

Unhackathon #9 roundup

With the World Cup in Russia wrapping up on the same evening as our ninth un-hackathon, football was on our minds, and, with tongue in cheek, Data Science Hong Kong co-organiser Xavier put the question to our cohort of predictive modellers to find the winner, hours before the result was known.

IMG_20180715_165227
We were asked to build a predictive model for the world cup result, but our vote worked well enough. 

Getting down to more serious stuff, Houston Ho presented his company’s work on using machine learning to predict whether an employee is due to leave their position. His tool aims to give human resources teams a score for each employee based on a variety of characteristics. His model is achieving 80% accuracy and he says he can achieve more.

See his presentation below.

DSHK co-organiser Guy Freeman also presented about his new development offering a central repository for scraped data, using an open source philosophy. He showed the system’s potential by using a dataset of property transactions in Hong Kong spanning 20 years.

See his presentation below.

Group projects

Michael attracted the most interest among the group in his restaurant prediction model project. Using data from two restaurant booking systems, he aimed to predict how busy a restaurant would get using a machine learning model.

See the results from the group’s work in the slides below.

Forecasting Visitors of Restaurants

Visiting from Tokyo, Suzana Ilic brought exciting skills to the unhackathon, and decided to set her sights on unpicking hype in the crypto space. She was a bit camera shy so no video but you can see her slides below.

Quantifying Hype

Image from iOS

Morris Wong aimed to build an auto-tagging system for publishing, along the lines of taggernews. This tech could have wide-scale application when it’s up and running. See his presentation below.

Pocket exploration

That’s it for this month’s event. We will be announcing our next event shortly.