FlatIron School #Back2Learn Data Science Hackathon - 18th September 2019 📢

Flatiron School #Back2Learn hackathon with Count

Welcome to tonight’s data hackathon! :wave:

We’re excited to be offering a choice of datasets for tonight’s event.

We will be using Count’s new data publishing platform during the session for you to access and explore the data. It only takes a few minutes to learn, so don’t worry if you’ve not used it before. You will be able to prepare data tables for some topics within Count to download for use in other tools, if you prefer.


How the hackathon will work :stopwatch:

  1. Welcome and introduction to Count
  2. Introduction to the available datasets
  3. Pick the topic you want to explore and group up with others to familiarise yourselves with the available data and identify what questions you want to ask
  4. Head to the dataset you want to explore (links below)
  5. Click on the Let’s Go to learn how to use Count (or how to create a data table for download, if you’re using another tool). You can find more help by clicking on the ? button in the bottom right.
  6. Create your findings then share them:
  • On the Count platform
  • By replying to this thread
  • On social media (@counthq, @flatironschool, #Back2Learn)

Submitting your findings :trophy:

At the end of the evening, participants will be able to present back their findings. To submit, reply to this thread with a link to your findings and a short explanation.

There will be a small prize available for the winner, as decided by the facilitators and influenced by your upvotes.


What datasets can you explore tonight?

1. Natural Resources Governance Institute Oil data :oil_drum:

Discover the role that national oil companies play in their home-country economies and in the global oil and gas markets, using data from the Natural Resource Governance Institute’s National Oil Company Database.

:question: Challenges:

  • What can you learn from this data about which countries are most reliant on oil a source of income or as a fuel source?
  • What can you find out from this data about Saudi Aramco and the potential impact of recent events?

2. Tennis - men’s singles grand slams since 2000 #tennisdata :tennis:

Dive into stats from each grand slam men’s singles match since 2000 and data about the players, in this specially curated dataset combining information from Jeff Sackmann, Tennis-Data.co.uk, and UltimateTennisStatistics.com.

:question: Challenge: What are the match characteristics of the most successful grand slam players?

3. UFC (Ultimate Fighting Championship):boxing_glove:

Explore fight outcomes, competitor stats and predictions from Compughter Ratings.

:question: Challenge: Which fighters perform best on the big occasions (high attendance events, later rounds etc.)?

4. Investment in the UK Tech sector #UKTechData :chart_with_upwards_trend:

Explore one of the most extensive dataset on the UK tech sector from Dealroom.co, including information about companies, investors, founders and funding rounds.

:question: Challenge: Investigate trends in the UK tech investment landscape over the last 10 years: how has the profile of companies receiving investment changed?

5. NFL American Football - men’s singles grand slams since 2000 :football:

Explore scores, stats and predictions from Compughter Ratings.

:question: Challenge: Which is the easiest/hardest conference for a team to be in? Which teams have the most consistent ratings throughout the season?


Important notes :warning:

We are still developing the Count platform at the moment: take care not to refresh the page or press the back button or you will lose your queries. Share your insights so that you can retrieve them if you refresh the page. We really appreciate it if you can let one of the facilitators know if something isn’t working as you expected it to, or use the feedback button on the notebook page.

To vote or comment on people’s submissions, you’ll need to sign up as a community member on the top right of this page. (It only takes a few seconds.)