OpenDataDay 2020

Welcome to OpenDataDay! RSVP

When: March 7th 2020

Where: Launchcode Mentor Center


Open Data Day is an annual celebration of open data all over the world! It is an opportunity to show the benefits of open data and encourage the adoption of open data policies in government, business and civil society. Check out for more info!


9:00 am - Sign-in

9:30 am - Short introduction with Jon Leek and Shawn Moore

10:00 am - Start Hacking!

12:00 pm - Lunch! Catered sandwiches from Snarff’s! (Vegetarian option is available, if a food accomodation is necessary email

4:00 pm - Wrap up and start presentations and give out prizes!

6:00 pm - End!


80$ worth of Amazon giftcards, Google nest mini, Set of 4 Tile keychains!

We will be judging based on three categories:

  1. Data Analysis
    • Pattern & relationship discovery - End product demonstrates discovery of pattern and/or relationship in the dataset.

    • Critical thinking - End product dives deeper into the dataset (e.g. investigate outliers), author(s) considered the dataset from multiple perspectives.

    • Technique - End product makes use of statistical methods. The use of advanced statistical methods is a plus.

    • Statistical rigor - Author(s) understands assumptions made about dataset and/or statistical methods. Appropriate statistical methods are applied for the problem.

  2. Story Telling
    • Meaningfulness to target audience - End product identified a target audience. Presentation is considered from target audiences’ point of view.

    • Narrative - End product guides readers through a narrative to arrive to a conclusion.

    • Engagement - End product teaches viewers something new, gives them a new perspective and/or inspire them to take action.

  3. Design
    • Use of visual cues - End product makes use of strong visual cues (e.g. dollar sign, skull, abstract shapes, colors) to indicate qualities.

    • Use of visual hierarchy - End product design uses visual hierarchy (e.g. bold colors, sizes) to ensure important information stands out.

    • Readability - End product is not cluttered, and conveys information in a clear manner.

    • Annotation - End product contains meaningful annotation to provide context to help the audience understand the data.

    • Aesthetics - End product has visual appeal.




Lidar Data (2017):


Visualization tools: Tableau public account - D3.js Python – matplotlib

Data Analysis Tools: Python – scipy, pandas, scikit-learn R libraries

GIS tools: Mapbox, turf.js, qgis, esri