Rules & Guidelines

Teams
Recommended team size is 6 (a minimum of 4 members is enforced).

Data
Datasets to be used for the Hackathon need to be public (all participants should have open access to the data). Either data needs to be available for use on public domain and legal to use, or provided data sources by participating companies. Teams must disclose the use of their own data set.
 

Code
All code used must be open sourced. It can be existing code, new code written for the hackathon project, or a combination, but all code must be open sourced and licensed appropriately.


Submitting projects
Final submission consists of: Title/Name of project; a short description of project (~300 words); name of team & team members; data source; code (linked through Github); screen-cast or screenshots describing the project. A link will be provided for project submissions on Sunday.


Presentations

Presentations will be judged during a poster session on Sunday where judges will go around and ask teams to present their projects. The top 10 teams (selected by the judges) will then present to all the judges and Hackathon participants. Teams will present using their own computers. Time allotted for each presentation is 4 minutes (strictly enforced), plus up to 4min of Q&A.

Introduction

In line with the core vision of Cornell's newly developed campus in NYC, this event is directly applicable to the new age of data and aims to help students understand the career paths created by it. The event further challenges you to develop solutions in an intensive, limited time frame, while working with a team of students outside your own major. This highly in demand skill set is applicable to companies in almost all domains of life. Throughout the event, mentors from industry and faculty will coach you. At the conclusion of the hackathon on Sunday, teams will demo their proposed solutions to an audience comprised of students, faculty, staff, alumni, mentors, and judges, both physically present and through live streaming. While the event is a competition, it is also a 'coopetition' and encourages collaboration among all teams. 

Why Data Science?
Because it has not been done before (at least not on the east coast and not by a university)! Data science hackathons are new for both working professionals and university students. Many major industries are riding on a new wave of opportunity as collecting data and computation is becoming cheaper and more efficient. A decade ago, only major companies invested in data science. Now, almost all companies are collecting more and more data, but struggling to monetize it efficiently in decision making. There is a huge demand for qualified data scientists.


What is Data Science?

Deriving meaning from data by understanding how it fits into the larger picture of an organization. Think of business analytics that utilize CS, modeling, statistics, analytics, and mathematics.  By 2020 the world will generate 50x the amount of information compared to 2011 [EMC.com]. The U.S. could face a shortage of up to 190,000 professionals with data science skills by 2018 [McKinsey Global Institute]. Business, healthcare, and urban living will all benefit from problems analyzed using data science.

If you would like to participate in the hackathon, feel free to check out some resources and start hacking.

 
Three Verticals to Compete In

Visualization

Product

Analysis

 

 ~$5K in cash & prizes will be awarded

Keynote Speakers

  • Hilary Mason
    Founder, Fast Forward Labs

    Friday Keynote


  • Michael Li, PhD
    Founder, The Data Incubator

    Friday Keynote


  • Claudia Perlich, PhD
    Chief Scientist, Dstillery

    Saturday Keynote


  • Antonio Castro
    Managing Director-Financial Services Information & Data Strategy, Accenture

    Saturday Keynote


  • Sarah Guido
    Data Scientist, Bitly
    Judge

  • Randy Carnevale
    Director of Data Science, Capital One

    Judge

  • Kevin Novak
    Head of Data Science, Uber

    Judge

  • Larry Solomon
    N. America Operating Officer, Accenture
    Judge

  • Drew Conway, PhD
    Head of Data, Project Florida

    Sunday Panel & Judge

  • Lela Prashad
    Chief Data Scientist, NiJeL
    Judge

  • Jake Hofman, PhD
    Research Scientist, Microsoft Research

    Sunday Panel

  • Gilad Lotan, MPS
    Chief Data Scientist, Betaworks

    Sunday Panel

  • Matthew Bryan
    Director-Data Project Innovation,
    Capital One Labs

    Sunday Moderator

  • Joe Bylund, PhD
    Data Scientist, Moat

    Saturday Tech Talk

  • Carl Smith, PhD
    Data Scientist, Moat

    Saturday Tech Talk

  • Ben Laird
    Data Scientist, Capital One Labs

    Saturday Tech Talk

  • Divyanshu Vats, PhD
    Senior Data Science Engineer, SailThru
    Saturday Tech Talk

  • Alex Gaudio
    Senior Data Science Engineer, SailThru
    Saturday Tech Talk

  • Michelle Claire Chandra
    Community, CartoDB
    Saturday Tech Talk

  • Andy (Peter) Eschbacher
    Map Scientist, CartoDB
    Saturday Tech Talk

Partners

SPONSORS

Judges

Larry Solomon, N. America Operating Officer, Accenture

Drew Conway, Head of Data, Project Florida

Sarah Guido, Data Scientist, Bitly

Randy Carnevale, Director of Data Science, Capital One
Kevin Novak, Head Data Scientist, Uber
Jesse Beyroutey, IA Ventures
Lela Prashad, Chief Data Scientist & Co-Founder, NiJeL

Judging

Following the final pitches on Sunday afternoon, the panel of judges will evaluate each project and pick winners.
Company/sponsor specific prizes will be selected by participating companies and their representatives.

 

Each project will be evaluated in the 3 categories below.


Product
A data product, something used by a customer over time, will be judged upon its usefulness, interface and creativity.


Analysis
A good analysis project will apply sophisticated statistics or machine learning to derive interesting insights from the dataset. An analysis project will be judged upon technical strength, complexity, impact of derived result and innovation.


Visualization
A good visualization project will find interesting ways to look into a dataset or combine information from multiple data sources. A viz. project will be judged upon design aesthetics, value add, and complexity.

 

The first prize will go to the best overall score. Three subsequent prizes will be selected for each individual category.

It is highly recommended, to consider all 3 while deciding the scope of your project. Reach out to the organizers or mentors if you have any questions.

 

General Criteria for Each Category

Creativity of Idea

Creativity of approach and solution

Technical difficulty

Importance of question asked and impact on problem addressed

Degree of completion 

Resources

  • Map Visualization

  • Full page of curated resources

  • Quantifies Human Emotion

Mentors







Swag & Prizes

  • Nescafe coffee machines from Amazon

  • Everyone gets six months of Student Prime free & $5 gift card

  • Everyone gets a t-shirt, blanket, water bottle & more 

  • Networking & Dinner w/Chief Data Scientist & Engineers

  • Networking & Lunch with Accenture Tech Executives, & Winner Luncheon & fleece embroidered overnight blankets

  • Dinner with Data Scientists & Engineering Panel

  • Networking & Drinks with Executives
    Tech Talk by Jack Welde

  • Lunch & Networking with Data Science Team
    Captial One Labs SIG bottles

  • Lunch & Neworking with CTO & CSO
    CartoDB T-shirts

winners