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UA Innovate Hackathon & Innovation Challenge - Data Analytics

Table of Contents

Getting Started

Step 1

  • Request a feature code
    • Ask IBM mentor
  • Use this to avoid putting in credit card information

Step 2

Step 3

Step 4

Step 5

Other Resources


Getting started with IBM Cloud

Data Analytics events

Join us for our workshops & sessions to help you with your solution.

  • Prompt Kickoff 11:30 am CST (Saturday) | Learn more about the prompt
  • Workshop 1: Intro to Data Science Tools, 1:00 pm CST (Saturday) | Learn about the data science process & see a demo of Watson Studio
  • Workshop 2: Deep dive on Hackathon prompt, 2:30 pm CST (Saturday) | Get the help you need to get started: intro to data analytics, problem statements, registering for IBM Cloud and Watson Studio
  • Midpoint Judging, 7:00 pm CST (Saturday) | Get feedback on your solution so far from our mentors
  • Final Judging, 10:00 am CST (Sunday) | Present your solution

Prompt

How can we use building energy use data to inform business decisions that can reduce costs for companies returning to the office?

Use the provided dataset from Women in Data Science (WiDS) and address the problem statement as either a data analyst OR data scientist as follows:

  • As a data scientist create a Machine learning Model that predicts energy efficiency for a building using the data features provide in the training dataset.

  • As a data analyst create a Jupyter notebook, mobile application, or website that can show a dashboard that answer the data analysis questions for your manager -see sample questions below and feel free to come up with others (you can answer as many as you are able to):

    • how does energy usage vary from state to state?
    • are there any patterns in the data for energy usage throughout the year?
    • what does energy consumption look like for similar facility types?
    • what type of building is least and most efficient per sq footage?
    • how is energy consumption related to how old the facility is?
    • are newer buildings more energy efficient?
    • can you create a report to summarize the test data set?
      • are there any missing data?
      • how would you handle missing data?
      • are there any outliers?
      • provide statistics around numerical data?
      • do some columns have a large number of categories?

Deliverables

To submit your entry for the Data Analaytics track, following the directions below (for either data analyst or data scientist).

Submit to Devpost:

Data Analyst Team:

One or more of the following for your dashboard (that answers one or more of the given data analysis questions):

  • Jupyter notebook with data analytics
  • Website with dashboard that shows data analytics
  • Mobile Application with dashboard that shows data analytics

Required:

  • Data visualization charts that answer the relevant data analytics questions
  • Use of at least one IBM service

Data Scientist Team

  • Jupyter notebook with machine learning model that predicts energy efficiency
  • Optional: Deployed model on IBM Cloud (Using Watson Machine Learning in Watson Studio)

Present in person:

Powerpoint presentation:

  • Team
  • Problem/Question you are answering
  • How you used code to approach the problem:
  • Describe the answer you found
  • Data Analytics charts
  • Demo of solution

Evaluation

Your solution will be evaluated on both your presentation and technical solution. Specific criteria:

  • Solution Completeness
  • Documentation
  • Technical Depth
  • Problem & Solution Description
  • Quality of Analysis & Presentation

Support

Our IBM mentors are here to help you as you work through this data analytics hackathon! We have both in-person and virtual mentors available to help Reach out for help on the #data-analytics channel in the UA Innovate Slack Workspace. Join the workspace here.