(weeks 1-6)
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For the first project I scraped data from the New York AA meeting website, cleaned and standardized the data.
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Then I used the google maps API to add geo-location (lat and long) to the data and and stored it in a MongoDB database hosted on and aws EC2 instance.
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I then created an aggregation pipeline for the MongoDB server to feed meeting information to a simple front-end google maps application which shows the nearby meetings happening that day.
Project 2: Creating a public facing API for visualizing data from a hand activity tracking device I made.
(weeks7-10)
- I used a 3 axis Accelerometer to measure the movements of an artists hand while folding an origami model. The sensor data was fed directly to a PostgreSQL database on an AWS Relational Database Service (RDS) instance I set up.
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To follow up I created a simple API which returned a json document for the data for whatever model or activity a developer requests.
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Finally I designed simple example use case for the data and API querying the API and plotting the data using matplotlib and D3.
Here is a chart of the Latency for Mongodb queries from the Google Maps project:(week5) https://ianssmith.github.io/data-structures/week5/latencyViz/
Postgres api URI (week10[no-longer live]): https://postgres-api-ianssmith.c9users.io/model/crane -To access data type in the above URI and append the desired model you would like to see (in place of 'crane' in the above example)