Health Sensors & Big Data

By ·Neuroscientist who thinks you should be Sensored

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Sensors are everywhere, and are playing an increasingly important role in our health, our environments, and our interactions with the world. In the past 3 months, the Qualcomm Tricorder X-Prize and the Nokia Sensing X-Challenge have been launched. Quantified Self has hit the mainstream, and the amount of data coming from sensors and sensor devices is exploding.

It's clearly an exciting time to be at the intersection of tech and health. But we've got a ways to go. Though it's now easy to collect petabytes of data, the field is still taking wild stabs in the dark trying to understand how to make this data interesting and actionable. We're moving from a world in which the problem is collecting enough data to a world where we need better tools to understand the data pouring in through the floodgates.

In this Skillshare, we're going to address a selection from the following questions:
• What are sources of sensor data? (Mobile phones, APIs, devices websites)
• How can you access sensor data, without building your own hardware?
• How can you access sensor data, prototyping your own hardware with off-the-shelf parts?
• What are your options for storing your data in the cloud? In a HIPAA-compliant format?
• What are the tradeoffs for different data-storage options?
• Once you have data, what tools can you use to analyze it? What if you don't know how to program?
• How can you mash up sensor data with other data (e.g. location, time of day, social networks)?
• How do you design a pilot experiment to test your hypothesis?

We'll focus on some real-world examples, and we encourage you to bring some problems in your own life you'd like us to work through as a group.

**This class in will be held at Orange Silicon Valley's offices (NOT at Rock Health).**

Prerequisites
Helpful, but not necessary: experience in programming. All levels welcome, bring questions
Taught by Rachel Kalmar

Neuroscientist who thinks you should be Sensored

Rachel Kalmar has 12 years of experience relating large, noisy data sets to human behavior. She started diysensors.org, a community meetup and discussion group for people working on sensor devices and applications.  She has a PhD in neuroscience from Stanford University and is an alum of the d.scho...

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