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Python for Self-Trackers: How to Visualize and Better Understand Your Life in Data


It’s easier than ever to track our lives, work and bodies with a smartphone, wearable, home sensor or computer. But what can we do with all this data? Can it help you become a better version of yourself? While we often hear about data in terms of data leaks and surveillance, personal data and self-tracking can be empowering too. With smartphones, wearables, tracking apps, home sensors, and many other methods, it’s easier than ever to collect a lot of data on our lives. But we are still struggling to engage and find meaning in all of the data we collect. Python and its data science toolset can help transform personal data into a personal dashboard of data visualizations and predictive models. How can python help us better collect, visualize, understand and find patterns in our personal data and self-tracking? In this talk, I’ll show you how to track your life in different ways, and, with python’s data science toolkit, how to engage and understand that data. The stated goal of the quantified self and self-tracking is to “measure or document something about your self such that it gains meaning.” I think we can go one step further and use data to become better; use data to become data-driven!

Mark Koester (@markwkoester) is a self-tracker, writer, and web and mobile app developer. Creator: - PhotoStatsApp, a photo tracking app without the cloud, - PodcastTracker, a web app to log your podcast listening, and - Biomarker Tracker, a health analytics service to better understand your blood test results. He currently runs a boutique dev shop ( and is an active open source contributor. Former Regional Lead in Greater China at Techstars, a seed-stage accelerator. He regularly writes about self-tracking, quantified self and data-driven life at Social Media: * * *


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