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Published on Jul 26, 2014
PyData Berlin 2014 Applications for self tracking that collect, analyze, or publish personal and medical data are getting more popular. This includes either a broad variety of medical and healthcare apps in the fields of telemedicine, remote care, treatment, or interaction with patients, and a huge increasing number of self tracking apps that aims to acquire data form from people’s daily life. The Quantified Self movement goes far beyond collecting or generating medical data. It aims in gathering data of all kinds of activities, habits, or relations that could help to understand and improve one’s behavior, health, or well-being. Both, health apps as well as Quantified Self apps use either just the smartphone as data source (e.g., questionnaires, manual data input, smartphone sensors) or external devices and sensors such as ‘classical’ medical devices (e.g,. blood pressure meters) or wearable devices (e.g., wristbands or eye glasses). The data can be used to get insights into the medical condition or one’s personal life and behavior. This talk will provide an overview of the various data sources and data formats that are relevant for self tracking as well as strategies and examples for analyzing that data with Python. The talk will cover: Accessing local and distributed sources for the heterogeneous Quantified Self data. That includes local data files generated by smartphone apps and web applications as well as data stored on cloud resources via APIs (e.g., data that is stored by vendors of self tracking hardware or data of social media channels, weather data, traffic data etc.) Homogenizing the data. Especially, covering typical problems of heterogeneous Quantified Self data, such as missing data or different and non-standard data formatting. Analyzing and visualizing the data. Depending on the questions one has, the data can be analyzed with statistical methods or correlations. For example, to get insight into one's personal physical activities, steps data form activity trackers can be correlated to location data and weather information. The talk covers how to conduct this and other data analysis tasks with tools such as pandas and how to visualize the results. The examples in this talk will be shown as interactive IPython sessions.