Data Science for Wearables Summer Term 2024

Introduction to Data Science for Wearables: Covering essential data science principles and their application in analyzing time-series data from wearables. This includes an overview of wearable technology’s role in health and fitness, alongside statistical foundations for robust data analysis.
Statistical Data Analysis and Experimentation: Focusing on designing statistically valid empirical data collection methods with wearables, including conducting experiments and achieving accurate statistical test results.
Handling Time-Series Data: Techniques for managing time-series data challenges, such as imputation for missing data and dimensionality reduction, to simplify analysis without losing critical information.
Feature Engineering and Machine Learning Basics: Introducing feature extraction methods from raw data and transitioning to machine learning, specifically for tasks like classification and pattern recognition in wearable sensor data.
Practical Application with Wearables: Empirical experimentation with wearable devices to apply covered theories in real-world scenarios, enhancing learning through hands-on experience. No prior knowledge required; the course caters to all levels, providing necessary background knowledge.

Lecture: Monday 1.30pm - 3.00pm in G2.U.10-14 (computer pool room in the basement of the Digital Health Center)
Tutorial: Tuesday 9.15am - 10.45am in G2.U.10-14

Please find all course material on openHPI.

The final grade is composed of three equal parts:

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