| Summary: | Time Series Analysis is one of the classic topics in data analysis and machine learning. It has significant business value and by the end of this course, you'll understand why. The course teaches you the basics of time series analysis (e.g., relevant types of data analysis problems, classical approaches using autoregressive models, etc.); surveys recent advances (i.e., structured inference and deep learning-based approaches); reviews the best practices for handling time series data; and helps you understand the best use cases for time series analysis. The video focuses on state-of-the-art Python libraries and toolsets-- the de facto standards for practical data science-- that allow you to quickly move models from prototype to production. Learners should have basic familiarity with Python, data analysis, and data science. Discover the best practices in prediction and anomaly detection using Python Explore and understand the basics of time series analysis Survey recent advances in time series analysis and machine learning Learn how to use the most relevant Python data analysis libraries and toolsets Review key concepts related to time series storage and databases Understand the business value of analyzing time series data Mikio Braun is the engineering lead for search at Zalando, one of Europe's biggest fashion platforms. An in-demand speaker at O'Reilly Media's Strata Data conferences, Mikio holds a PhD in machine learning from the University of Bonn
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