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This course teaches about time-series analysis and the methods used to predict, process, and recognize sequential data. Topics include:
By the end of this course, students will have practical knowledge of:
The course is structured around eight weeks of lectures and exercises. Each week requires three hours to complete.
Working knowledge of pandas and scikit-learn*
This class introduces time series and its applications. Topics include:
This class introduces stationarity and its mathematical transformations. It includes:
This class teaches about data smoothing methods and their applications. Learn about:
This class explains autocorrelation and partial autocorrelation. Topics include:
This class introduces AutoRegressive Moving Average (ARMA), ARIMA, and Seasonal AutoRegressive Integrated Moving Average (SARIMA) models. Topics include:
This class goes into further detail about advanced time series. Topics include:
This class introduces signal transformations. Learn about:
This class teaches how to use deep learning with time series analysis. Topics include: