Course Description
This 2 day course helps improve your company's forecasting operations by teaching employees with intermediate data and analytics skills how to identify and evaluate forecast performance. Addressing possible problems and pitfalls, they’ll discover effective ways to identify, develop, and improve forecast approaches using a wide range of techniques and challenges, including visualizations to communicate results with stakeholders. The course uses Python to explore, visualize, and model time series data—all with an emphasis on practical application.
Course Outcomes
Upon completion of the course, attendees should be able to:
Define and identify time series applications
Understand modeling concepts including stationarity, autocorrelation, and seasonality
Use Python to implement and assess time series forecasting models
Training Content
MODULE 1:
Time Series Analysis in Python
Pandas for time series data
Approaches to common issues of time series modeling in Python
Implementing standard models
Introduction to packages and tools
Applications, common issues, and solution types
MODULE 2:
Time Series Modeling
Motivation: Case studies with data samples
Transformations and seasonal decomposition
Standard time series approaches in Python
Visualizing time series data in Python
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