Python for Time Series Data Analysis
- 15.5 hours on-demand video
- 3 articles
- 4 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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- Pandas for Data Manipulation
- NumPy and Python for Numerical Processing
- Pandas for Data Visualization
- How to Work with Time Series Data with Pandas
- Use Statsmodels to Analyze Time Series Data
- Use Facebook's Prophet Library for forecasting
- Understand advanced ARIMA models for Forecasting
- General Python Skills (knowledge up to functions)
Welcome to the best online resource for learning how to use the Python programming Language for Time Series Analysis!
This course will teach you everything you need to know to use Python for forecasting time series data to predict new future data points.
We'll start off with the basics by teaching you how to work with and manipulate data using the NumPy and Pandas libraries with Python. Then we'll dive deeper into working with Pandas by learning about visualizations with the Pandas library and how to work with time stamped data with Pandas and Python.
Then we'll begin to learn about the statsmodels library and its powerful built in Time Series Analysis Tools. Including learning about Error-Trend-Seasonality decomposition and basic Holt-Winters methods.
Afterwards we'll get to the heart of the course, covering general forecasting models. We'll talk about creating AutoCorrelation and Partial AutoCorrelation charts and using them in conjunction with powerful ARIMA based models, including Seasonal ARIMA models and SARIMAX to include Exogenous data points.
Afterwards we'll learn about state of the art Deep Learning techniques with Recurrent Neural Networks that use deep learning to forecast future data points.
This course even covers Facebook's Prophet library, a simple to use, yet powerful Python library developed to forecast into the future with time series data.
So what are you waiting for! Learn how to work with your time series data and forecast the future!
We'll see you inside the course!
- Python Developers interested in learning how to forecast time series data