Time series data is one of the most common data types and understanding how to work with it is a critical data science skill if you want to make predictions and report on trends. In this track, you'll learn how to manipulate time series data using pandas, work with statistical libraries including NumPy and statsmodels to analyze data, and develop your visualization skills using Matplotlib, SciPy, and seaborn. You'll then apply your time series skills using real-world data, including financial stock data, UFO sightings, CO2 levels in Maui, monthly candy production in the US, and heartbeat sounds. By the end of this track, you'll know how to forecast the future using ARIMA class models and generate predictions and insights using machine learning models.
Overview
Syllabus
- Manipulating Time Series Data in Python
- In this course you'll learn the basics of working with time series data.
- Time Series Analysis in Python
- In this four-hour course, you’ll learn the basics of analyzing time series data in Python.
- Visualizing Time Series Data in Python
- Visualize seasonality, trends and other patterns in your time series data.
- ARIMA Models in Python
- Learn about ARIMA models in Python and become an expert in time series analysis.
- Time Series Analysis for Transportation
- Machine Learning for Time Series Data in Python
- This course focuses on feature engineering and machine learning for time series data.
Taught by
Stefan Jansen, Rob Reider, Thomas Vincent, Chris Holdgraf, and James Fulton
