Python for Time Series Data Analysis
Welcome to one of the Best Online Time Series Courses for learning how to analyze time series using the Python computer language! You will learn all you need to know in this course to use Python to forecast time series data in order to anticipate brand-new future data points. By showing you how to use the Python libraries NumPy and Pandas to deal with and alter data, you'll start with the fundamentals.
Then you'll delve even further into using Pandas by learning about visualizations using the Pandas library and how to use Pandas and Python to work with time-stamped data. The strong built-in Time Series Analysis Tools of the statsmodels library will then start to become more familiar to you. You will next go on to the main portion of the course, which covers general forecasting models. In order to include exogenous data points into sophisticated ARIMA-based models, such as seasonal ARIMA models and SARIMAX, the instructor will discuss the creation of autocorrelation and partial autocorrelation charts.
After that, you'll discover cutting-edge Deep Learning methods with Recurrent Neural Networks, which employ deep learning to predict upcoming data points. Even Facebook's Prophet library, a user-friendly yet potent Python package designed to make future predictions using time series data, is covered in this course. So why are you still waiting? Discover how to anticipate the future using your time series data!
Requirements:
- General Python Skills (knowledge up to functions)
Who this course is for:
- Python Developers interested in learning how to forecast time series data
Course Rating: 4.6/5
Enroll here: https://www.udemy.com/course/python-for-time-series-data-analysis/
