Time Series Analysis in Python - Data Analysis & Forecasting

Welcome to the Python for Time Series - Data Analysis & Forecasting course. This course is designed for students who want to learn Python applications for time series datasets. This course assumes that you have basic level of knowledge on Python Programming. For getting most from the course you can apply the codes by yourself. All the codes in the course are typed in the videos so with non pre-written codes you are going to understand concepts better. The course covers the usage of Python libraries for time series data. There will be short lectures on statistics and Python library fundamentals at the beginning of the course to help you remember the basics. Then, the Python libraries used for time series data will be covered. After completing this course, you will be able to use the Pandas library for Time Series Data, check for seasonality in Time Series Data, perform a Dickey-Fuller test (a test for stationarity) on Time Series Data, build an ARIMA model for Time Series Data, and complete a Time Series project. Additionally, you will be able to visualize Time Series Data and forecast using Time Series Models. If you are interested in Python for Time Series, you can enroll in my course. You can reach me about the course anytime through the Q&A section on Udemy. I will be constantly checking the code and keeping it updated in the course.