# Time Series in Python – Data Analysis & Forecasting

Learn Python for Time Series – Learn Python libraries for Time Series analysis and forecasting

Welcome to the Python for Time Series – Data Analysis & Forecasting course. This course is built for students who wants to learn python applications for time series data sets. This course covers the usage of Python libraries on time series data. There will be both short lectures of statistics and Python fundamentals at the starting of the course in order to remembering the basics. Then the libraries of Python which is used for time series data will be covered. At the end of the course an analysis and forecast is going to be made from gold prices data set for practicing what is learned in the course for one more time. So the course outline is:

What you’ll learn

• Learn building an ARIMA model in Python.
• Learn the application of Dickey – Fuller test in Python.
• Learn checking for seasonality in Python.
• Repeat the Statistics and Python fundamentals.

Course Content

• Statistics Fundamentals –> 9 lectures • 26min.
• Python Fundamentals –> 9 lectures • 49min.
• Pandas –> 2 lectures • 11min.
• Checking for seasonality & Stationarity –> 2 lectures • 7min.
• Autocorrelation & Decomposition –> 2 lectures • 8min.
• Building an AR model and Forecasting –> 1 lecture • 7min.
• Building an ARIMA model and Forecasting –> 1 lecture • 5min.
• Time Series Project –> 3 lectures • 12min.

Requirements

Welcome to the Python for Time Series – Data Analysis & Forecasting course. This course is built for students who wants to learn python applications for time series data sets. This course covers the usage of Python libraries on time series data. There will be both short lectures of statistics and Python fundamentals at the starting of the course in order to remembering the basics. Then the libraries of Python which is used for time series data will be covered. At the end of the course an analysis and forecast is going to be made from gold prices data set for practicing what is learned in the course for one more time. So the course outline is:

1. Statistics Fundamentals
2. Python Fundamentals
3. Pandas Introduction
4. Usage of Python Libraries for Time Series Data
5. Time Series Project

After taking this course you will be able to use Pandas library for Time Series Data, check for seasonality for Time Series Data, do a Dickey – Fuller test (test for stationary) for Time Series Data, build an ARIMA model for Time Series Data and build a Time Series project completely. If you are interested in Python for Time Series, you can enroll into my course. You can reach to me  about the course everytime from the Q&A section of the course on Udemy.

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