Introduction to Time Series With Python

Hello everyone!

Welcome to Introduction to Time Series Course with Python [2021].

Time Series Analysis has become an especially important field in recent years.

With inflation on the rise, many are turning to the stock market and cryptocurrencies in order to ensure their savings do not lose their value.
COVID-19 has shown us how forecasting is an essential tool for driving public health decisions.
Businesses are becoming increasingly efficient, forecasting inventory and operational needs ahead of time.
Let me cut to the chase. This is not your average Time Series Analysis course. This course covers modern developments such as deep learning, time series classification (which can drive user insights from smartphone data, or read your thoughts from electrical activity in the brain), and more.

We will cover techniques such as:

Basic Pandas Operations
Advanced Pandas Operations
Working with Pandas Datetime
Modelling Time Series
Components of a Time Series
Differencing
Percentage Change, Subtracting the mean
Correlation in Time Series
Rolling Window of Correlations
High Correlation
AutoCorrelation
AR & MA Models
ARMA model
Decision Tree Model
Forest Random Model
Gradient Boosted Tree Model
Handling Missing Data
Cointegration Model
Non-Stationary Series and No Cointegration
Granger Causality
ARIMA Model and forecasting
We will cover applications such as:

Netflix dataset
Disney dataset
Google Trend vacation dataset
Spotify dataset
Temperature average of ST.Louis dataset
Bank of America dataset
J.P. Morgan dataset
Furniture dataset
Corn dataset
So what are you waiting for? Signup now to get the skills to use the latest time series analysis techniques that you cannot learn anywhere else.

Thanks for reading, and I'll see you in class!

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