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

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 .

Let me cut to the chase. This is not your average Series Analysis course. This course covers modern developments such as deep learning, 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 Date

Modelling Series

Components of a Series

Differencing

Percentage Change, Subtracting the mean

Correlation in 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 series analysis techniques that you cannot learn anywhere else.

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




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