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Python for Time Series Data Analysis

Welcome to the best online resource for learning how to use the Python programming Language for Time Series Analysis.

This course will teach you everything you need to know to use Python for forecasting time series data to predict new future data points.

We'll start off with the basics by teaching you how to work with and manipulate data using the NumPy and Pandas libraries with Python. Then we'll dive deeper into working with Pandas by learning about visualizations with the Pandas library and how to work with time stamped data with Pandas and Python.

Then we'll begin to learn about the statsmodels library and its powerful built in Time Series Analysis Tools. Including learning about Error-Trend-Seasonality decomposition and basic Holt-Winters methods.

Afterwards we'll get to the heart of the course, covering general forecasting models. We'll talk about creating AutoCorrelation and Partial AutoCorrelation charts and using them in conjunction with powerful ARIMA based models, including Seasonal ARIMA models and SARIMAX to include Exogenous data points.

Afterwards we'll learn about state of the art Deep Learning techniques with Recurrent Neural Networks that use deep learning to forecast future data points.

This course even covers Facebook's Prophet library, a simple to use, yet powerful Python library developed to forecast into the future with time series data.

So what are you waiting for. Learn how to work with your time series data and forecast the future.

We'll see you inside the course.

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Rating 4.5 based on 47 ratings
Length 15.5 total hours
Starts On Demand (Start anytime)
Cost $9
From Udemy
Instructor Jose Portilla
Download Videos Only via the Udemy mobile app
Language English
Subjects Programming
Tags Programming Languages Development

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What people are saying

time series

The graphs in the end w/fbprophet was a little wonky but def recommend Great course, thorough on much of the theory as well as the practice of time series.

Before Jose Portilla, the instructor, released his Python for Time Series Data Analysis course on Udemy, there were already a few time-series courses on DataCamp.

I like the careful step-by-step approach in teaching the basics of python and coding, although I wish the videos could expand on the mathematical explanation of the time series models in depth.

It would have been better had we also learned about ARIMAX model, cross-validation using time series (different cross-validation from Prophet) and how to use cross-validated results to forecast.

Its very good resource to learn about univariate time series analysis using classic forecasting, introduction with deep learning and also using facebook prophet Great Job.

A amazing course in Time Series.

Cover most of the concept and method in Time Series.

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very well

The course is well organized and very well-documented.

The instructor is very easy to understand, knows the topic very well, and presents complex subject matter in an easy to understand progression.

Thank you, Except RNN, all lectures were very well explained and I really liked the overall course content.

Continuing The teacher is explaining things very well, has a good voice, and is not wasting my time.

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autocorrelation and partial

Mr. Portilla builds upon time-series data in pandas by opening me to the exciting and fascinating new world of seasonal decomposition, the Holt-Winters model, autocorrelation and partial autocorrelation, stationarity, and non-seasonal and seasonal ARIMA forecasting (along with the occasional exogenous variable).

Mr. Portilla shows how to evaluate and select the best ARIMA models not only through examining autocorrelation and partial autocorrelation plots, AIC, and root mean square error.

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mr. portilla

Though these courses provided a solid overview of time-series analysis, they never provided the notebook resources and used the latest state-of-the-art tools like Mr. Portilla.

Mr. Portilla concludes the course with recurring neural networks and Facebook's Prophet library, which I consider as great supplemental bonuses for time-series forecasting in Python.

The only other issues that Mr. Portilla needs to address are the pmdarima and Keras import errors that the other students and I experienced while working on the exercises.

But Mr. Portilla needs to be better aware of these library import error issues in his exercise notebooks and rectify them as soon as possible.

Overall, Mr. Portilla strikes another home run with this course!

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Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Adjunct Professor, Time Series Econometrics $36k

Professor (adjunct series) $37k

Assistant Editor for RFC Series $58k

Traffic Studies Specialist Series $64k

Senior Series Editor $67k

Junior Series Producer, Music Choice Concert Series $71k

Series Editor $84k

Spark series editor $87k

Manager, Series Marketing $88k

Senior Editor for RFC Series $102k

3000 Series Team Leader $111k

Assistant Professor (adjunct series) $162k

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Rating 4.5 based on 47 ratings
Length 15.5 total hours
Starts On Demand (Start anytime)
Cost $9
From Udemy
Instructor Jose Portilla
Download Videos Only via the Udemy mobile app
Language English
Subjects Programming
Tags Programming Languages Development

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