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 204 ratings |
|---|---|
| Length | 15.5 total hours |
| Starts | On Demand (Start anytime) |
| Cost | $16 |
| 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
I was doing my project on time series forecasting and this course helped me ace that project and introduced me to several new concepts.
Its really good course to get knowledge on time series analysis.
This course provides general ideas on different ways of modelling and forecasting Time Series data.
Thank you and would highly recommend this course to anyone who is new to Time Series forecasting.
The instructor teaches all the basics of time series forecasting without going too deep under the hood.
This is best course I have found on time series, Jose has the ability to clearly explain the topic and he also understand how much a trainee can understand.
The course gives a nice instruction for using the python library to forecast the time series.
Great course on time series analysis.
It provides very good guidance to Time Series Analysis in Python Clear, good examples, easy to follow.
This course is completely useful for data scientists.They can prepare to work with this course about time series work Great course for beggining to forecast Time Series datasets It is excellent course and really helpful course for me to develop my skills.
This course is really useful and handles all real time problems and being a time series data analyst I see a lot of value and sense from the course.
There wouldn't be a better course in time series analysis.
Great explanations to learn time series and apply these to real world problems.
Would be more helpful if some sections could include more real-world advanced stuff like more material on including exogenous variables or some messier data sets which would fare about real world problems Awesome course on Time Series.
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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's Python for Time Series Data Analysis course has not only satisfied my yearning but also exceeded my expectations.
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.
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.
I suggest that Mr. Portilla add two more exercise notebooks to the General Forecasting chapter.
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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| Rating | 4.5★ based on 204 ratings |
|---|---|
| Length | 15.5 total hours |
| Starts | On Demand (Start anytime) |
| Cost | $16 |
| 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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