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Applied Time Series Analysis in Python

Use Python and Tensorflow to apply the latest statistical and deep learning techniques for time series analysis
4.4
4.4/5
(144 reviews)
712 students
Created by

9.3

CourseMarks Score®

9.0

Freshness

9.0

Feedback

9.3

Content

Platform: Udemy
Video: 6h 56m
Language: English
Next start: On Demand

Top Time Series Analysis courses:

Detailed Analysis

CourseMarks Score®

9.3 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

9.0 / 10
This course was last updated on 2/2021.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

9.0 / 10
We analyzed factors such as the rating (4.4/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

9.3 / 10
Video Score: 8.6 / 10
The course includes 6h 56m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
The average video length is 7 hours 03 minutes of 14 Time Series Analysis courses on Udemy.
Detail Score: 9.3 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.9 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

2 articles.
8 resources.
0 exercise.
0 test.

Table of contents

Description

This is the only course that combines the latest statistical and deep learning techniques for time series analysis. First, the course covers the basic concepts of time series:
•stationarity and augmented Dicker-Fuller test
•seasonality
•white noise
•random walk
•autoregression
•moving average
•ACF and PACF,
•Model selection with AIC (Akaike’s Information Criterion)
Then, we move on and apply more complex statistical models for time series forecasting:
•ARIMA (Autoregressive Integrated Moving Average model)
•SARIMA (Seasonal Autoregressive Integrated Moving Average model)
•SARIMAX (Seasonal Autoregressive Integrated Moving Average model with exogenous variables)
We also cover multiple time series forecasting with:
•VAR (Vector Autoregression)
•VARMA (Vector Autoregressive Moving Average model)
•VARMAX (Vector Autoregressive Moving Average model with exogenous variable)
Then, we move on to the deep learning section, where we will use Tensorflow to apply different deep learning techniques for times series analysis:
•Simple linear model (1 layer neural network)
•DNN (Deep Neural Network)
•CNN (Convolutional Neural Network)
•LSTM (Long Short-Term Memory)
•CNN + LSTM models
•ResNet (Residual Networks)
•Autoregressive LSTM
Throughout the course, you will complete more than 5 end-to-end projects in Python, with all source code available to you.

You will learn

✓ Descriptive vs inferential statistics
✓ Random walk model
✓ Moving average model
✓ Autoregression
✓ ACF and PACF
✓ Stationarity
✓ ARIMA, SARIMA, SARIMAX
✓ VAR, VARMA, VARMAX
✓ Apply deep learning for time series analysis with Tensorflow
✓ Linear models, DNN, LSTM, CNN, ResNet
✓ Automate time series analysis with Prophet

Requirements

• Basic knowledge of Python
• Basic knowledge of deep learning
• Jupyter notebook installed (or access to Google Colab)

This course is for

• Beginner data scientists looking to gain experience with time series
• Deep learning beginners curious about times series
• Professional data scientists who need to analyze time series
• Data scientists looking to transition from R to Python

How much does the Applied Time Series Analysis in Python course cost? Is it worth it?

The course costs $14.99. And currently there is a 82% discount on the original price of the course, which was $84.99. So you save $70 if you enroll the course now.
The average price is $12.6 of 14 Time Series Analysis courses on Udemy.

Does the Applied Time Series Analysis in Python course have a money back guarantee or refund policy?

YES, Applied Time Series Analysis in Python has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the Applied Time Series Analysis in Python course, but there is a $70 discount from the original price ($84.99). So the current price is just $14.99.

Who is the instructor? Is Marco Peixeiro a SCAM or a TRUSTED instructor?

Marco Peixeiro has created 1 courses that got 144 reviews which are generally positive. Marco Peixeiro has taught 716 students and received a 4.4 average review out of 144 reviews. Depending on the information available, Marco Peixeiro is a TRUSTED instructor.
Data Scientist and Instructor
Experience as a data scientist

I completed a bachelor degree in a field that did not interest me. Instead, I started learning web development on the side and landed my first job as a web developer.

I went on to teach myself data science, as I was very curious about the idea of machines learning by themselves. I proceeded to land another job as a professional data scientist, even though I do not have a masters or a PhD.

As a self-taught data scientist and web developer, I know what it feels like to dive in a completely new field. I know the hard parts, and I know what must be taught to land a professional job and gain new skills with a real impact on our career.

Experience as an instructor

As far I can remember, I was always the person explaining to my peers. Through tutoring, blog articles, and courses, I have a passion for sharing my knowledge and teaching. I strive to have an impact on my students and see them become better and more knowledgeable.

Show more

9.3

CourseMarks Score®

9.0

Freshness

9.0

Feedback

9.3

Content

Platform: Udemy
Video: 6h 56m
Language: English
Next start: On Demand

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