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Learner Reviews & Feedback for Specialized Models: Time Series and Survival Analysis by IBM

4.4
stars
37 ratings
10 reviews

About the Course

This course introduces you to additional topics in Machine Learning that complement essential tasks, including forecasting and analyzing censored data. You will learn how to find analyze data with a time component and censored data that needs outcome inference. You will learn a few techniques for Time Series Analysis and Survival Analysis. The hands-on section of this course focuses on using best practices and verifying assumptions derived from Statistical Learning. By the end of this course you should be able to: Identify common modeling challenges with time series data Explain how to decompose Time Series data: trend, seasonality, and residuals Explain how autoregressive, moving average, and ARIMA models work Understand how to select and implement various Time Series models Describe hazard and survival modeling approaches Identify types of problems suitable for survival analysis Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Time Series Analysis and Survival Analysis.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Supervised Machine Learning, Unsupervised Machine Learning, Probability, and Statistics....

Top reviews

MB
May 6, 2021

A very well-structured course with useful techniques and detail guidelines. The Python code templates are also really useful when bringing into real-life problems.

GS
May 15, 2021

It is a good course to build foundation on the modeling of timerseries data. It will be good to add other lessons for anomaly detection on timeseries.

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1 - 10 of 10 Reviews for Specialized Models: Time Series and Survival Analysis

By Lam C V D

Oct 10, 2020

The problem with this course is they use simulated data which cannot cut it. They need to use real life datasets and students given chance on how to do it properly.

By Ashish P

Apr 9, 2021

Interesting course with a whole bunch of new algorithms! Although great work from the tutor in explaining all those slides and the codes, still sadly, I would again point out that the Accent is really really hard to comprehend, inspite of the fact that English is like my native language.

Secondly, in the latter half of the course, specially in the labs for Arima, Sarima, FB prophet etc. where there is a whole bunch of complex new information to be digested, the pace in the labs feels to be apparently very rushed and haphazard.

There are too many concepts presented together but in the end it remains still quite unclear the sequence in which these methods could be applied to solve real world problems.

Helpful would be to use more real world Data Sets than Toy sets and show the sequence in which all these different Algorithms could be applied together on the same data set, to compare their performances.

Nevertheless, owing to the complexity of the subject, I appreciate the hard work put in by the tutors and the team at coursera and IBM!

Thank you!

By Keyur U

Dec 24, 2020

Toughest of all the 6 courses in the bunch.

By Rufus T

Apr 8, 2021

Good course with some useful tips, the Survival part of the course was particularly interesting.

By Mohamed G H

Feb 26, 2021

Not much details but good as an overview on the topic

By My B

May 7, 2021

A very well-structured course with useful techniques and detail guidelines. The Python code templates are also really useful when bringing into real-life problems.

By Ghada S

May 16, 2021

It is a good course to build foundation on the modeling of timerseries data. It will be good to add other lessons for anomaly detection on timeseries.

By Jose M

Feb 16, 2021

Again, thanks to the instructor in the videos

By vikas v

Nov 22, 2020

Amazing Concepts explanations

By krysten z

Oct 16, 2020

not able to cancel the course.