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

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....
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1 - 2 of 2 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 krysten z

Oct 16, 2020

not able to cancel the course.