Artificial Intelligence & Data Science
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Specialized Models: Time Series and Survival Analysis

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Course Features

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Duration

11 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

11 hours

Course Description

This course will introduce you to Machine Learning topics that can be used in conjunction with essential tasks such as forecasting and analysing censored data. Learn how to analyze data with a time component, and how to infer the outcome from censored data. A few techniques will be taught for Survival Analysis and Time Series Analysis. This course focuses on applying best practices and validating assumptions derived through statistical learning.

This course will help you: Identify common modeling problems with time series data. Explain how to decompose Time Series Data: trend, seasonality and residuals. Describe survival modeling approaches and hazard modeling. Identify types suitable for survival analysis. This course is for data scientists who are interested in getting hands-on experience in Time Series Analysis and Survival Analysis. What skills are required? You should be familiar with Python programming to get the most from this course.

Course Overview

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Instructor-Moderated Discussions

Skills You Will Gain

What You Will Learn

Describe hazard and survival modeling approaches

Explain how autoregressive, moving average, and ARIMA models work

Explain how to decompose Time Series data: trend, seasonality, and residuals

Identify common modeling challenges with time series data

Identify types of problems suitable for survival analysis

Understand how to select and implement various Time Series models

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