Specialized Models: Time Series and Survival Analysis

Specialized Models: Time Series and Survival Analysis

Instructors: Mark J Grover

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4 modules

Gain insight into a topic and learn the fundamentals.

139 reviews

Intermediate level

Some related experience required

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

139 reviews

Intermediate level

Some related experience required

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

12 assignments¹

AI Graded see disclaimer

Taught in English

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Build your Machine Learning expertise

This course is part of the IBM Machine Learning Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

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There are 4 modules in this 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.

This module introduces the concept of forecasting and why Time Series Analysis is best suited for forecasting, compared to other regression models you might already know. You will learn the main components of a Time Series and how to use decomposition models to make accurate time series models.

What's included

10 videos4 readings3 assignments

10 videos Total 83 minutes

Course Introduction 2 minutes
Introduction to Forecasting and Time Series Analysis 12 minutes
Pandas Time Series Notebook - Part 1 8 minutes
Pandas Time Series Notebook - Part 2 10 minutes
Pandas Time Series Notebook - Part 3 13 minutes
Pandas Time Series Notebook - Part 4 9 minutes
Time Series Decomposition 5 minutes
Decomposition Models 9 minutes
Decomposition Notebook - Part 1 8 minutes
Decomposition Notebook - Part 2 8 minutes

4 readings Total 40 minutes

Time Series and Survival Analysis 10 minutes
Time Series Demo (Activity) 10 minutes
Time Series Decomposition Demo (Activity) 10 minutes
Summary/Review 10 minutes

3 assignments Total 20 minutes

Check for Understanding 5 minutes
Check for Understanding 5 minutes
End of Module Quiz 10 minutes

This module introduces you to the concepts of stationarity and Time Series smoothing. Having a Time Series that is stationary is easy to model. You will learn how to identify and solve non-stationarity. Smoothing is relevant to you as it will help improve the accuracy of your models.

What's included

13 videos3 readings3 assignments

13 videos Total 110 minutes

Stationarity and Autocorrelation 5 minutes
Stationarity Notebook - Part 1 8 minutes
Stationarity Notebook - Part 2 8 minutes
Stationarity Notebook - Part 3 14 minutes
Nonstationarity Examples 4 minutes
Identifying Nonstationarity 8 minutes
Common Transformations 6 minutes
Time Series Smoothing 6 minutes
Smoothing Moving Averages 5 minutes
Smoothing Exponential Intro 5 minutes
Advanced Smoothing 12 minutes
Smoothing Notebook - Part 1 13 minutes
Smoothing Notebook - Part 2 16 minutes

3 readings Total 30 minutes

Stationarity Demo (Activity) 10 minutes
Time Series Smoothing Demo (Activity) 10 minutes
Summary/Review 10 minutes

3 assignments Total 20 minutes

Check for Understanding 5 minutes
Check for Understanding 5 minutes
End of Module Quiz 10 minutes

This module introduces moving average models, which are the main pillar of Time Series analysis. You will first learn the theory behind Autoregressive Models and gain some practice coding ARMA models. Then you will extend your knowledge to use SARMA and SARIMA models as well.

What's included

9 videos3 readings3 assignments

9 videos Total 117 minutes

Autoregressive Models and Moving Average Models 7 minutes
Useful Plots 10 minutes
ARMA Models Notebook - Part 1 10 minutes
ARMA Models Notebook - Part 2 11 minutes
ARIMA and SARIMA Models 12 minutes
SARIMA Prophet Notebook - Part 1 12 minutes
SARIMA Prophet Notebook - Part 2 16 minutes
SARIMA Prophet Notebook - Part 3 15 minutes
SARIMA Prophet Notebook - Part 4 25 minutes

3 readings Total 30 minutes

ARMA Models Demo (Activity) 10 minutes
SARIMA Prophet Demo (Activity) 10 minutes
Summary/Review 10 minutes

3 assignments Total 20 minutes

Check for Understanding 5 minutes
Check for Understanding 5 minutes
End of Module Quiz 10 minutes

This module introduces two additional tools for forecasting: Deep Learning and Survival Analysis. In addition to AI and Machine Learning applications, Deep Learning is also used for forecasting. Survival Analysis is a branch of Statistics first ideated to analyze hazard functions and the expected time for an event such as mechanical failure or death to happen. Survival Analysis is still used widely in the pharmaceutical industry and also in other business scenarios with limited data related to censoring, the lack of information on whether an event occurred or not for a certain observation.