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Specialized Models: Time Series and Survival Analysis

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Specialized Models: Time Series and Survival Analysis

Instructors: Mark J Grover

18,709 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

145 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.

145 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

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

12 assignments¹

AI Graded see disclaimer

Taught in English

91%

of learners achieved a positive career outcome

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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.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate from IBM

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.

Module details

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 videos3 readings3 assignments

10 videosTotal 83 minutes

Course Introduction2 minutes
Introduction to Forecasting and Time Series Analysis12 minutes
Pandas Time Series Notebook - Part 18 minutes
Pandas Time Series Notebook - Part 210 minutes
Pandas Time Series Notebook - Part 313 minutes
Pandas Time Series Notebook - Part 49 minutes
Time Series Decomposition5 minutes
Decomposition Models9 minutes
Decomposition Notebook - Part 18 minutes
Decomposition Notebook - Part 28 minutes

3 readingsTotal 30 minutes

Time Series Demo (Activity)10 minutes
Time Series Decomposition Demo (Activity)10 minutes
Summary/Review10 minutes

3 assignmentsTotal 20 minutes

Check for Understanding5 minutes
Check for Understanding5 minutes
End of Module Quiz10 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 videosTotal 110 minutes

Stationarity and Autocorrelation5 minutes
Stationarity Notebook - Part 18 minutes
Stationarity Notebook - Part 28 minutes
Stationarity Notebook - Part 314 minutes
Nonstationarity Examples4 minutes
Identifying Nonstationarity8 minutes
Common Transformations6 minutes
Time Series Smoothing6 minutes
Smoothing Moving Averages5 minutes
Smoothing Exponential Intro5 minutes
Advanced Smoothing12 minutes
Smoothing Notebook - Part 113 minutes
Smoothing Notebook - Part 216 minutes

3 readingsTotal 30 minutes

Stationarity Demo (Activity)10 minutes
Time Series Smoothing Demo (Activity)10 minutes
Summary/Review10 minutes

3 assignmentsTotal 20 minutes

Check for Understanding5 minutes
Check for Understanding5 minutes
End of Module Quiz10 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 videosTotal 117 minutes

Autoregressive Models and Moving Average Models7 minutes
Useful Plots10 minutes
ARMA Models Notebook - Part 110 minutes
ARMA Models Notebook - Part 211 minutes
ARIMA and SARIMA Models12 minutes
SARIMA Prophet Notebook - Part 112 minutes
SARIMA Prophet Notebook - Part 216 minutes
SARIMA Prophet Notebook - Part 315 minutes
SARIMA Prophet Notebook - Part 425 minutes

3 readingsTotal 30 minutes

ARMA Models Demo (Activity)10 minutes
SARIMA Prophet Demo (Activity)10 minutes
Summary/Review10 minutes

3 assignmentsTotal 20 minutes

Check for Understanding5 minutes
Check for Understanding5 minutes
End of Module Quiz10 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.