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Specialized Models: Time Series and Survival Analysis
  • Level Professional
  • Duration 11 hours
  • Course by IBM
  • Offered by

About

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.

Modules

Introduction to Forecasting and Time Series Analysis

    • 6 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

6 Videos

  • Course Introduction
  • Introduction to Forecasting and Time Series Analysis
  • Pandas Time Series Notebook - Part 1
  • Pandas Time Series Notebook - Part 2
  • Pandas Time Series Notebook - Part 3
  • Pandas Time Series Notebook - Part 4

1 Readings

  • Time Series Demo (Activity)

Time Series Decomposition

    • 4 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

4 Videos

  • Time Series Decomposition
  • Decomposition Models
  • Decomposition Notebook - Part 1
  • Decomposition Notebook - Part 2

1 Readings

  • Time Series Decomposition Demo (Activity)

End of module review

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • End of Module Quiz

1 Readings

  • Summary/Review

Stationarity and Autocorrelation

    • 7 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

7 Videos

  • Stationarity and Autocorrelation
  • Stationarity Notebook - Part 1
  • Stationarity Notebook - Part 2
  • Stationarity Notebook - Part 3
  • Nonstationarity Examples
  • Identifying Nonstationarity
  • Common Transformations

1 Readings

  • Stationarity  Demo (Activity)

Time Series Smoothing

    • 6 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

6 Videos

  • Time Series Smoothing
  • Smoothing Moving Averages
  • Smoothing Exponential Intro
  • Advanced Smoothing
  • Smoothing Notebook - Part 1
  • Smoothing Notebook - Part 2

1 Readings

  • Time Series Smoothing Demo (Activity)

End of module review

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • End of Module Quiz

1 Readings

  • Summary/Review

ARMA models

    • 4 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

4 Videos

  • Autoregressive Models and Moving Average Models
  • Useful Plots
  • ARMA Models Notebook - Part 1
  • ARMA Models Notebook - Part 2

1 Readings

  • ARMA Models  Demo (Activity)

ARIMA and SARIMA models

    • 5 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

5 Videos

  • ARIMA and SARIMA Models
  • SARIMA Prophet Notebook - Part 1
  • SARIMA Prophet Notebook - Part 2
  • SARIMA Prophet Notebook - Part 3
  • SARIMA Prophet Notebook - Part 4

1 Readings

  • SARIMA Prophet Demo (Activity)

End of module review

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • End of Module Quiz

1 Readings

  • Summary/Review

Deep Learning for Forecasting

    • 5 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

5 Videos

  • Deep Learning - Part 1
  • Deep Learning - Part 2
  • Deep Learning - Part 3
  • Deep Learning Notebook - Part 1
  • Deep Learning Notebook - Part 2

1 Readings

  • Deep Learning for Forecasting Demo (Activity)

Survival Analysis

    • 3 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

3 Videos

  • Survival Analysis and Censoring - Part 1
  • Survival Analysis and Censoring - Part 2
  • Survival Analysis Notebook

1 Readings

  • Survival Analysis Demo (Activity)

End of module review

    • 1 Readings
    • 1 PeerReview
    • 1 Assignment
Show info about module content

1 Assignment

  • End of Module Quiz

1 Peer Review

  • Final Project

1 Readings

  • Summary/Review

Auto Summary

This engaging course, "Specialized Models: Time Series and Survival Analysis," is ideal for aspiring data scientists eager to gain hands-on experience in forecasting and analyzing censored data. Taught by Coursera, it covers techniques for Time Series and Survival Analysis, including ARIMA models and hazard modeling. The course spans 660 minutes and is available under Starter and Professional subscriptions. Learners should have a background in Python, Data Cleaning, EDA, Calculus, Linear Algebra, and Machine Learning.

Mark J Grover
Instructors

Mark J Grover

Miguel Maldonado
Instructors

Miguel Maldonado