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Specialized Models: Time Series and Survival Analysis

IBM Skills Network

About this Course

24,459 recent views

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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Intermediate Level

Approx. 11 hours to complete

English

Subtitles: English

Skills you will gain

Dimensionality Reduction
Unsupervised Learning
Cluster Analysis
Time Series
K Means Clustering

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Intermediate Level

Approx. 11 hours to complete

English

Subtitles: English

Instructors

Instructor rating

4.33/5 (38 Ratings)

Mark J Grover

Digital Content Delivery Lead

IBM Data & AI Learning

38,584 Learners

13 Courses

Miguel Maldonado

Machine Learning Curriculum Developer

Data and AI Learning

24,915 Learners

5 Courses

Offered by

IBM Skills Network

IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.

Syllabus - What you will learn from this course

Week

Week 1

2 hours to complete

Introduction to Time Series Analysis

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.

2 hours to complete

10 videos (Total 83 min), 3 readings, 3 quizzes

10 videos

Course Introduction2m

Introduction to Forecasting and Time Series Analysis11m

Pandas Time Series Notebook - Part 17m

Pandas Time Series Notebook - Part 210m

Pandas Time Series Notebook - Part 313m

Pandas Time Series Notebook - Part 49m

Time Series Decomposition4m

Decomposition Models9m

Decomposition Notebook - Part 17m

Decomposition Notebook - Part 27m

3 readings

Time Series Demo (Activity)10m

Time Series Decomposition Demo (Activity)10m

Summary/Review10m

3 practice exercises

Check for Understanding5m

End of Module Quiz10m

Week

Week 2

3 hours to complete

Stationarity and Time Series Smoothing

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.

3 hours to complete

13 videos (Total 110 min), 3 readings, 3 quizzes

13 videos

Stationarity and Autocorrelation5m

Stationarity Notebook - Part 17m

Stationarity Notebook - Part 27m

Stationarity Notebook - Part 314m

Nonstationarity Examples4m

Identifying Nonstationarity7m

Common Transformations6m

Time Series Smoothing6m

Smoothing Moving Averages5m

Smoothing Exponential Intro5m

Advanced Smoothing12m

Smoothing Notebook - Part 112m

Smoothing Notebook - Part 215m

3 readings

Stationarity Demo (Activity)10m

Time Series Smoothing Demo (Activity)10m

Summary/Review10m

3 practice exercises

Check for Understanding5m

End of Module Quiz10m

Week

Week 3

3 hours to complete

ARMA and ARIMA Models

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.

3 hours to complete

9 videos (Total 117 min), 3 readings, 3 quizzes

9 videos

Autoregressive Models and Moving Average Models6m

Useful Plots9m

ARMA Models Notebook - Part 110m

ARMA Models Notebook - Part 210m

ARIMA and SARIMA Models12m

SARIMA Prophet Notebook - Part 112m

SARIMA Prophet Notebook - Part 215m

SARIMA Prophet Notebook - Part 314m

SARIMA Prophet Notebook - Part 425m

3 readings

ARMA Models Demo (Activity)10m

SARIMA Prophet Demo (Activity)10m

Summary/Review10m

3 practice exercises

Check for Understanding5m

End of Module Quiz10m

Week

Week 4

3 hours to complete

Deep Learning and Survival Analysis Forecasts

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.

3 hours to complete

8 videos (Total 70 min), 3 readings, 4 quizzes

8 videos

Deep Learning - Part 16m

Deep Learning - Part 26m

Deep Learning - Part 34m

Deep Learning Notebook - Part 114m

Deep Learning Notebook - Part 211m

Survival Analysis and Censoring - Part 110m

Survival Analysis and Censoring - Part 28m

Survival Analysis Notebook8m

3 readings

Deep Learning for Forecasting Demo (Activity)10m

Survival Analysis Demo (Activity)10m

Summary/Review10m

3 practice exercises

Check for Understanding5m

End of Module Quiz10m

Reviews

4.5

28 reviews

5 stars
73%
4 stars
14%
3 stars
7%
2 stars
4%
1 star
2%

TOP REVIEWS FROM SPECIALIZED MODELS: TIME SERIES AND SURVIVAL ANALYSIS

by GSMay 15, 2021

It is a good course to build foundation on the modeling of timerseries data. It will be good to add other lessons for anomaly detection on timeseries.

by KPApr 7, 2022

excellent and well explained course, especially for SARIMAX models.

by GVDec 16, 2021

I liked this course. It gives all the necessary information about classical machine learning algorithms as well as deep learning techniques

by JMJul 23, 2021

Great course, very well taught and topics are useful for future applications

View all reviews

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Specialized Models: Time Series and Survival Analysis

Specialized Models: Time Series and Survival Analysis

About this Course

Skills you will gain