ibm v4
7829  Reviews star_rate star_rate star_rate star_rate star_half

Specialized Models: Time Series and Survival Analysis

This IBM Web-Based Training (WBT) is Self-Paced and includes: - Instructional content available online for duration of course - Visuals without hands-on lab exercises This course introduces you to...

Read More
$485 USD GSA  $257.78
Course Code W7106G-SPVC
Duration 1.5 days
Available Formats Self Paced

This IBM Web-Based Training (WBT) is Self-Paced and includes:
- Instructional content available online for duration of course
- Visuals without hands-on lab exercises

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.

Skills Gained

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 Can Benefit

This course targets aspiring data scientists interested in acquiring hands-on experience with Time Series Analysis and Survival Analysis.

Prerequisites

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.

Course Details

Course Outline

1. Introduction to Time Series Analysis

2. Stationarity and Time Series Smoothing

3. ARMA and ARIMA Models

4. Deep Learning and Survival Analysis Forecasts