• Online, Self-Paced
Course Description

Organizations use time series analysis and market basket analysis to understand patterns over time. Time series analysis uses data collected over regular intervals to analyze how the variable changes over time, while market basket analysis is an application of association rule learning that tries to learn what items occur together frequently in the same transaction.

In this course, discover how time series analysis works and how time series models like the autoregressive integrated moving average (ARIMA) model can help you forecast future values of time-varying data using historical values. Next, visualize time series data using moving averages and time series decomposition and fit an ARIMA model on this data for forecasting future values. Finally, use association rule learning for market basket analysis to analyze transaction data from a bakery and perform association rule learning on this data to figure out what items are frequently bought together.

Upon course completion, you will be able to confidently use KNIME for time series analysis and market basket analysis.

Learning Objectives

{"discover the key concepts covered in this course"}

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Systems Architecture

Feedback

If you would like to provide feedback for this course, please e-mail the NICCS SO at NICCS@hq.dhs.gov.