This course teaches you how to perform exploratory data analysis in Splunk using statistical commands, clustering, correlation, and custom visualizations. You will learn to turn raw machine data into business insights. Topics include the analytics workflow, data cleaning and preprocessing, numerical and string clustering, correlation analysis, anomaly detection, and forecasting with Splunk's machine learning tools.
Who Should Attend
Splunk power users, data analysts, and security or IT professionals who want to apply data science techniques to Splunk data.
Prerequisites
- Strong working knowledge of Splunk search, fields, statistical processing, lookups, subsearches, and knowledge objects
- Completion of the Splunk core user and power user courses or equivalent experience
Course Objectives
- Describe the analytics workflow and Splunk's machine learning tools
- Explore, clean, and visualize data with bin, fieldsummary, eval, rex, and cleantext
- Cluster events with kmeans and string-based methods
- Correlate data and build meta transactions
- Detect anomalies in time-series and event data
- Forecast trends with Splunk predictive commands
