By the end of this course, learners will be able to analyze large datasets using Splunk’s statistical commands, transform raw events into meaningful metrics, build time-based and categorical visualizations, and correlate related events to uncover operational insights. Learners will also be able to apply conditional logic, enhance dashboards with advanced visualizations, and interpret trends and geographic patterns using Splunk.

Analyze and Visualize Data Using Splunk Statistics

Recommended experience
What you'll learn
Analyze large datasets using Splunk statistical commands to derive meaningful metrics and trends.
Build time-based, categorical, and advanced visualizations to communicate operational insights clearly
Correlate events and apply conditional logic to uncover patterns, anomalies, and geographic insights.
Details to know

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February 2026
16 assignments
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There are 4 modules in this course
This module introduces learners to Splunk’s statistical analysis capabilities by exploring the stats command and its core aggregation functions, enabling effective data summarization and insight extraction from large datasets.
What's included
6 videos4 assignments
This module focuses on transforming aggregated data into visual insights using Splunk’s chart and timechart commands, helping learners design effective visualizations for categorical and time-series analysis.
What's included
9 videos4 assignments
This module explores advanced visualization techniques in Splunk, including scatter plots, gauges, trend lines, totals, and geographical maps, to enhance analytical depth and dashboard effectiveness.
What's included
8 videos4 assignments
This module covers advanced data transformation and event correlation techniques in Splunk, enabling learners to manipulate fields, apply conditional logic, and correlate related events for deeper operational insights.
What's included
10 videos4 assignments
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