This course is for users who want to attain operational intelligence level 4, (business insights) and covers exploratory data analysis by using statistical tools and custom visualizations.

▪ Analytics Framework

▪ Exploring and visualizing data

▪ Cleaning and Preprocessing Data

▪ Numerical and String based clustering

▪ Data Correlation

▪ Meta Transactions

▪ Detecting Anomalies ▪ Forecasting

Splunk users

To be successful, students should have a solid understanding of the following courses:

▪ Intro to Splunk

▪ Using Fields

▪ Scheduling Reports and Alerts

▪ Visualizations

▪ Working with Time

▪ Statistical Processing

▪ Comparing Values

▪ Result Modification

▪ Leveraging Lookups and Sub-searches

▪ Correlation Analysis

▪ Search Under the Hood

▪ Intro to Knowledge Objects

▪ Creating Field Extractions ▪ Search Optimization

All these modules are available in the Splunk Power User Fast Start

Topic 1 – What is Data Science

▪ Define terms related to analytics and data science

▪ Describe the analytics workflow

▪ Describe Artificial Intelligence and Machine Learning

▪ Examine common Machine Learning myths

▪ Describe Splunk’s Machine Learning tools

Topic 2 – Exploratory Data Analysis

▪ Use bin and makecontinuous to restructure and visualize data

▪ Examine field statistics with fieldsummary

▪ Transform fields with eval and fillnull

▪ Clean text with the rex and cleantext commands

▪ Solve Anscombe’s Quartet

▪ Apply boxplots and 3d scatterplots to visualize data

Topic 3 – Event Clustering

▪ Take a behavioral based approach to cluster data

▪ Cluster numerical fields using the kmeans command

▪ Cluster based of string similarity with the cluster command

▪ Find patterns in clusters

Topic 4– Correlations and Transactions

▪ Define correlation and co-occurrence

▪ Use SPL correlation commands

▪ Use the statistical tests from the Machine Learning Toolkit to correlate fields

▪ Use streamstats and chart commands to correlate data

Topic 5– Anomaly Detection

▪ Define Statistical Outliers

▪ Use Add-hoc methods of numerical anomaly detection

▪ Find numerical or categorical anomalies with the AnomalyDetection command

Topic 6 – Forecasting

▪ Define forecasting use cases

▪ Use the predict command to forecast future timeseries