This 13.5-hour course is for users who want to a5ain operational intelligence level 4, (business insights) and covers implementing analytics and data science projects using Splunk's statistics, machine learning, built-in and custom visualization capabilities.

Topic 1 – Analytics Workflow

• Define terms related to analytics and data science
• Describe the analytics workflow
• Describe common usage scenarios
• Navigate Splunk Machine Learning Toolkit

Topic 2 – Training and Testing Models

• Split data for testing and training using the sample command

• Describe the fit and apply commands

• Use the score command to evaluate models

Topic 3 – Regression: Predict Numerical Values

• Differentiate predictions from estimates

• Identify prediction algorithms and assumptions

• Model numeric predictions in the MLTK and Splunk Enterprise

Topic 4 – Clean and Preprocess the Data

• Define preprocessing and describe its purpose

• Describe algorithms that preprocess data for use in models

• Use FieldSelector to choose relevant fields

• Use PCA and ICA to reduce dimensionality

• Normalize data with StandardScaler and RobustScaler

• Preprocess text using Imputer, NPR, TF-IDF, and HashingVectorizer

Topic 5 – Clustering

• Define Clustering

• Identify clustering methods, algorithms, and use cases

• Use Smart Clustering Assistant to cluster data

• Evaluate clusters using silhoue5e score

• Validate cluster coherence

• Describe clustering best practices

Topic 6 – Forecasting Fields

• Differentiate predictions from forecasts

• Use the Smart Forecasting Assistant

• Use the StateSpaceForecast algorithm

• Forecast multivariate data

• Account for periodicity in each time series

Topic 7 – Detect Anomalies

• Define anomaly detection and outliers

• Identify anomaly detection use cases

• Use Splunk Machine Learning Toolkit Smart Outlier Assistant

• Detect anomalies using the Density Function algorithm

• View results with the Distribution Plot visualization

Topic 8 – Classify: Predict Categorical Values

• Define key classification terms

• Identify when to use different classification algorithms

• Evaluate classifier tradeoffs

• Evaluate results of multiple algorithms

Splunk classes are designed for specific roles such as Splunk Administrator, Developer, User, Knowledge Manager, or Architect.

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

• Fundamentals 1, 2, & 3
• Advanced Searching & Reporting Or the following single-subject courses:
• What is Splunk?
• 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
• Introduction to Knowledge Objects
• Creating Field Extractions
• Search Optimization

• Analytics Framework
• Regression for Prediction
• Cleaning and Preprocessing Data
• Algorithms, Preprocessing and Feature Extraction
• Clustering Data
• Detecting Anomalies
• Forecasting
• Classification

Our certification tracks provide comprehensive education for Splunk customer and partner personnel according to their areas of

Instructor-led lecture with labs, delivered via virtual classroom or at your site.