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Splunk for Analytics and Data Science

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Course Description

This 13.5-hour course is for users who want to attain 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.

 

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Instructor-led Training Schedule

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Prerequisites Knowledge

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
  • Multivalue Fields
  • Introduction to Knowledge Objects
  • Creating Knowledge Objects
  • Creating Field Extractions
  • Enriching Data with Lookups
  • Data Models
  • Introduction to Dashboards
  • Dynamic Dashboards
  • Using Choropleth
  • Search Optimization

Course Topics

  • Analytics Framework
  • Exploratory Data Analysis
  • Machine Learning
  • Using Algorithms to Build Models
  • Market Segmentation
  • Transactional Analysis
  • Anomaly Detection
  • Estimation and Prediction
  • Classification

Course Objectives
 

Module 1 – Analytics Workflow
  • Define terms related to analytics and data science
  • Describe the analytics workflow
  • Describe common usage scenarios
  • Navigate Splunk Machine Learning Toolkit
 
Module 2 – Exploratory Data Analysis
  • Describe the purpose of data exploration
  • Identify SPL commands for data exploration
  • Split data for testing and training using the sample command
 
Module 3 – Predict Numeric Fields with Regression
  • Differentiate predictions from estimates
  • Identify prediction algorithms and assumptions
  • Describe the fit and apply commands
  • Model numeric predictions in the MLTK and Splunk Enterprise
  • Use the score command to evaluate models
 
Module 4 – Clean and Preprocess the Data
  • Define preprocessing and describe its purpose
  • Describe algorithms that preprocess data for use in models
  • Use FieldSector to choose relevant fields
  • Use PCA and ICA to reduce dimensionality
  • Normalize data with StandardScaler and RobustScaler
  • Preprocess text using Imputer, and NPR, TF-IDF, HashingVectorizer and the cluster command
 
Module 5 – Cluster Data
  • Define Clustering
  • Identify clustering methods, algorithms, and use cases
  • Use Smart Clustering Assistant to cluster data
  • Evaluate clusters using silhouette score
  • Validate cluster coherence
  • Describe clustering best practices
 
Module 6  – Anomaly Detection
  • 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
  • Optimize anomaly detection with Local Outlier Factor
  • View results with the Distribution Plot visualization
Module 7 – Estimation and Prediction
  • Differentiate predictions from forecasts
  • Use the Smart Forecasting Assistant
  • Use the StateSpaceForecast algorithm
  • Forecast multivariate data
  • Account for periodicity in each time series
Module 8 – Classification
  • Define key classification terms
  • Use classification algorithms
  • AutoPrediction
  • LogisticRegression
  • SVM (Support Vector Machines)
  • RandomForestClassifier
  • Evaluate classifier tradeoffs
  • Evaluate results of multiple algorithms
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