SPLK-ANDATSC - Splunk 8.0 for Analytics and Data Science

SPLK-ANDATSC - Splunk 8.0 for Analytics and Data Science

SPLK-ANDATSC - Splunk 8.0 for Analytics and Data Science


Duration: 3.0 days

This 13.5 hour certification & training 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.


Please refer to course overview


Module 1 - Analytics Workflow

  • Define terms related to analytics and data science
  • Define 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 FieldSelector to choose relevant fields
    • Use PCA and ICA to reduce dimensionality
    • Normalize data with Standard Scaler and Robust Scaler
    • Preprocess text using Imputer, and NPR, TF-IDF, Hashing Vectorizer 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 the 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
    • Auto Prediction
    • Logistic Regression
    • SVM (Support Vector Machines)
    • Random Forest Classifier
  • Evaluate classifier tradeoffs
  • Evaluate results of multiple algorithms




  • Splunk Fundamentals 1
  • Splunk Fundamentals 2
  • Splunk Fundamentals 3
  • or equivalent Splunk experience


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