Splunk for Analytics & Data Science

Duration : 2 Days (16 Hours)

Splunk for Analytics & Data Science Course Overview:

This course is designed for users aiming to achieve Operational Intelligence Level 4, focusing on gaining business insights. It covers the implementation of analytics and data science projects using Splunk’s statistics, machine learning, and both built-in and custom visualization capabilities.

Intended Audience:

  • Data Scientists
  • Business Analysts
  • Splunk Administrators
  • Machine Learning Practitioners
  • Splunk Power Users
  • IT Professionals
  • Anyone seeking to leverage Splunk for analytics and data science.

Learning Objectives of Splunk for Analytics & Data Science:

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

Topic 1: Analytics Workflow

  • Defining terms related to analytics and data science
  • Describing the analytics workflow
  • Explaining common usage scenarios
  • Navigating Splunk Machine Learning Toolkit

Topic 2: Exploratory Data Analysis

  • Describing the purpose of data exploration
  • Identifying SPL commands for data exploration
  • Splitting data for testing and training using the sample command

Topic 3: Predict Numeric Fields with Regression

  • Differentiating predictions from estimates
  • Identifying prediction algorithms and assumptions
  • Describing the fit and apply commands
  • Modeling numeric predictions in the MLTK and Splunk Enterprise
  • Using the score command to evaluate models

Topic 4: Clean and Preprocess the Data

  • Defining preprocessing and describing its purpose
  • Describing algorithms for data preprocessing
  • Using FieldSelector to choose relevant fields
  • Using PCA and ICA to reduce dimensionality
  • Normalizing data with StandardScaler and RobustScaler
  • Preprocessing text using Imputer, NPR, TF-IDF, HashingVectorizer, and the cluster command

Topic 5: Cluster Data

  • Defining clustering
  • Identifying clustering methods, algorithms, and use cases
  • Using Smart Clustering Assistant to cluster data
  • Evaluating clusters using silhouette score
  • Validating cluster coherence
  • Describing clustering best practices

Topic 6: Anomaly Detection

  • Defining anomaly detection and outliers
  • Identifying anomaly detection use cases
  • Using Splunk Machine Learning Toolkit Smart Outlier Assistant
  • Detecting anomalies using the Density Function algorithm
  • Optimizing anomaly detection with the Local Outlier Factor
  • Viewing results with the Distribution Plot visualization

Topic 7: Estimation and Prediction

  • Differentiating predictions from forecasts
  • Using the Smart Forecasting Assistant
  • Using the StateSpaceForecast algorithm
  • Forecasting multivariate data
  • Accounting for periodicity in each time series

Topic 8: Classification

  • Defining key classification terms
  • Using classification algorithms, including AutoPrediction, LogisticRegression, SVM (Support Vector Machines), and RandomForestClassifier
  • Evaluating classifier tradeoffs
  • Evaluating results of multiple algorithms

To excel in this course, students should have a solid understanding of the following prerequisites:

From the Fundamentals Series:

  • Splunk Fundamentals 1
  • Splunk Fundamentals 2
  • Splunk Fundamentals 3

Alternatively, students should have a comprehensive grasp of 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

Discover the perfect fit for your learning journey

Choose Learning Modality

Live Online

  • Convenience
  • Cost-effective
  • Self-paced learning
  • Scalability


  • Interaction and collaboration
  • Networking opportunities
  • Real-time feedback
  • Personal attention


  • Familiar environment
  • Confidentiality
  • Team building
  • Immediate application

Training Exclusives

This course comes with following benefits:

  • Practice Labs.
  • Get Trained by Certified Trainers.
  • Access to the recordings of your class sessions for 90 days.
  • Digital courseware
  • Experience 24*7 learner support.

Got more questions? We’re all ears and ready to assist!

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