Splunk for Analytics and Data Science

This course, delivered over three virtual days, covers implementing analytics and data science projects using Splunk's statistics, machine learning, built-in and custom visualization capabilities.

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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 Prerequisites

  • Using Splunk
  • Searching and Reporting with Splunk
  • Creating Splunk Knowledge Objects
  • Advanced Searching and Reporting with Splunk
  • OR equivalent Splunk experience

Class Format

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

Course Objectives

Module 1 - Analytics Framework

  • Define terms related to analytics and data science
  • Describe the framework for multi-departmental analytics projects
  • Identify analytics project best practices
  • Identify common use cases

Module 2 - Exploratory Data Analysis

  • Define exploratory data analysis
  • Describe Splunk exploratory data analysis solutions

Module 3 - Machine Learning Workflow

  • Define some concepts and terms associated with machine learning
  • Describe the machine learning workflow
  • Split data for training and testing models
  • Use Machine Learning Toolkit Showcases and Assistants

Module 4- Using Algorithms to Build Models

  • Use Machine Learning Toolkit commands and features
  • Use and compare algorithms
  • Refine models

Module 5- Market Segmentation and Transactional Analysis

  • Describe market segmentation and transactional analysis
  • Define use cases and solutions

Module 6 - Anomaly Detection

  • Define anomaly detection
  • Identify anomaly detection use cases
  • Describe Splunk anomaly detection solutions

Module 7 - Estimation and Prediction

  • Define estimation and prediction
  • Identify estimation and prediction use cases
  • Describe Splunk estimation and prediction solutions

Module 8 - Classification

  • Define key classification terms
  • Evaluate classifier results