Course Description

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.

Instructor-led Training Schedule
 Start Date  Start Time  Time Zone
19-Mar-18 09:00 AM (GMT-07:00) Arizona
26-Mar-18 09:00 AM (GMT+01:00) Brussels, Copenhagen, Madrid, Paris
28-Mar-18 09:00 AM (GMT-07:00) Arizona
02-Apr-18 09:00 AM (GMT-07:00) Arizona
09-Apr-18 09:00 AM (GMT-07:00) Arizona
View Schedule

Course Prerequisites

  • Splunk Fundamentals 1
  • Splunk Fundamentals 2
  • Advanced Searching and Reporting with Splunk
  • or equivalent Splunk experience

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 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
  • Fit and apply models in Splunk
  • 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