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Splunk Training + Certification

Splunk for Analytics and Data Science

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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.

 

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Instructor-led Training Schedule

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Course Prerequisites

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