Splunk for Analytics and Data Science

Popis kurzu

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.

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.

Obsah kurzu

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

Předpoklady

• Splunk Fundamentals 1


• Splunk Fundamentals 2
• Advanced Searching & Reporting (strongly recommended)
• 
  Advanced Dashboards & Visualizations

Studijní materiály