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
V angličtině
