Splunk for Analytics and Data Science

Instructor-led Training Schedule

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