Splunk for Analytics and Data Science (SADS)

Voraussetzungen

To be successful, students should have a solid understanding of the following courses:

• Intro to Splunk
• Using Fields (SUF)
• Scheduling Reports & Alerts
• Visualizations
• Working with Time (WWT)
• Statistical Processing (SSP)
• Comparing Values (SCV)
• Result Modification (SRM)
• Leveraging Lookups and Subsearches (LLS)
• Correlation Analysis (SCLAS)
• Search Under the Hood
• Intro to Knowledge Objects
• Creating Field Extractions (CFE)
• Search Optimization (SSO)
• Exploring and Analyzing Data with Splunk (EADS)

Detaillierter Kursinhalt

Topic 1 – Analytics Workflow

• Define terms related to analytics and data science
• Describe the analytics workflow
• Describe common usage scenarios
• Navigate Splunk Machine Learning Toolkit

Topic 2 – Training and Testing Models

• Split data for testing and training using the sample command
• Describe the fit and apply commands
• Use the score command to evaluate models

Topic 3 – Regression: Predict Numerical Values

• Differentiate predictions from estimates
• Identify prediction algorithms and assumptions
• Model numeric predictions in the MLTK and Splunk Enterprise

Topic 4 – Clean and Preprocess the Data

• Define preprocessing and describe its purpose
• Describe algorithms that preprocess data for use in models
• Use FieldSelector to choose relevant fields
• Use PCA and ICA to reduce dimensionality
• Normalize data with StandardScaler and RobustScaler
• Preprocess text using Imputer, NPR, TF-IDF, and HashingVectorizer

Topic 5 – Clustering

• Define Clustering
• Identify clustering methods, algorithms, and use cases
• Use Smart Clustering Assistant to cluster data
• Evaluate clusters using silhouette score
• Validate cluster coherence
• Describe clustering best practices

Topic 6 – Forecasting Fields

• Differentiate predictions from forecasts
• Use the Smart Forecasting Assistant
• Use the StateSpaceForecast algorithm
• Forecast multivariate data
• Account for periodicity in each time series

Topic 7 – Detect Anomalies

• Define anomaly detection and outliers
• Identify anomaly detection use cases
• Use Splunk Machine Learning Toolkit Smart Outlier Assistant
• Detect anomalies using the Density Function algorithm
• View results with the Distribution Plot visualization

Topic 8 – Classify: Predict Categorical Values

• Define key classification terms
• Identify when to use different classification algorithms
• Evaluate classifier tradeoffs
• Evaluate results of multiple algorithms