SPLK-ADS: Splunk for Analytics and Data Science

Module 1: Analytics Workflow

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

Module 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

Module 3: Regression: Predict Numerical Values

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

Module 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

Module 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

Module 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

Module 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

Module 8: Classify: Predict Categorical Values

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