Module 1 - Analytics Workflow
- Define terms related to analytics and data science
- Define the analytics workflow
- Describe common usage scenarios
- Navigate Splunk Machine Learning Toolkit
Module 2 - Exploratory Data Analysis
- Describe the purpose of data exploration
- Identify SPL commands for data exploration
- Split data for testing and training using the sample command
Module 3 - Predict Numeric Fields with Regression
- Differentiate predictions from estimates
- Identify prediction algorithms and assumptions
- Describe the fit and apply commands
- Model numeric predictions in the MLTK and Splunk Enterprise
- Use the score command to evaluate models
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 Standard Scaler and Robust Scaler
- Preprocess text using Imputer, and NPR, TF-IDF, Hashing Vectorizer and the cluster command
Module 5 - Cluster Data
- 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 - Anomaly Detection
- 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
- Optimize anomaly detection with the Local Outlier Factor
- View results with the Distribution Plot visualization
Module 7 - Estimation and Prediction
- Differentiate predictions from forecasts
- Use the Smart Forecasting Assistant
- Use the StateSpaceForecast algorithm
- Forecast multivariate data
- Account for periodicity in each time series
Module 8 - Classification
- Define key classification terms
- Use classification algorithms
- Auto Prediction
- Logistic Regression
- SVM (Support Vector Machines)
- Random Forest Classifier
- Evaluate classifier tradeoffs
- Evaluate results of multiple algorithms