SPLK-ANDATSC - Splunk 8.0 for Analytics and Data Science

SPLK-ANDATSC - Splunk 8.0 for Analytics and Data Science

SPLK-ANDATSC - Splunk 8.0 for Analytics and Data Science

Overview

Duration: 3.0 days

This 13.5 hour certification & training course is for users who want to attain operational intelligence level 4, (business insights) and covers implementing analytics and data science projects using Splunk's statistics, machine learning, built-in and custom visualization capabilities.

Objectives

Please refer to course overview

Content

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

Audience

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Prerequisites

Splunk Fundamentals 1
Splunk Fundamentals 2
Splunk Fundamentals 3
or equivalent Splunk experience

Certification

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