Splunk 8.0 for Analytics and Data Science

Splunk 8.0 for Analytics and Data Science

Splunk 8.0 for Analytics and Data Science

Upcoming Classes

Online

Instructor-led online training

Location Aug 2021 Sep 2021 Oct 2021 Nov 2021 Dec 2021 Jan 2022 Feb 2022
AMER Eastern Time - Virtual Aug 11 – Aug 13
 Sep 15 – Sep 17
 Oct 13 – Oct 15
 Nov 17 – Nov 19
 Dec 15 – Dec 17
 Jan 5 – Jan 7
EMEA UK Time - Virtual Aug 25 – Aug 27
 Sep 29 – Oct 1
 Nov 15 – Nov 17
 Jan 5 – Jan 7
AMER Pacific Time - Virtual Aug 25 – Aug 27
 Sep 29 – Oct 1
 Oct 27 – Oct 29
 Dec 1 – Dec 3
 Jan 19 – Jan 21

Summary

This 13.5 hour 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.

Description

  • Analytics Framework
  • Exploratory Data Analysis
  • Regression for Prediction
  • Cleaning and Preprocessing and Feature Extraction
  • Algorithms, Preprocessing and Feature Extraction
  • Clustering Data
  • Detecting Anomalies
  • Forecasting
  • Classification

Duration

3 Days

Objectives

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 StandardScaler and RobustScaler
    • Preprocess text using Imputer, and NPR, TF-IDF, HashingVectorizer 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
      • AutoPrediction
      • LogisticRegression
      • SVM (Support Vector Machines)
      • RandomForestClassifier
    • Evaluate classifier tradeoffs
    • Evaluate results of multiple algorithms

Prerequisites

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

Onsite Training

For groups of three or more

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Public Training

AMER Eastern Time - Virtual

EMEA UK Time - Virtual

AMER Pacific Time - Virtual


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