Splunk for Analytics and Data Science (SADS)

 

Course Content

  • Analytics Framework
  • Exploratory Data Analysis
  • Machine Learning
  • Using Algorithms to Build Models
  • Market Segmentation
  • Transactional Analysis
  • Anomaly Detection
  • Estimation and Prediction
  • Classification

Prerequisites

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

  • Fundamentals 1, 2, & 3 (Retired)
  • Advanced Searching & Reporting

Or the following single-subject modules:

Course Objectives

This 13.5-hour module 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.

Please note that this course may run over three days, with 4.5 hour sessions each day.

Outline: Splunk for Analytics and Data Science (SADS)

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 – 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

Topic 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

Topic 4 – Clean and Preprocess the Data

  • Define preprocessing and describe its purpose
  • Describe algorithms that preprocess data for use in models
  • Use FieldSector 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

Topic 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

Topic 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 Local Outlier Factor
  • View results with the Distribution Plot visualization

Topic 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

Topic 8 – Classification

  • Define key classification terms
  • Use classification algorithms
  • AutoPrediction
  • LogisticRegression
  • SVM (Support Vector Machines)
  • RandomForestClassifier
  • Evaluate classifier tradeoffs
  • Evaluate results of multiple algorithms

Prices & Delivery methods

Online Training

Duration
3 days

Price
  • Online Training: CAD 1,905
  • Online Training: US$ 1,500
  • Splunk Training Credits: 150 SPC
Dates and Booking
Enquire a date
Classroom Training

Duration
3 days

Price
  • Canada: CAD 1,905
  • Splunk Training Credits: 150 SPC
Dates and Booking
Enquire a date

Click on town name or "Online Training" to book Schedule

This is an Instructor-Led Classroom course
Instructor-led Online Training:   This computer icon in the schedule indicates that this date/time will be conducted as Instructor-Led Online Training.
*   This class is delivered by a partner.

United States

 Online Training 09:00 US/Eastern * Enroll
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