Splunk 8.0 for Analytics and Data Science
Upcoming Classes
Online
Instructor-led online training
Location | May 2022 | Jun 2022 | Jul 2022 | Aug 2022 | Sep 2022 | Oct 2022 | Nov 2022 |
---|---|---|---|---|---|---|---|
AMER Pacific Time - Virtual |
May 25 – May 27 |
Jun 29 – Jul 1 |
Jul 27 – Jul 29 | ||||
EMEA UK Time - Virtual |
Jun 1 – Jun 3 Jun 29 – Jul 1 | ||||||
AMER Eastern Time - Virtual |
Jun 1 – Jun 3 Jun 22 – Jun 24 |
Jul 11 – Jul 13 |

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
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
- 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
- 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
- Differentiate predictions from forecasts
- Use the Smart Forecasting Assistant
- Use the StateSpaceForecast algorithm
- Forecast multivariate data
- Account for periodicity in each time series
- Define key classification terms
- Use classification algorithms
- AutoPrediction
- LogisticRegression
- SVM (Support Vector Machines)
- RandomForestClassifier
- Evaluate classifier tradeoffs
- Evaluate results of multiple algorithms