This 13.5 hour course (3 days a 4,5 hours) 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.

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

Splunk Fundamentals 1

Splunk Fundamentals 2

Splunk Fundamentals 3

or　equivalent Splunk experience

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