Splunk for Analytics and Data Science

Splunk for Analytics and Data ScienceSADSSPSplunkSP-SADS8.0<ul> <li>Analytics Framework</li><li>Regression for Prediction</li><li>Cleaning and Preprocessing Data</li><li>Algorithms, Preprocessing and Feature Extrac7tion</li><li>Clustering Data</li><li>Detecting Anomalies</li><li>Forecasting</li><li>Classification</li></ul><p>To be successful, students should have a solid understanding of the following courses:</p> <ul> <li>Intro to Splunk</li><li><span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/fr/course/splunk-suf"><svg role="img" aria-hidden="true" focusable="false" data-nosnippet class="cms-linkmark"><use xlink:href="/css/img/icnset-linkmarks.svg#linkmark"></use></svg>Using Fields <span class="fl-prod-pcode">(SUF)</span></a></span></li><li>Scheduling Reports & Alerts</li><li>Visualizations</li><li><span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/fr/course/splunk-wwt"><svg role="img" aria-hidden="true" focusable="false" data-nosnippet class="cms-linkmark"><use xlink:href="/css/img/icnset-linkmarks.svg#linkmark"></use></svg>Working with Time <span class="fl-prod-pcode">(WWT)</span></a></span></li><li><span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/fr/course/splunk-ssp"><svg role="img" aria-hidden="true" focusable="false" data-nosnippet class="cms-linkmark"><use xlink:href="/css/img/icnset-linkmarks.svg#linkmark"></use></svg>Statistical Processing <span class="fl-prod-pcode">(SSP)</span></a></span></li><li><span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/fr/course/splunk-scv"><svg role="img" aria-hidden="true" focusable="false" data-nosnippet class="cms-linkmark"><use xlink:href="/css/img/icnset-linkmarks.svg#linkmark"></use></svg>Comparing Values <span class="fl-prod-pcode">(SCV)</span></a></span></li><li><span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/fr/course/splunk-srm"><svg role="img" aria-hidden="true" focusable="false" data-nosnippet class="cms-linkmark"><use xlink:href="/css/img/icnset-linkmarks.svg#linkmark"></use></svg>Result Modification <span class="fl-prod-pcode">(SRM)</span></a></span></li><li><span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/fr/course/splunk-lls"><svg role="img" aria-hidden="true" focusable="false" data-nosnippet class="cms-linkmark"><use xlink:href="/css/img/icnset-linkmarks.svg#linkmark"></use></svg>Leveraging Lookups and Subsearches <span class="fl-prod-pcode">(LLS)</span></a></span></li><li><span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/fr/course/splunk-sclas"><svg role="img" aria-hidden="true" focusable="false" data-nosnippet class="cms-linkmark"><use xlink:href="/css/img/icnset-linkmarks.svg#linkmark"></use></svg>Correlation Analysis <span class="fl-prod-pcode">(SCLAS)</span></a></span></li><li>Search Under the Hood</li><li>Intro to Knowledge Objects</li><li><span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/fr/course/splunk-cfe"><svg role="img" aria-hidden="true" focusable="false" data-nosnippet class="cms-linkmark"><use xlink:href="/css/img/icnset-linkmarks.svg#linkmark"></use></svg>Creating Field Extractions <span class="fl-prod-pcode">(CFE)</span></a></span></li><li><span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/fr/course/splunk-sso"><svg role="img" aria-hidden="true" focusable="false" data-nosnippet class="cms-linkmark"><use xlink:href="/css/img/icnset-linkmarks.svg#linkmark"></use></svg>Search Optimization <span class="fl-prod-pcode">(SSO)</span></a></span></li><li><span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/fr/course/splunk-eads"><svg role="img" aria-hidden="true" focusable="false" data-nosnippet class="cms-linkmark"><use xlink:href="/css/img/icnset-linkmarks.svg#linkmark"></use></svg>Exploring and Analyzing Data with Splunk <span class="fl-prod-pcode">(EADS)</span></a></span></li></ul><p><strong>Topic 1 – Analytics Workflow</strong></p> <ul> <li>Define terms related to analytics and data science</li><li>Describe the analytics workflow</li><li>Describe common usage scenarios</li><li>Navigate Splunk Machine Learning Toolkit</li></ul><p><strong>Topic 2 – Training and Testing Models</strong></p> <ul> <li>Split data for testing and training using the sample command</li><li>Describe the fit and apply commands</li><li>Use the score command to evaluate models</li></ul><p><strong>Topic 3 – Regression: Predict Numerical Values</strong></p> <ul> <li>Differentiate predictions from estimates</li><li>Identify prediction algorithms and assumptions</li><li>Model numeric predictions in the MLTK and Splunk Enterprise</li></ul><p><strong>Topic 4 – Clean and Preprocess the Data</strong></p> <ul> <li>Define preprocessing and describe its purpose</li><li>Describe algorithms that preprocess data for use in models</li><li>Use FieldSelector to choose relevant fields</li><li>Use PCA and ICA to reduce dimensionality</li><li>Normalize data with StandardScaler and RobustScaler</li><li>Preprocess text using Imputer, NPR, TF-IDF, and HashingVectorizer</li></ul><p><strong>Topic 5 – Clustering</strong></p> <ul> <li>Define Clustering</li><li>Identify clustering methods, algorithms, and use cases</li><li>Use Smart Clustering Assistant to cluster data</li><li>Evaluate clusters using silhouette score</li><li>Validate cluster coherence</li><li>Describe clustering best practices</li></ul><p><strong>Topic 6 – Forecasting Fields</strong></p> <ul> <li>Differentiate predictions from forecasts</li><li>Use the Smart Forecasting Assistant</li><li>Use the StateSpaceForecast algorithm</li><li>Forecast multivariate data</li><li>Account for periodicity in each time series</li></ul><p><strong>Topic 7 – Detect Anomalies</strong></p> <ul> <li>Define anomaly detection and outliers</li><li>Identify anomaly detection use cases</li><li>Use Splunk Machine Learning Toolkit Smart Outlier Assistant</li><li>Detect anomalies using the Density Function algorithm</li><li>View results with the Distribution Plot visualization</li></ul><p><strong>Topic 8 – Classify: Predict Categorical Values</strong></p> <ul> <li>Define key classification terms</li><li>Identify when to use different classification algorithms</li><li>Evaluate classifier tradeoffs</li><li>Evaluate results of multiple algorithms</li></ul>- Analytics Framework - Regression for Prediction - Cleaning and Preprocessing Data - Algorithms, Preprocessing and Feature Extrac7tion - Clustering Data - Detecting Anomalies - Forecasting - ClassificationTo be successful, students should have a solid understanding of the following courses: - Intro to Splunk - Using Fields (SUF) - Scheduling Reports & Alerts - Visualizations - Working with Time (WWT) - Statistical Processing (SSP) - Comparing Values (SCV) - Result Modification (SRM) - Leveraging Lookups and Subsearches (LLS) - Correlation Analysis (SCLAS) - Search Under the Hood - Intro to Knowledge Objects - Creating Field Extractions (CFE) - Search Optimization (SSO) - Exploring and Analyzing Data with Splunk (EADS)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 – Training and Testing Models - Split data for testing and training using the sample command - Describe the fit and apply commands - Use the score command to evaluate models Topic 3 – Regression: Predict Numerical Values - Differentiate predictions from estimates - Identify prediction algorithms and assumptions - Model numeric predictions in the MLTK and Splunk Enterprise Topic 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, NPR, TF-IDF, and HashingVectorizer Topic 5 – Clustering - 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 – Forecasting Fields - Differentiate predictions from forecasts - Use the Smart Forecasting Assistant - Use the StateSpaceForecast algorithm - Forecast multivariate data - Account for periodicity in each time series Topic 7 – Detect Anomalies - 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 - View results with the Distribution Plot visualization Topic 8 – Classify: Predict Categorical Values - Define key classification terms - Identify when to use different classification algorithms - Evaluate classifier tradeoffs - Evaluate results of multiple algorithms2 jours1500.001250.001500.001500.002070.001600.001500.00150.00150.00150.00150.00150.00150.00150.00150.00150.00