{"course":{"productid":25918,"modality":1,"active":true,"language":"de","title":"Splunk for Analytics and Data Science","productcode":"SADS","vendorcode":"SP","vendorname":"Splunk","fullproductcode":"SP-SADS","courseware":{"has_ekit":false,"has_printkit":true,"language":""},"url":"https:\/\/portal.flane.ch\/course\/splunk-sads","objective":"<ul>\n<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>","essentials":"<p>To be successful, students should have a solid understanding of the following courses:<\/p>\n<ul>\n<li>Intro to Splunk<\/li><li><span class=\"cms-link-marked\"><a class=\"fl-href-prod\" href=\"\/swisscom\/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 &amp; Alerts<\/li><li>Visualizations<\/li><li><span class=\"cms-link-marked\"><a class=\"fl-href-prod\" href=\"\/swisscom\/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\/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\/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\/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\/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\/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\/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\/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\/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>","outline":"<p><strong>Topic 1 &ndash; Analytics Workflow<\/strong><\/p>\n<ul>\n<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 &ndash; Training and Testing Models<\/strong><\/p>\n<ul>\n<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 &ndash; Regression: Predict Numerical Values<\/strong><\/p>\n<ul>\n<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 &ndash; Clean and Preprocess the Data<\/strong><\/p>\n<ul>\n<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 &ndash; Clustering<\/strong><\/p>\n<ul>\n<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  &ndash;  Forecasting Fields<\/strong><\/p>\n<ul>\n<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 &ndash; Detect Anomalies<\/strong><\/p>\n<ul>\n<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 &ndash; Classify: Predict Categorical Values<\/strong><\/p>\n<ul>\n<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>","summary":"<p>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&#039;s statistics, machine learning, built-in and custom visualization capabilities.<\/p>\n<p><strong>Please note that this course may run over three days, with 4.5 hour sessions each day.<\/strong><\/p>","objective_plain":"- Analytics Framework\n- Regression for Prediction\n- Cleaning and Preprocessing Data\n- Algorithms, Preprocessing and Feature Extrac7tion\n- Clustering Data\n- Detecting Anomalies\n- Forecasting\n- Classification","essentials_plain":"To be successful, students should have a solid understanding of the following courses:\n\n\n- Intro to Splunk\n- Using Fields (SUF)\n- Scheduling Reports & Alerts\n- Visualizations\n- Working with Time (WWT)\n- Statistical Processing (SSP)\n- Comparing Values (SCV)\n- Result Modification (SRM)\n- Leveraging Lookups and Subsearches (LLS)\n- Correlation Analysis (SCLAS)\n- Search Under the Hood\n- Intro to Knowledge Objects\n- Creating Field Extractions (CFE)\n- Search Optimization (SSO)\n- Exploring and Analyzing Data with Splunk (EADS)","outline_plain":"Topic 1 \u2013 Analytics Workflow\n\n\n- Define terms related to analytics and data science\n- Describe the analytics workflow\n- Describe common usage scenarios\n- Navigate Splunk Machine Learning Toolkit\nTopic 2 \u2013 Training and Testing Models\n\n\n- Split data for testing and training using the sample command\n- Describe the fit and apply commands\n- Use the score command to evaluate models\nTopic 3 \u2013 Regression: Predict Numerical Values\n\n\n- Differentiate predictions from estimates\n- Identify prediction algorithms and assumptions\n- Model numeric predictions in the MLTK and Splunk Enterprise\nTopic 4 \u2013 Clean and Preprocess the Data\n\n\n- Define preprocessing and describe its purpose\n- Describe algorithms that preprocess data for use in models\n- Use FieldSelector to choose relevant fields\n- Use PCA and ICA to reduce dimensionality\n- Normalize data with StandardScaler and RobustScaler\n- Preprocess text using Imputer, NPR, TF-IDF, and HashingVectorizer\nTopic 5 \u2013 Clustering\n\n\n- Define Clustering\n- Identify clustering methods, algorithms, and use cases\n- Use Smart Clustering Assistant to cluster data\n- Evaluate clusters using silhouette score\n- Validate cluster coherence\n- Describe clustering best practices\nTopic 6  \u2013  Forecasting Fields\n\n\n- Differentiate predictions from forecasts\n- Use the Smart Forecasting Assistant\n- Use the StateSpaceForecast algorithm\n- Forecast multivariate data\n- Account for periodicity in each time series\nTopic 7 \u2013 Detect Anomalies\n\n\n- Define anomaly detection and outliers\n- Identify anomaly detection use cases\n- Use Splunk Machine Learning Toolkit Smart Outlier Assistant\n- Detect anomalies using the Density Function algorithm\n- View results with the Distribution Plot visualization\nTopic  8 \u2013 Classify: Predict Categorical Values\n\n\n- Define key classification terms\n- Identify when to use different classification algorithms\n- Evaluate classifier tradeoffs\n- Evaluate results of multiple algorithms","summary_plain":"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.\n\nPlease note that this course may run over three days, with 4.5 hour sessions each day.","skill_level":"Intermediate","version":"8.0","duration":{"unit":"d","value":2,"formatted":"2 Tage"},"pricelist":{"List Price":{"US":{"country":"US","currency":"USD","taxrate":null,"price":1500},"GB":{"country":"GB","currency":"GBP","taxrate":20,"price":1250},"PL":{"country":"PL","currency":"USD","taxrate":23,"price":1500},"DE":{"country":"DE","currency":"EUR","taxrate":19,"price":1500},"CA":{"country":"CA","currency":"CAD","taxrate":null,"price":2070},"CH":{"country":"CH","currency":"CHF","taxrate":8.1,"price":1600},"NL":{"country":"NL","currency":"EUR","taxrate":21,"price":1500}}},"lastchanged":"2026-02-03T13:13:28+01:00","parenturl":"https:\/\/portal.flane.ch\/swisscom\/json-courses","nexturl_course_schedule":"https:\/\/portal.flane.ch\/swisscom\/json-course-schedule\/25918","source_lang":"de","source":"https:\/\/portal.flane.ch\/swisscom\/json-course\/splunk-sads"}}