Exploring and Analyzing Data with SplunkEADSSPSplunkSP-EADS1.0<ul>
<li>Analytics Framework</li><li>Exploring and visualizing data</li><li>Cleaning and Preprocessing Data</li><li>Numerical and String based clustering</li><li>Data Correlation</li><li>Meta Transactions</li><li>Detecting Anomalies</li><li>Forecasting</li></ul><p>To be successful, students should have a solid understanding of the following courses:
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<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 and 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></ul><h4>Topic 1 – What is Data Science</h4><ul>
<li>Define terms related to analytics and data science</li><li>Describe the analytics workflow</li><li>Describe Artificial Intelligence and Machine Learning</li><li>Examine common Machine Learning myths</li><li>Describe Splunk’s Machine Learning tools</li></ul><h4>Topic 2 – Exploratory Data Analysis</h4><ul>
<li>Use bin and makecontinuous to restructure and visualize data</li><li>Examine field statistics with fieldsummary</li><li>Transform fields with eval and fillnull</li><li>Clean text with the rex and cleantext commands</li><li>Solve Anscombe’s Quartet</li><li>Apply boxplots and 3d scatterplots to visualize data</li></ul><h4>Topic 3 – Event Clustering</h4><ul>
<li>Take a behavioral based approach to cluster data</li><li>Cluster numerical fields using the kmeans command</li><li>Cluster based of string similarity with the cluster command</li><li>Find patterns in clusters</li></ul><h4>Topic 4– Correlations and Transactions</h4><ul>
<li>Define correlation and co-occurrence</li><li>Use SPL correlation commands</li><li>Use the statistical tests from the Machine Learning Toolkit to correlate fields</li><li>Use streamstats and chart commands to correlate data</li></ul><h4>Topic 5– Anomaly Detection</h4><ul>
<li>Define Statistical Outliers</li><li>Use Add-hoc methods of numerical anomaly detection</li><li>Find numerical or categorical anomalies with the AnomalyDetection command</li></ul><h4>Topic 6 – Forecasting</h4><ul>
<li>Define forecasting use cases</li><li>Use the predict command to forecast future timeseries</li></ul>- Analytics Framework
- Exploring and visualizing data
- Cleaning and Preprocessing Data
- Numerical and String based clustering
- Data Correlation
- Meta Transactions
- Detecting Anomalies
- ForecastingTo be successful, students should have a solid understanding of the following courses:
- Intro to Splunk
- Using Fields (SUF)
- Scheduling Reports and 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)Topic 1 – What is Data Science
- Define terms related to analytics and data science
- Describe the analytics workflow
- Describe Artificial Intelligence and Machine Learning
- Examine common Machine Learning myths
- Describe Splunk’s Machine Learning tools
Topic 2 – Exploratory Data Analysis
- Use bin and makecontinuous to restructure and visualize data
- Examine field statistics with fieldsummary
- Transform fields with eval and fillnull
- Clean text with the rex and cleantext commands
- Solve Anscombe’s Quartet
- Apply boxplots and 3d scatterplots to visualize data
Topic 3 – Event Clustering
- Take a behavioral based approach to cluster data
- Cluster numerical fields using the kmeans command
- Cluster based of string similarity with the cluster command
- Find patterns in clusters
Topic 4– Correlations and Transactions
- Define correlation and co-occurrence
- Use SPL correlation commands
- Use the statistical tests from the Machine Learning Toolkit to correlate fields
- Use streamstats and chart commands to correlate data
Topic 5– Anomaly Detection
- Define Statistical Outliers
- Use Add-hoc methods of numerical anomaly detection
- Find numerical or categorical anomalies with the AnomalyDetection command
Topic 6 – Forecasting
- Define forecasting use cases
- Use the predict command to forecast future timeseries9 heures1000.00835.001000.001000.001000.001380.001100.00100.00100.00100.00100.00100.00100.00100.00