CVF 1072 - Machine Learning with Big Data Technology

1. Analytics Framework
2. Exploratory Data Analysis
3. Overview of Machine Learning
4. Market Segmentation
5. Transactional Analysis
6. Anomaly Detection
7. Estimation and Prediction
8. Classification
9. Data Visualization
10. Splunk IT Service Intelligence Advanced Analytics
11. Splunk User Behavior Analytics
12. Custom Machine Learning with Splunk
13. Modeling Use Cases with Splunk

1. define terms related to analytics and data science.
2. describe the framework for multi-departmental analytics projects.
3. identify analytics project best practices.
4. identify common use cases.
5. define exploratory data analysis.
6. identify exploratory data analysis use cases.
7. describe Splunk exploratory data analysis solutions.
8. define fundamental concepts and terms associated with machine learning.
9. model data using machine learning.
10. split data for training and testing models.
11. define market segmentation.
12. identify market segmentation use cases.
13. describe Splunk market segmentation solutions.
14. define Transactional Analysis.
15. identify Transactional Analysis use cases.
16. describe Transactional Analysis solutions.
17. define anomaly detection.
18. identify anomaly detection use cases.
19. describe Splunk anomaly detection solutions.
20. define estimation and prediction.
21. identify estimation and prediction use cases.
22. describe Splunk estimation and prediction solutions.
23. describe why classification is key to analytics.
24. define key classification terms.
25. evaluate classifier results.
26. describe why data visualization is key to analytics.
27. optimize Splunk visualizations for specific data and personas.
28. enrich machine data with structured business data.
29. prep data for advanced analytics using Splunk.
30. leverage packaged machine learning (Splunk) to identify root cause of service disruptions.
31. deploy machine learning using Splunk IT Service Intelligence.
32. detect insider threats using Splunk User Behavior Analytics with built-in machine learning .
33. establish baseline for normal operational patterns and use statistical measurements to determine threshold variability patterns.
34. dynamically adapt thresholds to changing behavior and highlight anomalous activity.
35. combine event data with advanced analytics to reduce event clutter, false positives, and extensive rules maintenance.
36. implement automated detection of insider threats and external attacks.
37. identify deviations across multiple entities-users, devices, and applications-by comparing them against an entity’s baseline and its dynamically generated peer groups.
38. provide visualization of statistical aggregates across multiple entities along with enriched kill-chain visualization of a threat vector.
39. administer model building, validation, and deployment.
40. provide interactive examples for typical IT, security, business process, and IoT (Internet of Things) use cases
41. build models using any of the 300+ algorithms accessible through the ML Toolkit.
42. Use Splunk to automatically detect anomalies and patterns in data to help investigators identify and resolve incidents.
43. identify normal data patterns in varying levels of detail in order to alert only on abnormal conditions for a specific set of circumstances.
44. identify patterns of activity to anticipate and react to circumstances that might otherwise disrupt operations or revenues such as proactive maintenance.
45. apply machine learning analysis to historical data and models to forecast demand, manage inventory, optimize operations, and react to changing conditions.​

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