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Classroom Contents
How to Make Sure Your Data Science Isn't Vulnerable to Attack
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- 1 Intro
- 2 What is data science
- 3 Data exploration preparation principles
- 4 Applying the algorithms
- 5 Communication
- 6 Machine Learning
- 7 Strong Foundations
- 8 Measure
- 9 Naming conventions
- 10 Detect
- 11 Analysis
- 12 New detection
- 13 New vulnerabilities
- 14 Metadata
- 15 Data quirks
- 16 Check new detection
- 17 How to communicate
- 18 Data flow in infosec
- 19 Balance between caveats and usability
- 20 Different perspectives on the data
- 21 Vulnerability assets
- 22 Actual insight
- 23 Actionable insight
- 24 Beyond your data set
- 25 Other data sets
- 26 Get more data
- 27 The takeaway
- 28 Importance of basic statistics
- 29 Common mistakes
- 30 Lessons learned
- 31 Measuring risk
