Recommended Path: Data Scientist
This article describes our Recommended Data Scientist Path.
Our Data Scientist Path was designed for individuals who work in R to develop data processing pipelines, prepare analytical applications, design architecture, and create models for machine learning. Upon completing our learning paths, the Data Scientist Learner will be able to utilize secure coding principles within the SDLC to design secure applications while working in R.
The Data Scientist training content is organized into three progressive levels:
- Data Scientist Foundational: Introduces the basics of application security, such as the different types of security vulnerabilities, the importance of secure coding practices, and the secure development lifecycle.
- Data Scientist Intermediate: A technical deep dive into the threats and security controls relevant to data scientists, including OWASP Top 10, threat modeling, and security testing.
- Data Scientist Advanced Path: Learners choose their language/technology/framework to move into more advanced topics with the opportunity to learn how to break and fix code in a real application environment.
- R
Foundational: Data Scientist
Video Only | Total Learning Path Duration: 4 hours and 14 minutes
Introduction to Security Journey
Privacy and Customer Data Protection
Six Foundational Truths of Application Security
Intermediate: Data Scientist
Video and Hands-on | Total Learning Path Duration: 4 hours and 53 minutes
Secure Design Principles | Part 1
Secure Design Principles | Part 2
Logging and Exception Handeling
Cross-Site Scripting (XSS) | Part 1
Cross-Site Scripting (XSS) | Part 2
Static Application Security Testing (SAST)
Static Application Security Testing (SAST) (HackEDU)
Dynamic Application Security Testing (DAST)
Dynamic Application Security Testing (DAST) (HackEDU)
Penetration Testing and Bug Bounty
Advanced: R (Data Scientist)
Video and Hands-on | Total Learning Path Duration: 5 hours and 23 minutes
Designing a Secure App or Product
Secure Design Principles | Part 1
Secure Design Principles | Part 2
Producing Clean, Maintainable, and Secure Code
Secure Coding Best Practices: Part 1
Secure Coding Best Practices: Part 2
Securing the Development Environment
Protecting your Code Repository
Security Best Practices for R | Part 1
Security Best Practices for R | Part 2