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