1. Mathematical & Statistical Machine Learning

Label	KSAC Description		Bloom's Taxonomy Level	Cross-Cutting
a	Explain core statistical inference concepts (for example, deriving relevant hypotheses, evaluating the hypotheses, and prediction with uncertainty)	knowledge	2	Data Analytics 5b
b	Explain and demonstrate how differences in data and desired outcomes impact the appropriateness of data analysis techniques (e.g., descriptive vs. diagnostic vs. predictive vs. prescriptive)	knowledge	2	Generalist 1e, Data Analytics 5c
c	Explain core probability concepts (e.g., random variables, common distributions, conditional probability, Bayes theorem)	knowledge	2	Data Analytics 5e
d	Describe when and why one should use Machine Learning (compared to other techniques)	knowledge	2
e	Describe the limitations of machine learning	knowledge	2
f	Identify and describe several SDLC models (e.g. waterfall, Agile)	knowledge	2	Data Analytics 7c
g	Apply principles of matrix algebra to linear transformations	skill	3
h	Translate data into mathematical vectors	ability	3
i	Demonstrate an understanding of cloud architecture and the capabilities of services such as AWS, Azure, IBM, Oracle and Google	knowledge	2	Cyber Security 4d, Networking 1a, Software Development 1g, Data Management 1p