Explore KSACs by Pathway

Explore Knowledge, Skills, Abilities, and Credentials (KSACs) by IT Pathway.

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