5. Probability and Descriptive and Inferential Statistics

Label	KSAC Description		Bloom's Taxonomy Level	Cross-Cutting
a	Demonstrate knowledge of probability and standard statistical distributions	knowledge	1	Generalist 2a
b	Explain core statistical inference concepts (for example, deriving relevant hypotheses, evaluating the hypotheses, and prediction with uncertainty)	knowledge	2	Machine Learning 1a
c	Differentiate among data analytic approaches (e.g., descriptive vs. diagnostic vs. predictive vs. prescriptive analytics)	knowledge	2	Generalist 1e, Machine Learning 1b
d	Demonstrate and explain the role and importance of model validation and accuracy metrics in analytics projects, hypothesis testing, and information retrieval	knowledge	2	Generalist 2c
e	Explain core probability concepts (e.g., random variables, key distributions, conditional probability, Bayes theorem)	knowledge	2	Machine Learning 1c
f	Explain sampling methods (for example, stratified sampling, simple random sampling, and cluster sampling)	knowledge	2
g	Articulate the limits of statistical inference and statistical measurement	knowledge	2
h	Interpret and explain results from analysis based on the data analytics problem statement	skill	3
i	Choose appropriate statistical methods and apply towards data analysis	skill	3
j	Provide rationale for selecting the appropriate sampling methodology	skill	3	Machine Learning 4g
k	Demonstrate the ability to develop question sets that lead to actionable analytics	ability	3
l	Draw insights from results of analysis in the context of the original problem	ability	3