Explore KSACs by Pathway

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

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