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 |