Explore KSACs by Pathway
Explore Knowledge, Skills, Abilities, and Credentials (KSACs) by IT Pathway.
In late 2025, the Partnership pulled together members of industry from across tech to share how the in-demand skillsets for entry-level roles in their fields have changed in the last two years. The KSACs below reflect their feedback on entry-level expectations, especially in a tech workplace increasingly shaped by AI.
1. Mathematical & Statistical Machine Learning
| Label | KSAC Description | Bloom's Taxonomy Level | |
|---|---|---|---|
| a | Explain core statistical inference concepts (for example, deriving relevant hypotheses, evaluating the hypotheses, and prediction with uncertainty). | knowledge | 2 |
| 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 |
| c | Explain core probability concepts (e.g., random variables, key distributions, conditional probability, Bayes theorem). | knowledge | 2 |
| 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 | Apply principles of matrix algebra to linear transformations. | skill | 3 |
| g | Translate textual data into mathematical vectors. | ability | 3 |
| h | Demonstrate an understanding of cloud architecture and the capabilities of services such as AWS, Azure, IBM, Oracle and Google. | knowledge | 2 |
| i | Explain why training data must reflect real-world dynamics for machine learning models to perform as intended | knowledge | 2 |