Explore KSACs by 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 Knowledge, Skills & Abilities and Credentials (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 |