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.
4. Application of Machine Learning Models & Algorithms
| Label | KSAC Description | Bloom's Taxonomy Level | |
|---|---|---|---|
| a | Explain evaluation metrics for machine learning algorithms (e.g., accuracy, precision/recall, ROC curves, R^2). | knowledge | 2 |
| b | Identify the role of computational resources (e.g., CPUs, GPUs, memory, scalability) in training and deploying machine learning models | knowledge | 2 |
| c | Describe approaches to test for bias in data. | knowledge | 2 |
| d | Explain key troubleshooting techniques for machine learning algorithms (e.g., evaluate bias-variance tradeoff, use cross-validation). | knowledge | 2 |
| e | Explain how data quality and representativeness impact model performance and deployment outcomes | knowledge | 2 |
| f | Explain sampling methods with respect to different applications, i.e. error estimates, surveys, A/B-testing. | knowledge | 2 |
| g | Train a machine learning model and use it to make predictions. | skill | 3 |
| h | Perform data manipulation using appropriate tools and software. | skill | 3 |
| i | Provide rationale for selecting the appropriate sampling methodology. | skill | 3 |
| j | Use AI-assisted tools responsibly to support model development, troubleshooting, and interpretation while knowing when outputs must be verified | skill | 2 |