The Data Science Fundamentals assessment is designed to measure the knowledge and skills of entry-level to mid-level data science professionals, evaluating their ability to apply core data science concepts and principles to address common business needs. The test covers a broad range of focus areas, including Data Preparation and Feature Engineering, Supervised Learning: Regression and Classification, Model Evaluation and Validation, Probability and Statistics for Data Analysis, Unsupervised Learning and Clustering, and Data Visualization and Communication of Findings. It assesses understanding of the full data science lifecycle, from problem definition and data collection through modeling and deployment, as well as key concepts such as overfitting, bias, variance, and the distinctions between correlation and causation.

The assessment uses a variety of question types, including Multiple Choice, Multiple Correct Answer, and Essay Composition questions, with questions drawn from a large pool to help prevent cheating. Certain questions are subject to individual time limits to further deter cheating and to enhance the predictive performance of the assessment. One or more questions are scored using artificial intelligence, ensuring consistent and objective evaluation of candidate responses.

The specific attributes measured include:

Following completion, the test produces a Candidate Selection Report that includes an overall score and detailed scores for each attribute.

The Candidate Selection Report also provides expert interview questions that help you probe critical or low-scoring areas, along with a guide for noting your evaluation of the candidate's responses. Consistent use of an interview guide is an important part of gaining better candidate insights and making better hiring decisions.

Note that while the Data Science Fundamentals assessment is a useful and efficient instrument for confirming a base level of skill or knowledge about this important topic, it is not intended to pinpoint the exact level of candidate or skill. We recommend that skills tests, like this one, be used in parallel with other measurements, such as cognitive ability, job fit, and behavioral history. Each different type of test provides a valuable piece of information you can use when evaluating potential job performance.