Python 3.14 (Coding): Top Profitable Products Identification Test

To assess fresh graduate candidates' data science skills, it is important to test their understanding of statistical concepts, including probability theory, hypothesis testing, and regression analysis. Additionally, candidates should be proficient in programming languages such as Python and have experience with data preprocessing techniques such as data cleaning, feature engineering, and data normalization. Candidates should also be familiar with data visualization tools and techniques to communicate insights and findings effectively. Finally, candidates should have knowledge of supervised and unsupervised machine learning techniques, including classification, regression, clustering, and dimensionality reduction. By evaluating these skills, employers can gauge a candidate's data science competency and potential to succeed in data-driven roles.