One of the primary challenges in data science training is dealing with inconsistencies and missing values in datasets. This can arise due to errors in data collection, storage, or processing. To address this challenge, data scientists must employ techniques such as data cleaning, imputation, and outlier detection to ensure data quality and reliability.