Python for Data Science Questions

Practical proficiency in Python for data analysis and machine learning. Core skills include the NumPy library and Pandas dataframes for vectorized operations and memory efficient manipulation of large datasets, merging grouping and time series handling, and implementing feature engineering pipelines. Ability to implement reproducible training workflows with reliable data input and output, model serialization, experiment logging, and result versioning. Write clean modular code with functions and classes, unit tests, error handling, and readable documentation. Performance awareness includes profiling, algorithmic complexity analysis, use of efficient data structures, chunking strategies, parallelization, and integration with compiled libraries when necessary. Familiarity with common tooling and interactive workflows such as virtual environments, package management, and development notebooks.