How to Avoid Common Mistakes in Acquiring Proficiency in Machine Learning and Statistics for Data Science Career !
Overview : Common mistakes made by individuals learning machine learning and statistics for data science include: neglecting data quality, rushing into complex models without understanding fundamentals, poor feature selection, not properly validating models, overfitting to training data, ignoring data leakage, and not understanding the business context; to prevent these, focus on thorough data exploration, prioritize solid statistical foundations, practice proper data cleaning and preprocessing, carefully choose evaluation metrics, use cross-validation techniques, and always consider the real-world problem you are trying to solve. Specific mistakes and how to avoid them: Ignoring data quality: Not adequately cleaning, handling missing values, or identifying outliers in data before modeling. Solution: Perform thorough exploratory data analysis (EDA), visualize distributions, and implement appropriate data cleaning techniques. ...