💥‼️Data Science projects consist of 5 critical steps to ensure successful implementation. 📍1. Problem Understanding A. Define the business or research problem clearly. B. Translate the problem into data science questions (e.g., prediction, classification, clustering). C. Establish success criteria ,what would “good” look like?. 📍2. Governance of Data Science (How to Do Data Science) A. Define the ethical, legal, and organizational frameworks around the project. B. Ensure data privacy, fairness, and transparency. C. Establish data ownership, access rights, and accountability in decision-making. D. This step prevents misuse and aligns the project with regulations and business values. 📍3. Data Preparation A. Collect data from different sources. B. Clean the data (handle missing values, remove duplicates, fix inconsistencies). C. Remove outliers and noise that can distort results. D. Normalize, transform, and engineer features to make data usable. E. Split data into training, val...