Is data science just about running code? I don’t think so… In the field of data science, solving problems and answering questions through data analysis is the standard practice. And when a data …

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Code Like A Girl

Data science is far more than writing code — methodology is what separates successful projects from failures. CRISP-DM (Cross-Industry Standard Process for Data Mining), created in 1996, is the most widely used framework in the field. It structures a data science project into six phases: Business Understanding (defining objectives and translating them into a data mining problem), Data Understanding (collecting, describing, exploring, and verifying data quality), Data Preparation (selecting, cleaning, engineering features, and integrating data), Modeling (choosing algorithms, splitting data, building models, applying cross-validation, and tuning hyperparameters), Evaluation (selecting the best-performing model aligned with business goals), and Deployment (presenting results in actionable formats like dashboards or real-time systems).

Is data science just about running code? I don’t think so…

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