Data Science is not Just Models

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Comments

[Will Laurenson]

I agree. Building the model is only one part of the data science workflow. A model can have great accuracy and still be useless if the underlying data is incomplete, biased, or poorly understood.

A lot of the real work happens before and after modeling:

• Data collection and cleaning can take significant effort.
• Understanding the business or research problem helps determine what should actually be predicted.
• Feature engineering and selecting meaningful variables can be as important as the algorithm itself.
• Evaluation should use the right metrics and reflect the actual goal of the project.
• After deployment, models need monitoring because data and user behavior can change over time.

Communication is also an important part of data science. A good data scientist should be able to present findings clearly and help others understand the results.

[Excellence Technology]

Great article! It clearly explains the fundamentals of Data Science. Thanks for sharing such valuable insights.
Very informative content. The examples made complex Data Science concepts much easier to understand.