Don't do these DATA SCIENCE Mistakes

Beginner Mistakes :

• Spending a lot of time on theory.


• Jumping directly into coding ML algorithms without learning the prerequisites.


• Thinking to build the future without knowing the basics.


• Not spending enough time on exploring and visualizing the data.


• Focusing on accuracy over understanding how the model works.


• Assuming the algorithm is more important than domain knowledge.


• Not having a structured approach to problem-solving.


• Learning multiple tools at once.


• Not learning/working consistently.


• Less communication.

Intermediate Mistakes :

• Data Leakage.


• Sampling …

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