What Are Some Typical ‘Rookie’ Mistakes Data Scientists Make Early in Their Career?

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What’s up, I was asked this question by one of my interns I am mentoring, and thought it would also be a good idea to ask the community as a whole since my sample size is only from the embarrassing things I have done as a junior data scientist.

What are some typical ‘rookie’ mistakes that data scientists make early in their career?

Thanks in advance… :thinking:

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Hi!
Here are some common mistakes that junior data scientists often make:

  1. Not Clearly Defining the Problem: Jumping into data analysis without a clear understanding of the problem can lead to wasted effort and incorrect conclusions.

  2. Ignoring Data Cleaning and Exploration: Skipping data cleaning and exploratory data analysis (EDA) can result in poor model performance and misleading results.

  3. Overfitting Models: Creating overly complex models that perform well on training data but poorly on new, unseen data is a common pitfall.

  4. Neglecting to Validate Models: Failing to use proper validation techniques, such as cross-validation, can lead to overestimating the model’s performance.

  5. Misinterpreting Correlation and Causation: Assuming that correlation implies causation can lead to incorrect conclusions and decisions.

  6. Over-reliance on P-values: Believing that p-values are the ultimate measure of significance without considering the practical implications of the results.

  7. Poor Communication Skills: Struggling to explain complex technical details to non-technical stakeholders can hinder the impact of their work.

  8. Focusing Too Much on Tools and Techniques: Prioritizing learning new tools and techniques over understanding the business problem and the data can be counterproductive.

  9. Ignoring Bias in Data: Failing to recognize and address biases in the data can lead to unfair or inaccurate models.

  10. Not Seeking Feedback: Avoiding feedback from peers and mentors can slow down learning and improvement.

Encouraging your intern to be aware of these pitfalls can help them grow more effectively in their career.

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Hey there, Rookie data scientists often make mistakes like overfitting models, neglecting data cleaning, relying too heavily on complex algorithms, or misinterpreting statistical results. Proper validation and clear communication are crucial.

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The biggest mistake, as others have already alluded to, or explicitly stated, is thinking data science is all about algorithms and statistics.