To differentiate we need to understand below bias in our decisions, perspectives, views
Confirmation Bias - This refers to interpreting new information in a way that confirms our pre-existing beliefs. For instance, if you have knowledge of SQL Server, you may believe that the index storage patterns in NoSQL are similar. While there can be similarities, it's crucial to be aware of the differences as well.
Misconceptions of Skills - Often, we mistake awareness of technology as expertise. Merely being able to compile and produce output doesn't necessarily mean understanding how it works or its intricate mechanics.
Halo Effect - This is when you either completely like or dislike everything about a person or thing, with no middle ground. Judging a technology without a thorough understanding of it is an example of this effect. Intellectual humility - Acknowledging what you don't know is the drawing of wisdom
WYSIATI (What You See Is All There Is) - Here, you cannot consider what you do not know. It's about having a balanced view versus a mindset of 'I know it.'
Consequences of this
- We have a larger number of data scientists who possess a basic understanding of a variety of domains, rather than deep expertise in a few domains.
- Moreover, we have more data scientists who are algorithm-oriented compared to those who can skillfully blend algorithms, data, and common sense.
- The absence of awareness in domains and data will only result in solutions similar to those found on platforms like Kaggle, which don't necessarily meet business requirements.
- Preparation - No amount of preparation is enough to face the customer, Every possible scenario needs to be tested. The only thing that matters is the correctness of your analysis
Keep Thinking!!!
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