Always we get evaluated for success/failure. There is no place for passion/experimentation/work on something in long term.
- Pass / Fail
- Engineer / Doctor
- Data Scientist / DB Developer
The flaw that you get graded and evaluated fit or fail is itself wrong. How many times have we passed an exam but had to relook to learn a concept? From applying/solving Data Science use cases vs when I had to revisit basics. I had to relearn everything because In real-world scenario data/use case / ML Algo / Accuracy / Deployment those take-up time and focus, not first principles. Balancing both building blocks vs implementation needs time. 20 years gave me a perspective of how it evolves. 2 years of master's helped me unlearn/relearn. Still, I try to bridge the gaps. Sometimes the questions help me to find another convincing answer not a namesake answer. Multiple choice questions can deem you as failed but your perspective may be beyond those MCQs, When you learn a skill some things could be intuitive not remembered from an exam point of view. When you do several hands-on experiments, you can relate/connect better? What you read/see/hear may not connect well.
- Learning is endless and it cannot be evaluated with point-in-time marks/evaluation.
- Good communication skills do not mean good technical skills
- Certifications do not mean you have the required skills
- Titles do not need to reflect competency
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