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The Harvard Business Review called the data scientist ‘the sexiest job of the 21st century’ . With the large influx of people into this field and most of them being freshers, people are committing many basic mistakes while progressing in their data science career.
Source: https://dimensionless.in/top-10-mistakes-avoid-master-data-science
Your learning process should be a mix of both theory and practice. Whenever you learn something new, try to find a dataset and apply it over there. Take part in different competitions on websites like kaggle because you will not only learn more here but also will gain experience with the implementation of different concepts
Many people directly jump to machine learning or give a lot of importance to it but this should not be the case. It is still ok if you want to be a machine learning engineer in the future but definitely not ok for a data scientist. Machine learning is not everything which data science has to offer. There are statistics, domain knowledge and communication skills attached to it too.
Accuracy isn’t always what the business is after. Sure a model that predicts employee retention probability with 95% accuracy is good, but if you can’t explain how the model got there, which features led it there, and what your thinking was when building the model, your client will reject it.
In data science, tools are not important but the solution to the problem is. It does not matter how you get to the solution considering tools in hand. Tools are for the purpose of making life easier and enabling one to perform tasks quickly hence one should not pay large attention to the usage of tools
This is one of the most common mistakes many data scientists end up doing. Being a jack of all trades and master of none may give your knowledge a lot of breadths and but you will always lack the required depth. You will be able to start an approach to provide a solution to the problem but it will be very rare that you will till the end of it properly
I have seen data scientists jumping straight to conclusions without validating results they are getting from their analysis or model predictions.
Many data scientist skim over the concepts of data cleaning, EDA and visualizations and move to data modeling. Understanding data first and make it usable for modeling is paramount hence a lot of attention should be given to these topics to emerge out as a successful data scientist
Communications skills are one of the most under-rated and least talked about aspects a data scientist absolutely MUST possess. I am yet to come across a course that places a solid emphasis on this. You can learn all the latest techniques, master multiple tools and make the best graphs, but if you cannot explain your analysis to your client, you will fail as a data scientist.
If you are a data scientist, you will have to code but this is not the hardest part of it. People tend to think that data science is all about coding and should put a lot of attention in coding skills. No doubt coding skills are required but one need not master it all-together. Data Science course online
Many problems do not reach a convincing solution just because the initial research on the problem was less or the domain knowledge related to that problem was not sufficient. People tend to jump into the problem directly without getting enough domain knowledge or performing a good initial research on what the problem is and how one should go about it
