List Headline Image
Updated by divyacyclitics15 on Oct 06, 2018
10 items   2 followers   0 votes   5 views

Top 10 Mistakes to Avoid to Master Data Science

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.



Spending a lot of time learning concepts without any practical application .

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


Directly jumping to Machine Learning and (fancy) algorithms

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.


Considering model accuracy to be supreme

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.


More attention to tools rather than the problem at hand

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


Trying to learn everything at once

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


Jumping to conclusions without proper validation

I have seen data scientists jumping straight to conclusions without validating results they are getting from their analysis or model predictions.


Negligence towards data cleansing, EDA and visualizations

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


Thinking that communication skill is not required

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.


Giving too much importance to coding skills

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