3 Common Mistakes That Every Data Scientist Commit And How To Avoid Them?

By Author: Tarun
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3 common mistakes every data scientist make and how to avoid those mistakes

So, you have decided data science is the correct field for you. In the modern world more businesses are driven by data. It looks like every business will need a data science practice in the future. So, the demand for data science and Data scientists is very vast.

However, becoming a data scientist is not an easy task. It needs a problem-solving skill, structured and mannered thinking, coding, a very great patience and various technical skills among others to be really successful. If you are from a non-technical background, there’s a good chance a lot of your learning happens through books and video courses. Most of these resources might not help you in what the industry is looking for in a data scientist.
This is one of the reasons why aspiring data scientists are struggling a lot to fulfil the gap between self-education and to get real world jobs.
In this article, I will discuss about the top mistakes that every data scientist makes (I have made some of them myself) in their career. I have also talked about the solutions ...
... for the problems listed below

The major mistakes that data scientists will make

1) Learning theoretical concepts and not applying them practically
it’s really good to grasp the theory behind machine learning techniques. But if you don’t apply them, that will not work out and only learning theoretical concepts won’t let you to solve problems. When I started out learning data science, I made the same mistake. I studied books and online courses but didn’t applied any of them to solve a problem.
So, when I was facing a challenge or a problem regarding machine learning I was not able to remember half of the concept that I had learned There’s so much to learn algorithms, derivations, etc. There’s a high chance that you’ll lose your motivation in the middle through and give up. I have personally seen a lot of people who face the same situation who are aspiring to become data scientists.

How to avoid this mistake:

It’s really necessary that your learning process should be a healthy balance between theoretical learning and practical learning. As soon as you learn a concept, head over to some search engines and find a dataset or problem where you can apply the concept that you learnt. You’ll find that you are retaining and remembering the concept way better than before.

2) Trying to learn multiple tools at once
I have seen this one, mainly this one a lot of times. Because of the dilemma and the unique features of each tool, people tend to attempt learning all the tools at once. This is really a bad idea – you will end up mastering none of them. Tools are very important to perform data science problems; they are not the end goal.

How to avoid this mistake:

Pick a good tool and try to stick to it until you master over it. If you’ve already started learning R, then don’t be tempted to learn Python yet. Stick with R, learn it end-to-end and only then try to incorporate another tool into your skillset. You will learn more about data science with this approach.

3) Relying only on Degrees and Certifications

There are too many of these courses online being rushed over and completed by thousands upon thousands of aspiring data scientists. If they ever added unique value to your data science CV, that is no longer the case and there is no problem. Hiring managers do not care much for these pieces of paper – they place far more emphasis on your knowledge, and how you’ve applied it in real-life practical situations. Just think You still need a piece of paper to test your knowledge?

How to avoid this mistake:

I’m being very honest here Don’t get me wrong, certifications are valuable, but only when you apply that knowledge outside the classroom and put it out in the open. Use real-world dataset problems and whatever analysis you do, make sure you write everything about it. Create your own blog, post it on LinkedIn, or any other social networks and ask for feedback from the community. This shows that you are willing to learn and are you are flexible enough to ask for suggestions and work hard on them into your projects.
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All the best for your Data science future.