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Welcome everyone to Episode 23rd of Data Science and AI Weekly!

What do 1% of successful Data Scientists do that sets them apart from the remaining 99%?

Join Manav in the first video podcast episode of Data Science & AI Weekly and find out how to stand out from the crowd when learning Data Science.

TIME-STAMPED SHOW NOTES:

[00:07] Welcome to Episode 23!
[00:15] Topic of Discussion: Biggest mistake 99% of aspiring Data Scientists make!
[01:15] Why 99% newbie Data Scientists fail during interviews?
[01:40] Importance of Data Science Projects
[02:01] What do Data Science employers want?
[03:09] Which projects to avoid?
[03:41] Pre-requisites of good Data Science projects for you!
[04:15] Wrap up!
[04:25] Learn more about Data Science at www.insaid.co

You can follow the podcast here.

Welcome everyone to Episode 23rd of Data Science and AI Weekly!

I’m so pleased to welcome you to the first video episode that we are doing in this podcast and this is basically based on a lot of feedback that we have received from you that a lot of you love the podcast, but wanted it to be more visual.

And here we are with the video series. And I hope that through this video series, this podcast will become more engaging and more relevant for you. Right, so with this, let’s start with this episode of Data Science and AI Weekly.

In this episode, I’m going to talk about one thing that 99% of Data Science learners are not doing and if you’re learning Data Science and most likely, if you’re watching this, you are one of our students, maybe, let me know in the comment section if you’re doing this or not.

Now, I have interviewed a lot of candidates over the last couple of years and currently, I am training a lot of, a lot of candidates as well. Now, one usual tendency among  Data Science learners is that they want to do a certification, want to learn a bunch of things, data analysis, Machine Learning, etc. and then they want to directly go for interviews, right?

And when they go for interviews, they end up bombing interviews. And a lot of times they don’t even get selected for interviews! And the big question is why?

And the reality is 99% of learners don’t even get to get through interviews. The question again is why?

So the simple answer is that most of the candidates what they are not doing is they are not working on projects. Projects are what differentiates the 1% of learners from 99% of learners.

Now, this is so very important if you look at it from an employer’s perspective.

An employer wants… (what does an employer want?)

An employer wants someone who has good Data Science skills, who has good hands-on skills and who can get cracking from Day 1 and what is the best way in which you can demonstrate this thing?

You can’t demonstrate it by writing that I have done a certification. You can’t demonstrate it by writing a lot of jargon in your resume.

You can demonstrate it only through projects because that will become the centerpiece of, first of all, you getting shortlisted and finally you cracking interviews. So that’s the first thing.

The second thing I would recommend here when it comes to projects, is a lot of students think and professionals tend to think that let me just put any kind of projects in my resume or in my GitHub profile. That means now I have a lot of projects and I’ll get shortlisted again.

Do not do that!

So what employers have been seeing, time and again, are projects like Iris Data-Set, your Titanic Data-Set, etc being written by Data Science aspirants, please do not do that!

Please do not write these basic projects that are the beginning or the starting points of your entry into the Data Science world. And this is what everybody is writing in there; assuming you would want to write projects that are either intermediate level or complex level, which tells an employer that you are not at the basic level because writing a project like Titanic project in your resume, it can actually do you more harm than good.

So make sure the projects that you are picking up and you’re putting in your resume, they are really high-quality. And ideally from the domain in which you’re either currently working on or the target companies that you are looking at targeting; they are from those domains.

For example, if you’re targeting healthcare companies, you might want to take some intermediate to complex level projects in healthcare, because that would become a good talking point for a prospective employer in healthcare domain to talk to you and to finally recruit you.

So this is my one big tip. I want you to remember from this video podcast, which is projects are the start and end of becoming successful in Data Science.

So this is Episode 23rd of Data Science and AI Weekly. Let me know how did you find this episode and let me know if you liked this video approach of doing this podcast. I’ll be back with another episode which is Episode 24. Till then, see you and look forward to seeing your comments.

Author

Content Writer @ INSAID. A machine learning buff who loves to read, write and explain everything AI!

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