3 mistakes Rookies need to avoid in Data Science! | Ep #12

3 mistakes Rookies need to avoid in Data Science

Tune in to Episode #12 of Data Science & AI Weekly! Every newbie starting in Data Science should be aware of some pitfalls that are very common for students. Listen to this podcast to find out the 3 mistakes Rookies need to avoid in Data Science!


[00:09] Topic of Discussion: 3 mistakes Rookies need to avoid in Data Science!
[00:45] Mistake No. 1: What not to do when it comes to ML Algorithms?
[02:04] Mistake No. 2: Quantity Vs. Quality?
[03:35] Mistake No. 3: How to assess yourself?
[05:05] Last Mistake: Stay updated with the field!
[10:19] Wrap up!
[10:30] Learn more about Data Science at www.insaid.co

Welcome to Episode 12 of Data Science and AI weekly. Hi, everyone. My name is Manav and the Chief Data Science mentor at INSAID. And in Episode 12, which is this current episode, I’m going to discuss with you three mistakes that rookies make when they are trying to master Data Science and where they’re trying to enter Data Science that you need to avoid. And why these mistakes and why this episode is very important is that as you look forward to getting started in Data Science and becoming a Data Scientist if you avoid these three mistakes, your journey and become much smoother, and your chances of succeeding in this field will also multiply. So let’s go through what these three mistakes are one at a time. So the first mistake to avoid is get into the trap of learning too many Machine Learning algorithms, right. So one of the things that I see very often and I’ve been in this field for the last half a decade is that Machine Learning is what

Everybody wants to do, right. Nobody wants to do data cleaning, nobody talks a lot about Data Analysis, etc. But the shock that you get once you actually enter in Data Science field is that you realize this, that Machine Learning is only 20 or 30% of the world, not even 30, let’s say 20% of the world, there are various stages. And doing each one of the stage is or knowing how to tackle each one of the stage is extremely important, right? Let’s say, for example, doing Data Analysis, you need to be good at Data Analysis. And trust me, even if you’re really good in Data Analysis, and you know how to analyze data well, and then you can do a little bit of good Machine Learning. Trust me, this is good enough to track entry-level jobs in the field, right. So what I would recommend is Mistake number one, just to summarize is getting too obsessed about Machine Learning, learn Data Science, all the different aspects are equal.

Important, and that’s why you should be equal importance to all of the areas. Number two, Mistake number two, that newbies I have seen make in this field is trying to learn too many things without going deep in any of the areas, right. So this field is very, very vast, this field is very exciting. And there’s a lot to learn, no doubt, right. And that’s why it’s even more important because there’s a lot to learn. You would want to learn everything, but you would want to personally take interest in one particular area that you would want to go deep in. It could be, let’s say, your knowledge about a particular Python library. It could be also a Machine Learning algorithm. It could be that you can clean data and you know different ways in which data can be clean. It can be anything but what is important is to find your niche that you think you are really good at it could also be an application of Machine Learning and Data Science and Data Analysis in a particular industry.

So, the mistake just to summarize is to learn everything under the sun and then not to know how to apply any of this. So, what you need to do is you need to go broad but in some specific areas that you think are the most relevant for you go as deep as possible, right the deeper you go, the more you will benefit. That was Mistake number two. Now, Mistake number three that I see candidates making is that under assessing themselves, right and this is especially true for working professionals because all of the students that we have at INSAID are working professionals stay somehow thing that they say that model I am from blah blah background will I be able to transition into a Data Science tool. I am from non-IT background will I be able to transition into Data Science role, right?

Trust me, everybody and anybody can transition into Data Science role if you’re doing the right things. Think of it this way that lets say that you want to become a cricketer right. So to become a cricketer, you need to have good stamina you need to choose what do you want to be as a cricketer, you want to be a bowler you want to be a batsman whatever. So essentially, similarly, if you want to be a data scientist, you need to start your journey by doing the same thing that is learning Python well, mastering Python packages learning Data Analysis, mastering Machine Learning, reading as much as possible, etc. If you are doing all of these things, right, cracking Rose is not difficult at all. So don’t please under assess yourself and don’t think that I am from XYZ background. That’s why cracking Data Science tools is hard. It’s not hard if you’re doing the right things, tracking Data Science roles is easy.

These are the three top mistakes. Now one bonus mistake I would also want to talk about here is a mistake that I see 90% IT professionals making is, when they’re learning Data Science, they get so engrossed in the Machine Learning part and the Data Analysis part and the programs that they’re undergoing is that they are not reading about a Data Science as a field, this field is so growing so fast, evolving so fast, there are a lot of exciting things that top big multinational companies are doing. And a lot of startups are coming up with interesting products and projects also in there. So as an enthusiast, what you would want to do is you would want to remain connected to the ecosystem read a lot.

The more you read about this field, right, the better your understanding and the more precise your insights will start becoming in this field and the best part is, you can read on the go, right? You don’t have to be actually sitting in the front of your computer to read actually anything you can be on the go. And you can read as much as possible. And, in fact, I will be doing another podcast on some of the recommended readings that I suggest everybody should read if you want to follow the Data Science more consistently. So but these three mistakes are mistakes, essentially, that I would want you to write down. Because I’m sure that forgetting these mistakes is easy, but write down these mistakes.

Don’t make these mistakes. And trust me if you don’t make these mistakes, you will be on your path to success in Data Science much much much faster. Right? So this was Episode 12 of Data Science and AI Weekly. Thank you for tuning in to this episode. If you love this episode, leave a comment in the comment section. If you want to watch other such amazing episode, see the other episode in the playlist. The link is there in the description. I am Manav, I’m signing off. Thank you very much for watching.


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