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Tune in to Episode #17 of Data Science and AI Weekly now! In this podcast, you will find out what are the 3 most crucial things about becoming a Data Scientist in 2020 that no one will tell you. The podcast is hosted by Manav, Chief Data Science Mentor at International School of AI and Data Science.

TIME-STAMPED SHOW NOTES:

[00:09] Series Overview
[00:37] Topic of Discussion: 3 Things no one will tell you about becoming a Data Scientist.
[00:54] No. #1: What is the hardest thing about Data Science?
[02:22] No. #2: Data Science Training for Newbies
[04:48] No. #3: Data Scientists are problem solvers
[05:55] Wrap up!
[06:30] Learn more about Data Science at www.insaid.co

You can follow the podcast here.

Three things that no one will tell you about Data Science that you get to know only once you become a Data Scientist.

Hi, everyone. My name is Manav, and the Chief Data Science Mentor at INSAID. Welcome to Episode 17 of Data Science and AI Weekly. This is a weekly podcast series in which I share my insights and advice and tips with you on everything related to Data Science and AI. If you’re not subscribed to our channel, just subscribe to it immediately so that you never miss an update. We release this content very, very often.

So in today’s session, I have picked up a topic that is close to my heart. That is, what are some of the things that newbies don’t know about or even experts would not tell you about before you get started in the Data Science world formally by becoming a Data Scientist. So three things I’ll particularly talk about the first thing that no one will tell you is that the hardest thing about Data Science is Not Machine Learning. It is not data analysis, right?

The hardest thing about Data Science is formulating the business problem itself. A lot of times where a lot of good Data Scientists get stuck is that they know all the Machine Learning algorithms, they know the tools, but now they are working in a company but they’re not clear about how to formulate the business problem how to convert a business problem into a Data Science problem. Very recently, we had a senior partner from a math company, one of a very fast-growing Data Science consulting companies in India, she spoke about the exact same thing that problem formulation is something that you need to do very well. And this is what is lacking in the current breed of Data Scientists that are getting trained.

And if you can take care of this reform right now itself as you start your Data Science journey, trust me, you will emerge to be super, super. We’re successful in this because the industry needs people who can, first of all, formulate problems statements that can be solved through Data Science very easily. Right. So after you formulated the problem, so the journey becomes much, much easier. So that’s the first thing that no one will tell you that problem formulation is usually the hardest part.

The second hardest part or the second part about Data Science that nobody will tell you is that what newbies get surprised is when they started when they are undergoing a Data Science training. The focus is a lot on learning Machine Learning algorithms, right so all the institutions are busy teaching Machine Learning algorithms learn this learn that this is supervised versus unsupervised.

Now here comes deep learning. When you actually go in the industry, you will realize that Machine Learning is just 20-30% of the task. A lot of the task is to do with problem population data. planing data analysis itself. So while most of the training programs are focused on Machine Learning, real-world practical Data Science is a bit different. It is more focused on the entire life cycle rather than on Machine Learning. Right.

So this is something that no one will tell you about becoming a Data Scientist that if you can take care of this right now, itself, that good that you’re learning Machine Learning, but focus on the packages, Python packages, focus on the data analysis part focus on working on data sets and doing all the slicing and dicing of data part to do that pretty well, that is equally important because most likely, that is something those are the real-world issues that you will actually also face. So that’s the second thing that no one will tell you about becoming a Data Scientist until you actually become a Data Scientists.

Now the third thing, the third thing that nobody will tell you about becoming a Data Scientist is that a lot of times people will have unreasonable expectations from you, which means that as a Data Scientist, you are supposed to be a problem solver. So every so if the CEO for companies is talking to you, and they’ve been hired as a Data Scientist and a startup, so, they would think that now you are here now you are going to solve everything, all the problems, automation, everything is going to become all good. Because after all, you are a Data Scientist, right?

Similarly, in large, multinational companies as well, which are starting their Data Science practices, right. So what no one will tell you is that a lot of times when you’re getting started in Data Science, what you need to do is a good expectation setting, good expectation management as well. And briefing the management about what Data Scientists can do, what Data Scientists can deliver, and what Data Scientists what you as a Data Scientist cannot do or Not able to do and what are the constraints that you have right now it might be that data is not available, you might not have good access to data or access to quality tools, production-level tools as well.

So, you will need to play that part of an evangelist also, instead of just becoming a Data Scientist, wherein you are spreading that knowledge about what is required for the company to be successful in Data Science overall, and not it’s not just about you about but about the company becoming successful overall.

So you will need to play that part as well of getting everyone on board together with a common mission with a common vision and with a common approach towards what is possible and what is not possible from as a Data Scientist that you will be able to contribute right. So those are the three things that no one will tell you about becoming a Data Scientist. Let me know which one of these three Did you like the most and let you know in this entire podcast series that we have been doing has been able to help us so far.

This has been Episode 17. We are quite far along the journey. If you’ve been finding this podcast series helpful, just let us know in the comments section. And if you want me to cover a topic that you would want to get covered, let me know that as well. We read every comment, and possibly we will have a podcast ready for you next week itself. So thank you for tuning in. This is Manav I’m signing off. This was Episode 17 of Data Science and AI weekly. Thank you very much for tuning in.

Author

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

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