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Tune into this Episode #3 to find out how a Day in the life of a Data Scientist goes. The podcast is hosted by Manav, one of the Top 20 Data Science Academicians in India.

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Hi, everyone. Welcome to Episode 3 of Data Science in AI Weekly. 

My name is Manvender, people call me Manav and I’m the Chief Data Science Mentor at INSAID. And in Episode 3, just like the first two episodes of Data Science and AI weekly, we have an amazing topic that you would be very interested in. 

So what we covered in the first two topics is getting started in the field of Data Science. If you have not listened to those episodes, you should do that right away. In today’s episode, we are going to cover a day in the life of a Data Scientist right and why this is an interesting topic is that as you look forward to building a career in Data Science and as you look to become a Data Scientist, you would want to know what would you be doing on a day to day basis. And let’s get started with this episode. So let’s start the day in the life of a Data Scientist. You are a Data Scientist now and report to work at 9:30. You start your day.

From 9:30 to 11 o’clock, first of all, you review your work, you have team meetings, essentially in which you are discussing about the current Data Science project that you that your team is working on. And again, here, you might be working as an individual Data Scientist, if you’re working as in a startup, and in that case, the stakeholder might be the CEO of the startup for example, or you might be working as a Data Scientist in an established multinational company in which you will be having meeting with the head of your Data Science practice. 

So you’ll be spending the first 1-1:30 hours in meeting and discussing the latest Data Science project you will be reviewing in the meetings, the objectives, the KPIs of the project, where what kind of progress you have made. 

After that from 11 o’clock to 1-2 o’clock, you will be spending a good amount of time mostly in taking the project forward. Now all different projects in Data Sciences, it’s not one thing that you are doing it, it’s a multitude of things that you’re doing right from data cleaning to machine learning to visualizing data. 

So you could be at different stages of the project lifecycle. And let’s say that if right now I am building machine learning models, I will be building machine learning models till lunch. 

After that you as a Data Scientist, you have your lunch, and then you’re back to work. 

In the second half, you might be doing some bit of research, because when you talk about something like machine learning all the answers you might not already know. So you might be wanting to experiment more with the models that you’re selecting. So you will read up a few articles on machine learning on the internet relevant to the problem that you are tackling and you might also be researching about that industry problem that you are working on in a little more depth, so that, beyond the stakeholder that you’re working with and his perspective and your own perspective, you might want to get some neutral perspectives as well. 

After that, you continue working on the project in the second half, or you might have other discussions regarding an upcoming Data Science project that the company might be working on. So as a Data Scientist, there are two things that you would be doing, you would be working on the current Data Science project plus, you might be additionally discussing some other Data Science project that might be taking shape. 

In Data Science, a lot of times you don’t exactly get the problem in a very defined manner. So different stakeholders will come to you with different problems. A lot of times you will be meeting with them and you will be defining whether it is something that you can add value to. So this is what essentially the day looks like, some part of the day you’d be coding, some part of the day you will be building mathematical models, some part of the day you will be interacting with stakeholders, and some part of the day you will be spending in reporting to the client/ to your boss, etc. Right. 

So that’s how a day in the life of a Data Scientist goes. This also, to a lot of extent depends on the designation that you are at. For example, if you are the Chief Data Scientist, or if you’re a Head of Data Science, then your day would look a little different than what I’ve just described, but largely for Data Science as a role, this is what your day would like. 

Now, what are the good parts about a day like this? The good part is that you get to work on multiple things. You are programming a bit you are interacting with various stakeholders, you are learning about math, and you’re also wanting to research on the industry topics plus on some other days, as as you know, what everybody wants to know more about, you’ll be asked to give pieces of training in your company, you might be asked to represent your company in industry forums as well. Right? 

So that could happen like once a month, in once in two months, etc. And that’s what you also need to be good at. Right? So this is what a day in the life of a Data Scientist is. I hope you love this episode. 

My objective through these episodes is to help you get started in this field and to help you understand this field in a deeper manner. If you loved Episode 3, just leave in your comment. Let us know more about the kind of episodes that you would want to listen to. This was episode 3, I’m signing off from this episode. My name is Manav and I look forward to seeing you in another episode 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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