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Tune into this Episode #1 to find out what is Data Science and how to get started in Data Science. The podcast is hosted by Manav, one of the Top 20 Data Science Academicians in India.

 

TIME-STAMPED SHOW NOTES:

  • [00:42] Topic of Discussion: What is Data Science? Trends in Data Science.
  • [01:00] The field of Data Science: An overview of business problems.
  • [01:26] What does a Data Scientist do?
  • [01:40] History of Data Science and Machine Learning.
  • [02:09] Data Science: Then and Now 
  • [03:00] Organizational perspective on Data Scientists.
  • [03:15] Future of Data Science
  • [03:40] Data Science trends of 2019 & 2020
  • [04:05] Automation as the future of Data Science
  • [04:50 Data Science Technologies
  • [05:20] Wrap up!
  • [05:55] Learn more about Data Science at www.insaid.co

You can follow the podcast here:

Hi, welcome to the first episode of Data Science & AI weekly. My name is Manav and I’m going to be your host for this series of podcasts that we are launching on Data Science and AI. 

The whole purpose of this podcast is essentially to keep you tuned to what is happening in the world of data science, to also get you started in the field of Data Science, and to also answer any questions that you would have about getting into a Data Science career, what is happening in this world, and what essentially you should be doing to become successful in the space. 

So let’s start with our first episode by looking at exactly what the world of data science is and why this has become such a big hype lately. So let’s go back. First of all to what Data Science is and why this has become such a hype.

Now for starters, Data Science is a space or as a field which is an amalgamation of computer science, statistics and domain. What this means essentially is Data Scientists are using all these three fields to solve business problems. Now, what does that mean? Essentially what Data Scientists do is they run experiments, which is what the science part is on a particular business problem to get a particular result. Now, the important question is why this field has become such a rage and why this has become such a big hype and why did this not exist 5 or 10 years back. 

Now, what you would be surprised by is that the underlying concepts behind Data Science, which is a lot of maths, for example, machine learning, they’re already existed since decades back.

For example, What you’d be surprised by is that the term Machine Learning was coined way back in the 1950s itself. But what has changed over the last few decades is that because of the rise of power of computation, and because of ready access to a lot of powerful data science tools and packages, now, it’s people like you and me sitting in our homes who can do data science as well. 

And we don’t have to worry about getting lost in complicated code. So essentially, the power of computation is available to us, the power of tools is available to us but more importantly, the food for Data Science or the raw ingredient for data science, which is data, earlier it was not easily accessible or available. Now thankfully, we have tons and tons of data.

Right. And data access is one of the reasons why organizations now are focusing on recruiting a lot of Data Scientists, training a lot of Data Scientists and essentially getting value out of, of this whole area of data science. 

So that’s how we got into the space. Right? And that’s why this space has become such a huge hype. Now, the question is, what is the future of Data Science? Right, and that’s a question I’m sure a lot of you would have also been thinking about that when you’re looking at entering this field, what is this field’s future all about? 

So let’s look at it through some of the trends that are underlying this field. And there are essentially two key trends data will continue to explode. What do you think? Will it or will it not, of course, it will!

In the coming decade as well, because of this explosion of data, you will require people who can analyze this data, clean this data, who can build predictive models out of this data. And that’s why data scientists will have a great future from a career standpoint. 

And the second point, an important point is that automation. Automation is one word that I am sure that you must be hearing again and again. And one of the key things that Data Scientists do is automate things. For example, if you’re working as a data scientist in a bank, one of your tasks might be let’s automate credit card fraud detection. And that’s a problem you will be working as a data scientist on. And you will continue seeing more and more of these problems in different industries over a period of time. Automation is one thing that will keep data scientists relevant and from a career standpoint, that’s why the future is very, very bright. 

What will change, however, is the technologies being used in the space. Couple of years back when I was getting started in this space, R, which is a tool for Data Science used to be a big rage. Now in 2019-20, everybody is talking about Python. After a couple of years, maybe there is some other tool which solves the problems that data scientists face, maybe by 2025, the tools will change. 

But this field is what it will be, what it is right now. It will continue being hot. So that has been the journey of this field over the last couple of decades and what it is going to look like in the next couple of decades as well. 

So this was the first episode of Data Science and AI weekly. I hope you loved this episode, the goal of today’s episode was just to give you some underlying trends in this space. And if you have any questions, if you have more or more thoughts that you have now gotten after listening to this podcast, leave your questions in the comments section. 

And if you want us to cover any particular topic that you would want us to cover in Data Science and AI Weekly, just drop in your topic in the comments section and we will be happy to take that topic up in another episode of this podcast. 

Thank you very much for listening. See you in Episode 2 of Data Science and AI weekly. My name is Manav and look forward to coming back on this podcast with another episode.

Stay tuned for more!

Author

INSAID Research Team's mission is to research and educate on Machine Learning, Data Science and Artificial Intelligence. We spend significant amount of time on research & presentation of each tutorial, article & video in a way that people just like you can master these areas.

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