Meet Ranjini from [24]7.ai

Ranjini

At INSAID, we create accomplished and empowered Data Leaders. We groom our students to dominate the world of Data Science and AI and reshape their future. We value what our students bring to the table. We share their vision and support them during their journey and ensure that they carve a niche for themselves.

We’re proud to have tutored exceptional students all across India. Today, one such exceptional student, Ranjini stands in the spotlight.

Student Name: Ranjini
Current Organization: [24]7.ai
Batch: GCDAI – May 2019
Total years of experience: 7 years

Malvika: Hi Ranjini, before we begin, could you tell us more about your current work profile?

Ranjini: So, I started my career with a BPO and I was a Customer Support Associate at FirstSource servicing the holiday needs of the customer.

That was a big brand, Virgin Holidays, and then got an opportunity to become a Trainer. So started with the new hire training, helping people understand how to use the tools and how to serve the customers and also the soft-skill parts and tell them how to understand the frustration, answers put by the customers, and then move to another brand that was Expedia. And I was a Full-time Trainer here.

My responsibilities ranged from not just training the new hires but talking to the clients as well, presenting what is the achievement and what are the fallbacks for the quarterly meets that we had.

Also looking at the performance of the agents on the floor and pitch-in for any skill up-gradation or refresher training. All these things were a part of it. Lastly, if there is a new process or product launched by Expedia that needs to be learned by everyone so up, get trained about it from the client and then train the trainers as well. So these things were part of the journey.

Malvika: What got you interested in Data Science & Machine Learning? 

Ranjini: It’s completely into the field that I was supporting actually.

Every time a customer called in, it used to be either servicing the holiday; when I say holiday, the customers have their flights and their hotel, whatever things booked with company and so what we are supposed to do is help the customer if they really want to make changes to the journey or cancellation, or probably booking a new reservation, whatever it is, we had to do that for the customers.

Obviously, we had the tools for those, but what was interesting over there is like we were working on the live inventory. When we have 100 seats released, there can be plenty of people trying to get them. So, the systems are so good enough that considering many people supporting the customers for the same brand alone, the system was so efficient enough to show what was available and what was occupied.

And it used to give us a notification like so many customers are viewing this; so many are trying to book this at this moment; so many people have been looking at this reservation or hotel or flight for the past week and how the price might change in the future.

What is the price fluctuation that can be expected? All these things were the pop-up information. So looking at this, I really was wondering how is this working?

When we consider Expedia; Expedia is such a big brand and it is dealing with so many customers, so many accommodations and airlines, imagine how good the system could be to be able to give the information instantly. So that was what surprised me and got me to think over it. And that’s when I came across the INSAID session about Machine Learning and Data Science. I was impressed and was ready to enroll.

Malvika: What all tools and packages in Data Science & Machine Learning have you mastered in your Data Science & AI program at INSAID so far? 

Ranjini: The syllabus at INSAID is so in-depth and all the resources are very, very intuitive when you learn as a learner as well.

So we have learned quite a lot during these months and I’m sure we have a little bit more to learn in AI as well. I’m most interested in Pandas, which if you are a part of INSAID, you know is basic.

SK Learn is a kit that gives you possibly everything that you need to start Machine Learning with and Matplotlib helps you visualize things. As of now, we are currently learning Tableau. So, till now it is Matplotlib and Seaborn.

So these are the packages that I have been frequently using and I’m interested in these now. I’m going to learn a better set of packages in the future but I don’t want to jump the gun immediately. Whatever it is, take it at its own pace so that things are going to be better. 

Malvika: What are some of the initial challenges you faced when you got started on your Data Science journey and how did you overcome it? 

Ranjini: I have completed MCA but due to recession at the time, I couldn’t join any IT companies and that’s how I ended up in a BPO.

So with BPO it was completely into servicing the customers and talking a lot to the clients and all these things were the part, but nothing into anything related to the technical things like coding.

Yes, we had the tools that we were working on but the tools used to have a set of things that we are taught with and nothing to do with technology completely. If there was any error or issue then obviously, we used to approach the tech team who could help us.

So I’m a post-graduate in Computer Science, but still, when I came back to Data Science after this long gap, it seemed everything was going very fast. Yes, we had the starter kits where we had enough time to go through everything, understand the basics and get ready for the live sessions.

It did seem overwhelming for me at that moment but I did adjust over time and things were fine later. Even me with a Computer Science background found it difficult initially when it came to coding and refreshing all those points, then, of course, someone with no programming background will find it difficult is what I thought.

Malvika: What is the goal of Data Science? In your view, how has Data Science evolved in the last few years? 

Ranjini: The goal of Data Science, I would say, is indefinite.

We are growing. When I say growing, we as humans have been using a lot of things around us and due to which we are now gathering so much data.

Yes, we have plenty of data. Now using the data, we can learn a lot. And that’s how Data Science, AI, and Machine Learning have been evolving, and there’s a lot more to go. So it’s indefinite. It’s not going to be goal-oriented because it’ll serve as a small period goal like automating something.

So people will have short term goals when it comes to applications. But Data Science as a field keeps evolving keeping the big picture in mind.

Malvika: What are the current trends in Data Science that you are most excited about? 

Ranjini: I was most fascinated when I got to know about driverless cars.

Two years ago, I heard that some people somewhere were working on building driverless cars and that’s going to be initially to carry the goods and services. Obviously, they’re not interested in trying it on humans directly.

I was like, wow, this is something that I should know about. So now I’ve been watching some videos on YouTube and interesting stuffs about driverless cars.

Another recent trend that catches my attention is Natural Language Processing. Baidu has now crossed Google and Amazon’s achievement. I’m really excited about what is the next thing about natural language processing.

Although I haven’t started learning anything about natural language processing yet. That’s going to be a part of the AI syllabus now. This is something great that machines can also read and understand, comprehend things like humans. So these are the two things that I’m really excited about and I would love to learn more and also see if there’s something that I can contribute towards these in the future. That would be great.

Malvika: Which are some of the blogs that you read? Which are the top two Data Science & AI influencers you follow?

Ranjini: Yes, I have subscribed to Medium. And I keep getting new articles on a daily basis. Medium is a great platform. It’s not just going to be for someone new, it can also be for the people who are in the field to get themselves updated about what’s happening around the world in the Data Science field.

Apart from that, I follow Analytics Vidhya as well. That is also a blog where people post a lot of stuff and easy to understand. So these are the two blogs that I keep following.

I have got two Data Science Leaders I constantly follow; Steve Nouri and Randy Lao. Randy Lao is really impressive. He has been into Data Science for approximately 2-2.5 years now.

After completing his studies, he is a Data Science Mentor at Sharpest Minds at the moment. He keeps posting a lot of inspiring content. He has also released a book for budding Data Scientists recently called the Data Science handbook.

It was some 50 pages with how things work with Data Science, what are the basics and how one should get herself prepared, how to evolve, how to learn things over a period of time? Also, he keeps taking people’s suggestions, and he’s very active. 

These are the two people I actively follow.

Malvika: At INSAID, students are encouraged to build high-quality GitHub profiles. Have you built a GitHub portfolio and how do you think this will help you?

Ranjini: I got to know this as soon as I started INSAID.

Before that, I had no idea about this and we made a full-time Whatsapp group and people started talking about it. So I did all my research on what it was and created the account but I didn’t know what to post there.

I was like, what has to be done next?

It’s when we completed the second term and I was given the first project. So I was all excited, did everything and then I was waiting for reviews to get back to me. So once that was there, people were posting on the WhatsApp group. 

That’s when I got confident and updated my own projects. I got to know that you can’t just keep your work to yourself, telling the people around that you know what I have done, you you are free to see how my work is and then you can share your thoughts on that.

So when we give this opportunity to everybody around us, we learn a lot and become stronger in our areas of focus. You also get a number of opportunities and more room to improve yourself.

So GitHub is a great platform where you showcase your work, not just keeping it on your laptop alone. So that’s how I learned about GitHub and through Career Launchpad, I learned how to beautify things on GitHub. I optimized my profile accordingly. 

I’m continuously working on the same so that if and when I apply for any job in the future,  someone interested in my LinkedIn profile can also check out my GitHub profile and the world gets to know what I’m working on.

Malvika: Crafting a great Data Science resume is a critical part of getting shortlisted for Data Science roles. Tell us some ways in which you have improved your resume as part of Data Science Career Launchpad.

Ranjini: To be honest with you, that was a great initiative by INSAID. As I was from a BPO background, I was wondering how will I project my experience here in Data Science?

How would I build a resume, people will have at least some consideration for by looking at it?

That was my major concern so when it came to this INSAID Career Launchpad, I was looking forward to it. My biggest concern was the resume.

The sessions went really well, and also I had taken some screenshots of the presentation as well at that moment so that I don’t lose whatever I had to keep in mind and created a top-notch resume, and so I’m very grateful to you for that. 

Malvika: INSAID’s mission is to Groom Data Leaders of tomorrow. What do you understand by a Data Leader? And how is a Data Leader different from a Data Scientist?

Ranjini: It’s actually quite inspiring to know about INSAID grooming Data Leaders of tomorrow.

When I started I had no idea about the difference between Data Scientists and Data Leaders, but now I know innovation makes the difference between a leader and a follower.

So when I started here and slowly begun understanding what is Data Science and Data Scientist’s role, then I understood what is a Data Leader. A Data Leader is the one who needs to know what is the data required for a particular problem? 

A Data Leader needs to understand what is the data required, and then get the relevant data and make sure that data is used in the right way and should know when to use the restrictions on data as well.

Yes, they are people like our Data Scientists or Analysts who are looking into things, trying to analyze the data and then come up with an analysis, patterns and things like that. As a Data Leader, it’s important to look at the data which has been rejected or is deemed not useful for whatever the problem statement is because they can be missed out things that are not noticed by others.

But in the leader’s role, as I mentioned to you, innovation makes the difference.

So it’s important that one looks at the missed or leftover data, which has been rejected and also look at the anomalies and understand what is the difference that it’s going to make to your problem statement, or rather machine model that you’re going to build.

And also, one must know what are the good things about the model that we are building and also try and look for opportunities where you can fail your own model because someone else pointing it out is going to be out of your control.

Rather you should be the critic and understand where your model fails, try and fix it to some extent at least because we in Machine Learning can’t be 100% sure every time. So we will have room for errors.

But before giving that to the stakeholders or the clients, it’s better as a Data Leader to take the control, to understand what are the things can go wrong if we implement the model, and also try and understand the difference between correlation and causality because causation is different from correlation, just the relations between two individual parameters can be different from the causality because causality is a great thing, which is going to contribute your model.

So how good is your data is going affect the output of your model as well. So not just working on the data, but be a critic and also look at the model from a 360-degree view, literally become a client or a stakeholder or a critic, try and find out what are the bad things and get ready and also show the world that you are a leader who stands out from the others.

Malvika: How has your journey with INSAID been so far? Do you have any comments on how the curriculum has been structured, the faculty and the support team?

Ranjini: The curriculum is really great and impressive.

When I checked for any other courses elsewhere, INSAID’s curriculum looks really great, which has got a lot of things included and building a lot of confidence as well in oneself.

When it comes to the faculty, Deepesh is my favorite, he knows it all. That’s how he actually carried all the sessions of Machine Learning. He knows how to know share information in an easy way to the learners. That is a great thing about him. 

Malvika: Thank you for your time, Ranjini. All the best for your future!

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