Got 5LPA at Startup | How did he crack Walmart with 50LPA The Data Analyst Roadmap 2024
So I didn't get into new sigma. I went in a very over-confident way, that I will just get it. it's very easy. And in the first round I was out. So that was a big shock. See, there's no need of Data Structures and Algorithms in analytics.
If you go to the data scientist way, you would need Data Structures and Algorithms there. System design is there. But analytics is not about data science. Its not about DSA, OOPs concept, you have to learn very different things. That's why if you see, people who want to come in analytics from a non-tech background I would ask you a question on behalf of my audience, because everyone has this doubt.
Some companies say business analyst, some say data analyst. Some call themselves data engineer, some are data scientists. I would like to know an example of each one of them. When you completed your time in Swiggy, then you were going in Walmart. How much was the package in Swiggy? Just for our audience's motivation. So when I joined Swiggy, I joined around A company that you need in your career, some people change a lot of things.
In my case, it was Swiggy. And I very much, highly rate Swiggy because the people ensured me that I am learning the right things. Going in the right directions.
If I see before that, I didn't get any promotion in the 2 years. My question to you is, Swiggy gave you a lot of things, you learnt a lot. If you had to rate Swiggy out of 10, how much would you rate? So you will learn what a growth mindset is, in this podcast. Today with me is a guest who has seven years of experience. He is now a senior data scientist. I have done a lot of podcasts, but the experience of podcast with him was amazing.
I learnt a lot. We are often confused in different profiles, what a business analyst does, data analyst does, are these similar? This doubt will be clear after this podcast. He has seven years of experience. He spent his starting time in startups.
Then joined Swiggy. And now he works in Walmart. You'll get to know the journey from 0-50 lakhs. What a growth mindset is, how you should approach your career, when you are a fresher. Which you'll learn in this podcast. I learnt a lot from Gaurav, in person, while talking to him.
And even you can learn a lot if you watch the video till the end. So you have to promise me that you will watch the video till the end, and like this video as much as possible, like aim is 5000. Do it for such amazing podcasts. Let's talk to Gaurav, get to know his story, struggle, his negotiation, how he negotiated to achieve such big numbers.
So you'll get to know all that in this podcast. Let's get to know and understand. So Gaurav welcome to our YouTube channel. Today we will get to know about your seven year's journey in data science. Whatever your experience has been, right now you work for Walmart as a data scientist. Your Swiggy experience, your startup's experience will be beneficial for our audience.
I get a lot of comments, talk about data science, get someone experienced, I think you are the right one. And today we'll talk with you. So I won't take much time, over to you, you can start, first with your introduction, your academic background, where you belong, please tell a bit about yourself. Over to you. Firstly, thanks a lot Ajay for having me. I think its a pleasure to be on your show.
And I've watched so many of your podcasts. So lovely to be on the show. I will tell a bit about myself. My name is Gaurav Agarwal, and I currently work with Walmart as a senior data scientist. And in this journey, of around 7 years of experience.
There's been lots and lots of confusion. Everything came along. But the major role that happened that was very significant in my journey was at Swiggy. Where I spent close to 3, 3.5 years. And from there, when I was starting my analytics journey as a business analyst, I entered as a business analyst.
And there, what you get to learn in a fast growing startup. That is a turning point. And I think that same continued here also in Walmart. So, I have analytics, data science, experience of everything. Apart from that, I belong to Jabalpur, a city in Madhya Pradesh. And from there I did my graduation in 2017.
From computer science background. And just like various other students, I was a lot confused. What to do eventually. The first 2, 3 years were to do MBA, GATE. Just a good job was the requirement.
So in that confusion, lots of things came and went by. And somewhere, along with luck, the journey starts in analytics in a small startup. They hired me as an intern. And said if I do good work, then they'll keep me. And I had the opportunity to go in Infosys which came in my college. In the midst of this confusion I chose to go in a startup.
To try and put an effort. Because I had realised by then in analytics, I was hearing a buzzword from everyone. So I said, okay let's try with that.
So from there, a little journey starts. And I think, if I look in 7 years, it has been good. Not that bad, it was good.
So how many years was your startups experience. And how much did you get to learn? Well in the startup, in my 7 years of career experience, I just came in Walmart, 6-7 months ago. Before that, all the companies that I've worked in, all of them were startup.
So majority of my career has been into startup. Those startups slowly became bigger. So the first company was of 25 people, in which I used to work. After that I entered another startup whose name was Tedence. Its a very similar company which like, newfigma which is a famous one in analytics, is a service based company.
And after that, I think when I went in Swiggy in 2019, it became a very fast growing startup. Everyone knew what Swiggy and Zomato are. And that, if I learnt for 6 years in terms of learning experience, then that time's Swiggy CTO, Dale, whenever we had a townhall. He said a really good thing.
What you get to learn in startups, you won't get that into big companies. And I think I completely agree to that point. Whatever I've learned, major role in that is of startups, and they have taught a lot.
Not just growth, not just money. But you learnt real things. Because you were seeing things being made. I exactly saw Instamart, even though I didn't work on it. But I am seeing exactly what is working.
And since there are small teams. So you interact with everyone. So you know exactly how it happened. Why it failed.
And not just that its getting successful. You parallelly feel a lot of things. So execution is a major role there.
I think those things tell you a prospective in your career, that how things work. And specifically if you are talking about analyst, the problem in everyone's career is that we do work, everyone writes queries, can dig data. But people don't understand how your data, the insight that you are sharing, how decisions are made from it. Swiggy is a company which actually taugth me, how I am taking decisions from the work that I am doing. Because I myself am a costumer of Swiggy. So I am learning, understanding, that I am able to easily relate to it in a hypothesis form.
It got very easy for me. Many processes are flowing easily. I think that learning of startups, I think everyone should in their career, at least at a starting point, experience a little. Its a very good learning.
And I think that my journey about startups. So, after college, did you get placed in a company after college? Or did you find the job yourself and applied that startup experience? Why not any service based company or did you get a chance and you didn't join? In college, see there were two major types of companies coming in college, either companies like TCS, Infosys. One company was Mu Sigma. Which I liked a lot.
So I didn't get into Mu Sigma. I went in a very overconfident way, that I will crack this. Very easy. And in just the first round I was out.
So that was a big shock. Then was able to crack all of the TCS, Infosys companies. But I was not very happy. And, I think I'll accept, when you are in college, you are listening to a lot of things. So my view was not very good towards these companies at the time. I'll probably take those words back now.
Because I know now, how many jobs those companies create. So I got a job but I wasn't happy there. And towards the last year. I realised that there's a buzzword 'analytics', 'data science'. And I, since from computer science, already tried a lot like coding in the 3rd, 2nd year.
But I didn't get it. Maybe the environment of the college wasn't like that. Or the peer groups weren't like that, everyone's coding together, Data Structures and Algorithms. So I tried Data Structures and Algorithms a lot of times, but after 2, 3 months I would end back on zero.
So at the last year, I got a little bit of Python, so tried to do data science, analytics, machine learning slowly. So from there, I think doing slowly, even though in service based companies, I got one thing clear I wanted to try off-campus. So kept creating profile, wrote some blogs on whichever project I did. And failed in a lot of interviews.
I don't even remember the list in which I must have failed. I had applied everywhere. More than 50, 100 companies that I must have applied. Didn't get it. But slowly I think I started realising that I need to change resume here, how to define the project here which you are doing. This is where you are lacking.
Probably on the interview part. And I think, its like, after a point luck was good, one company gave a chance. And things clicked, and that chance, I think my interviewer, I am so grateful for that person because he pushed a lot during the interview, that I can do this. Rather than pin pointing that this person can't do it. So I think I got those people, came into the journey, and lifted me up. And I got that option.
When I got the option to go either Infosys or Implay analytics, which was the first internship. There was one internship, there was doubt, whether there would be full time or not. And on other hand, Infosys is a very big brand name. There's good training. Yeah. So in that situation, in that doubt That was my reason, firstly I wasn't thinking good about this company.
I knew the money I was getting in Infosys. And in software development, I understood that I wasn't getting Data Structures and Algorithms. Now I have to go in analytics. Then that reason was very clear, have to go in analytics, so let's go to a startup, work with effort, will get to learn good.
And we'll see what happens. So I got that cleared, to go in that form. Therefore I chose startup.
And I think that decision was actually a very good turning point in my career. Chose from there, so understood a lot of things very early. Yeah.
Just one thing I wanted to ask, so your initial years, before Swiggy, how much time was that? How much experience did you gain before Swiggy? I think I got an experience of around 2 and a half years before Swiggy. And what was your role there? Busines analyst, data, around data science? It was around analytics. There were different tags, tag in the first company was Associate Analytics Consultant. In the second company it was Business Analyst. Even in Swiggy, the tag was business analyst. But the work was similar everywhere.
What you had to do was put recommendations in companies from the insights from data. I would ask you a question on behalf of my audience, because everyone has that doubt, some companies say business analyst, data analyst, some call themselves data engineer, data scientist. I would like to understand an example about all of them. Like, this is business analyst, or business and data analyst are same. Please tell us that.
See, the data analyst and business analyst are a problem in the industry right now. The problem is that the naming convention is not similar, so when I was interviewing for Swiggy, I got in on a lot of interviews. I got same role in Myntra with the data analyst tag. Similar salary. Swiggy gave me business analyst. But the work in Swiggy, Myntra, or Ola, any company, if your work is like, you are very strong with SQL, your SQL part is good, and you are very efficient in problem solving and you know other tools.
You know Python a bit, you know Excel, PowerBi, dashboard tools. And eventually your game is you are working to solve a business problem. This aspect of problem solving, if you know this, generally companies call this role either business analyst, or sometimes call it data analyst. Now the confusion here is that, in Swiggy, there's an opportunity for business as well as an MIS analyst. MIS analyst is generally called reporting analyst.
Sometimes companies call them data analyst. So the confusion in whole industry is what is data analyst, business analyst, MIS analyst. But if I break it in easy language. So if your work is to just get the data out. And share it with your stakeholder.
Understand it like, there's a VP in business, they have to take some decision, and quickly want some data. So you are attached with him, working with him and he told you to get this data, so you wrote a query, get the data out, and sent it. If this is the majority of your work. And slowly you start to create reports in PowerBi. You will have a business analyst who is telling you the reporting work, which is a very manual work, if you are doing just that, then we predominantly call it reporting. So we have to differentiate in the work, tags can be different in companies.
But there's a vast difference in the salary, which I've observed. Swiggy is giving a business analyst, 12-14 lakhs. But an MIS analyst is getting 5-6 lakhs. So if you see, the growth is also very starkly different. So if you want to move ahead in the industry, you have to understand that you have to become a problem solver, I don't just want to get the data.
There's neither money in this work nor growth. You have to become a problem solver from where your journey will grow. Understanding this thing is important. According to you, what skillsets are required, the freshers that are watching, who want to get into the data field. You explained about the tags very clearly.
You can be a business analyst somewhere, and data analyst elsewhere, role can be changed. But what are the main skillsets, which you commonly use as being a data scientist. So, see data scientist is a different field. If I see data scientist, if there's a product based company, there you just won't make a machine learning model. Many people are confused about this.
Writing code in ML of 20 lines, got the libraries, circuit learn, pandas, tenserflow, you just have to write the code. That is not the job of a data scientist. There you are doing a lot of things. Now you have to put that code in production too. If I take example of Swiggy, everyone is using Swiggy or Zomato app.
So you see that when I order Swiggy, the ETA that I see, estimated time of arrival, it keeps changing slowly. So that is the work of data science. Data scientist made a model, a predictive model, and wrote the production level code and put it in production. So that whenever someone orders in real time, he can see the time. That is a data scientist job.
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Now, if I talk about a business analyst, then what did the business analyst decide in Swiggy? That the coupon that I am giving to the user, the 60% off, 60% off upto so and so, how was that decided? This business decision that you have to run 60% off, here you took the help of data and decided from the data that this is what we will run because this is the beneficial decision for the organization given this is the budget, so and so constraint. So this work is differentiated. So if I talk about an analytics career roadmap, then we need some tools and then we need some problem solving skills.
Tools are very common. What tools do you need? One, you learned SQL. SQL is bread and butter. Why? Because the companies, the scale at which they are getting data, that scale is very massive. Imagine if a big billion day sale is going on in Flipkart. Imagine Swiggy Zomato, Cred, PhonePe, the transactions that are happening are happening in abundance.
So you have to get the data first. After that, you will do something in it. You will make a report or you will get the insight from it. So SQL is bread and butter.
Without SQL, you cannot move ahead. The second thing is that you should get a little bit of a reporting tool, which is like Power BI or Tableau. Don't think too much about what you should learn. Whatever is easily accessible to you, you can pick any one.
I was in Tredence, so I used to use Tableau for my client. When I came to Swiggy, they used Power BI. Now I have gone to Walmart, so they say, whatever you want to use, do it, we have both.
So tools, I mean, it doesn't make much difference what you want to learn. Learn anything, you should learn a little bit. Then comes Excel. You eventually have to present to your stakeholder, see this is the insight, you made a pivot.
And Python, a scripting language should come, that sometimes there is a task of machine learning or some task of automation, so it is Python or R. But the major work, like I said, everyone will get data from tools, it is not a big game. Even MIS analysts are doing the same, they don't get money. When will you get money when you are a good problem solver? Problem solving means, the main issues that I have, the main big problems, where I will sit with the business stakeholder, I will discuss it and solve the problem along with it. For example, when we talked about 60%-40% coupon, so here you have to understand which customer set I want to run this experimentation on. This will take your mind, right? You have to decide that factor.
And there is money invested in organizations. With that answer, you will have to understand that crores of rupees are invested on that coupon over the next couple of months. When I am doing marketing in BigBillionDay sales, so tell me when I will show the product, when it will be on sale, you will need data everywhere.
And all these companies, their decisions are data-backed. Every decision is backed by data. Therefore, your importance, the money you are going to get, the growth you are going to get, will be because you are a very good problem solver. Problem solving comes slowly. One is business doming, so that I know what food tech is.
What kind of pillars are there in food tech. That will come with time. But there is an art, how do I break down that problem? What do I have to solve first? Let's assume that in Switzerland, orders have fallen from 30% to 10% in a sudden, from 2-3 weeks. So what do I solve first for my team? What a big business, so many pillars, there are delivery executives too. I have reached so many cities, I am in 500 cities.
So how do I break this problem? Identify, who do I have to solve first? Where will the most ROI be generated? For all these things, generally, we get growth from organizations. That is the main role which we need to learn. I think it is very important to have functional knowledge.
For the business you are working for, if you understand functional things, then you will be able to combine technology there and do better things, write good logic. Now tell me, Gaurav, first of all, you are telling very well, and guys, this is something that when a person is working, all these things come out of experience. So that's why it looks so organic. Now I would like to know how was your interview experience with Swiggy? And the two start-ups that you worked with, and the experience of Swiggy, the challenges, like you are saying that you learned a lot from Swiggy, tell us about your interview experience and how our freshers got help from the new kids. So, first of all, around 2019, I decided that I will leave Treden and go to a product-based company.
And there are many reasons for that. As you said, if you club the business domain with experience, then you will understand. In the role of analyst, the biggest problem that I faced in a service-based company was that I was working, but I was not able to relate to the business decisions that I was taking from my work. So that created a lot of confusion. So you gave me the data, I took out the data, I took out some insights, but what happened after that? There was a gap, maybe because I am in India and my client is from TOS, his business understanding is very limited.
The flow of information was very less. So by making that factor, let's leave here and go to a product-based company. And let's try to go to that product-based company where we ourselves are a customer.
Why was that important? That I can understand how things work. So when I interviewed Swiggy, I interviewed a lot of companies. And their interview experience is very similar. Everyone's interview experience will be similar.
So you take Swiggy, Mintra, Flipkart. That experience is something like this. The first round will be your SQL, the technical round. In today's time, another round was done by the company when I was in Swiggy, so we did another addition.
We initially give you a test on hacking, in which there are SQL questions, there is a problem-solving question. First you clear that, then you get into the real SQL question. So there are SQL questions, there are limited topics. You have given 10-15 questions. And every round is an elimination round. So that was the first round.
After the first round is cleared, then comes your case studies. So because I have two years of experience, and in every round, your interview is grilling. I mean, it's grilling in the resume.
In the project that I mentioned, what exactly did I do? And they are trying to understand that how much depth is there in the solution that I am mentioning here. So those discussions were happening in every round. In case studies, predominantly, any generic question could have been there. So in Swiggy, I was asked, we are doing batching, we are solving batching. Batching means that at one time, let's say Gaurav and Ajay's house is nearby and they ordered something at a similar time from a very similar store.
So if there was only one delivery executive who delivered both the orders, then his time would have been saved. The company would have saved money. So to solve that problem, I was asked a lot of questions. First, I was told what happens because I don't know about batching. After that, in that journey, how will you define the metrics? What is the success metric? What is the failure metric? All those questions were discussed.
Then there was a very generic case study discussion that let's say how many OLA cabs are there currently in Bangalore? How will you estimate this? So the same problem solving. So the emphasis, whatever company you see, it is similar that the SQL is very strong, then your emphasis is that you know problem solving very well. If these two things are actually very strong, then the next round is simply that your hiring manager is trying to understand that you have already left two companies in two years.
So now the third company is so soon, will you stay or not stay in my company? So that was a question. I gave a little answer to that, why am I leaving? I was very clear in my mind. As I told you, I am working, but I am not able to link.
And they realised that you are right, it is a genuine thing. And there is also resume grilling, there is also a case study. So the process remains to this extent. The only thing is, whatever you have mentioned, you should come in depth. This is an important aspect.
No DSA round? In analytics, there is no need for data structural algorithms. There is absolutely no need. If you go to the data scientist site, there you will find data structures and algorithms.
There you will find system design. But analytics is not about data science. It is not about DSA, it is not about the OOPS concept. You have to learn a lot of different things.
That is why if you see today, people want to come to analytics from non-tech background. Why? Because they feel at a very low level that they need to learn coding. Which is somewhat true. But as you grow, you should know things. You should also know machine learning. If you lead the team, you should also know Python.
With time these things happen. I have a question from you, if you don't mind. When you completed your time in Swiggy, then you were going to Walmart. How much was your package till Swiggy? Just for our audience's motivation. So when I joined Swiggy, then I joined around 12 or 13. And there is a very good story in that too.
I want to tell you. So first of all, I took out the company's interview. And there was an offer letter. I didn't have a lot of negotiation space. So initially they gave me the offer letter. But I kept taking out offers on offer.
So I took out a lot of companies. Almost 7-8 companies. At last I told Swiggy, this is my offer letter and I wouldn't join your company because the other company is giving me a good offer. So why should I join you? So they increased the package. They gave me a joining bonus of 12-13. My reason for joining was very simple.
I was getting more than 1 lakh from Ola in Swiggy. But I didn't join Ola. I joined Swiggy.
Because I had taken out a lot of information about the team. I talked to the manager at both the places. I couldn't get a lot of information in Ola about the team and the work. I got that information in Swiggy. Therefore I joined Swiggy.
After that, when I got two promotions in Swiggy, the salary, the last salary when I was there, it touched around 30 as a fixed component. And it had a lot of ESOPs. Swiggy was a very growing start-up. It was becoming a company of 1 billion to 10 billion dollars. In the meantime, I was getting stocks in every promotion.
Therefore the reason to stay was actually a little longer. Overall, the package was very big in Swiggy. This was the only base. It was around 30 when I became an analytics manager. After that, I had a lot of ESOPs. I had 25 LPAs and more than that.
I think I got total ESOPs of 57. I had a lot of bests in that. That was a different component.
There was some 10% of variable component on this part. The package was decent. If I start from 12-13, I saw a tremendous growth factor. I can see your confidence the way you are telling things. It happens, sir. I think I always say that there is a company that you need in your career.
Some people change a lot of things. In my case, it was Swiggy and I very much highly rate Swiggy because the people there ensured that I am learning the right things. I am going in the right direction. Before that, I had not got any promotion But I got 2 promotions in Swiggy.
Within 8 months of joining Swiggy, I got my first promotion. There is a little luck. There is a little luck. There is a company, managers, good people, good people, good people. All these things are connected.
In your career, you see that you have done a lot. But that was not in the credits. It was not in the Implement Analytics but it was in Swiggy.
I think in everyone's journey, I think a company comes where you do very well. When you get a chance and get good people, you stay there for a long time. I learned this from there.
I stayed in Swiggy for almost 3.5 years. In that journey, I got so much hype that the market had boomed so much that people were leaving and going for a double salary.
Because the market was at almost the booming level around 2019, 2021. I stayed. I decided that I will not get money today, I will get it tomorrow. No problem. The work that is being learned here, the people you can grow with them. But with time, I think a lot of people left Swiggy because of, I think, because of pay scale and all different reasons.
But that was probably the turning point that happened in my career. So I have a question from you. Swiggy gave you a lot, you learned a lot. If you have to rate from 10, how much will you rate Swiggy? I will highly rate Swiggy probably to 9. The reason for that was that you have seen a lot of things being built. You have seen things being built from zero.
As I said, there is one company that takes your career up a lot. So Swiggy turned out to be that organization for me. Therefore, I will give it a 9. After Swiggy, I would like to know from where did this Walmart come? Your interview experience, how many rounds were there? And what are you doing there now? What activities do you do daily? Now you are a senior data scientist and you have gone to a level. And no doubt, your package was very good till Swiggy. Now in Walmart, it seems that you have gone to a better level.
So tell us a little about your Walmart and why only Walmart? Did you give any other interviews before that? And please tell us a little about that. Interviews were given in many places. In Uber.
In Uber, I went till the last round. I was very confident. Somehow, I made some mistakes in Uber. I learned a lot from that. Walmart, why as such? There was no reason.
I had already reached the analytics manager position. And now I had a doubt in my mind as to what to do next. So when you reach the managerial position, you have, let's say, 40% of hands-on job. And then you are also managing your teammates. You are guiding them, you are improving them. I feel that it was happening a little early in my career.
I am touching almost 6.5 -7 years. So in my mind, it was already a back-end thought that I have to do something hands-on. At this point, I feel that Walmart is a company where you can set a very long hands-on career. So from there, I got the thought that HR reached out to me.
I was also looking out for a job at that time. Secondly, it is a very stable organization. Given the market conditions, you would, at some point of time, try to figure it out that the career that I have spent is almost a start-up.
And 6.5 years is a very fast-paced start-up. So you have to put in a lot of effort. Everything is done. So now, at one point, I thought that let's go to a bigger company and see that culture. And a new domain.
You saw e-commerce, so let's see something new. That was one of the reasons where you decided. But the interview experience I think was quite good. So predominantly, the work that I did in Swiggy, which was helpful for me, because I worked a lot in demand forecasting. I was predicting how many orders should come in Swiggy, in any area. So based on that, we used to hire or we used to stop the hiring of delivery executives.
Now, because this was a supply chain domain of Walmart, so the project that I did there, I started having a lot of discussions on it. There were a lot of detailed discussions on why did you use this model for forecasting? Why didn't you use other models? How did you forecast on festival day? So the guy who was taking my interview was a PhD and he was building something similar in Walmart for that. So two or three rounds, two rounds were only my discussions. After that, the first round was SQL, then a supply chain case study was given, that had to be solved.
You could use any tool, Excel or Python. So I did that assignment first, and then the case studies were done. And then majorly, I think this was more around what is the role, what is the responsibility, what to expect, and what kind of complexities you are going to deal with. So I think there were a lot of similar discussions. There were a lot of detailed discussions.
Technical rounds first, in 2-3, things were clear. Next round was more around how is your business aptitude. Do you really understand the business or not? Or the problems that you solved, how much depth of knowledge you had. Those discussions were in detail. So that was my overall experience there. Okay.
How is it going? Are you enjoying working at Walmart? How long has it been since you joined Walmart? I joined Walmart around 7 months. But I think it is a very good experience. For me, my experience has always been in start-up, as I told you. It is a very fast-paced start-up. When you reach here, you see a startling change.
A process-oriented company comes. Everything works according to the process. In Swiggy, it was not like that. In Swiggy, if you want to do something, you do it.
It failed. So they have a very different thought process that they work out. It works. But I think as an organization grows, specifically to a scale at like say, Walmart or Google, here you may have a lot of process-oriented work.
It works very well because you cannot scale an organization like that. I think it is a very good experience so far. I think I am learning a lot.
It is a new domain. from analytics to data science. It is Here, it is data science but it is analytics.
It is a combination of analytics and data science also. I am getting to learn a lot of new things. I am feeling very good so far.
I think this is how touch-board works. So guys, I would like to say one thing. As much as I have shot a podcast with you, seriously, I have learned a lot. I mean, I have an idea of fields because I have a front-end domain but I keep learning when I do a podcast. But I have learned a lot from you, seriously.
And I believe our audience must have learned a lot. And if you have learned, then please like this video and mention in the comments which segment you liked the most. Now tell me, Gaurav, that this is fine.
You have got a job. You are working professionally. You are very well settled.
And you are in Bangalore now. And what else do you do in your free time? Is this the only boring life? No, no. Because what happens in IT, sometimes if you don't have anything extra, then no matter how much you earn, it becomes a little boring. Is there anything like this with you? No, not at all. There are a lot of things that I am doing.
I have recently started my YouTube channel by the name Decoding Analytics. So whatever I have learned there, I tell about it from my side. My wife, she works in Amazon. She also shares her experience there. So that is something that I have newly started.
I have also been very interested towards mentoring. I am very active on LinkedIn. I keep posting there. So that is one major part that takes around. But I generally play something out there. For example, there is a sport called squash where I am playing.
I had a very good experience But I have also suffered a lot there. So that keeps going on. These are the few major things. I think mentoring is something that I have been very keenly trying to do. And that is something that I am trying to build on. So that is one part.
Very nice. So we have given the link of their LinkedIn handle and YouTube channel in the description. You can go and check it out. Okay. So before leaving, I would like final advice for our students who want to become like you, who want to become better than you.
So what advice would you give them from your experience? Although you have told very good things. But before leaving, what are your two words for our students? I will just say that there is a lot of confusion in everyone's career. Especially when we don't come from a good college, there is always doubt in our minds. There is self-doubt whether we will be able to do it or not. There are a lot of things in our minds. I have gone through a very similar journey.
But the only thing is to work consistently and connect with good people. Because the mentors have a lot of capability who change your career. They give you the right track. When I was starting, I didn't have that vision. My vision was to earn money.
That is a very small vision that you can have. And that doesn't lead to growth in your career. There is growth in your career when you understand things in real life. You start enjoying it.
And as things happen, your career grows. Find right people. There is no shortage of good people in today's time.
Meet good people, connect with good people. They can be in your company, they can be your friends, colleagues. But if they are related to the industry or not, they will share very good information. That is my advice. Information is abundantly available on YouTube.
Everything is available. Connect with good people and make some mentors. Gradually, the success you get in 2-3 years may be found in 2 or 1.5 years. That is my advice.
Thank you so much Gaurav for sharing all these insights. Thank you once again for sharing so much organic content. I learn something in every podcast. I have learnt from you, growth mindset. How to create a growth mindset through your learning. I think Gaurav is the right example.
So thank you so much Gaurav. And guys, if you liked this podcast and found it informative, then press the like button. Like AMS 5000 for more interesting podcasts. If you are new to this channel, then subscribe along with the bell icon. I will see you in another podcast. Till then, thank you so much.