I know data scientists in india who make four or five crores i studied in uh in a very small engineering college and thankfully i got a very good gate rank which was number two i called data scientists the sherlock holmes of data again it’s not that you should have a

Four year degree where you have studied tons of mathematics no who have no technical background who have successfully gone into data science careers amazon doesn’t care whether you come from a stanford or whether you come from iit or whether you come from a small college some people

Say hey i will work for three months and i want a job at fang or nay i don’t care which college you come from if you have skills i am willing to hire you and we pay more than man companies by the way for entry-level roles and also for senior roles hey everyone

Welcome back to e-learning bridge i hope you guys are doing good and is staying safe so i am back with another amazing and really exciting podcast for all the data professionals and my lovely data community so few weeks back i got a chance to visit bangalore and there i

Also got a chance to meet the og of data science and machine learning in india mr srikant verma and today you will be getting the crisp answer for most often asked question in the data science and machine learning engineering and sri kant don’t need any introduction his profile already speaks for him like

Someone who passed out from indian institute of science became a part of first few members of yahoo’s team in india and then worked in amazon for five years as a machine learning engineer and after that started the entrepreneur journey so please make sure you watch this podcast till the variant this

Podcast is highly highly recommended from my side he is highly experienced in the data science and machine learning and before starting the actual video make sure to like this video in the big numbers i have planned few more amazing podcast on machine learning engineer and i will release those podcast as soon as

You will complete 3000 likes on this video and yes don’t forget to subscribe the channel and press the notification icon for more amazing content related to data profile so thank you so much srikant for joining me on my podcast and honestly i’m very very much excited to have this discussion around the data

Science and machine learning but for the audience who are watching you for the very first time can you give a short introduction about yourself and anything you want to share about your professional journey first of all thank you shashank for being with us today and

For all those who may not know my name is srikanth verma i am one of the instructors at scalar for the data science and machine learning programs and prior to this i was a co-founder of a startup called applied roots for about four years also which got

Acquired by scalar and that’s how i ended up being a core component or a part of the scalars dsml programs and prior to prior to being a co-founder of a startup i was at amazon for about five years working both in u.s and india primarily on data science

Related problems and prior to that i was actually a startup co-founder building a computer vision startup so most of my most of my professional career has been uh either building startups from ground up or building products from ground up and i’m super excited that i’m getting

To do that again at scalar where we’re building the whole data science program from ground up awesome awesome fantastic journey and that’s why i’m very much excited because on my channel i’ve been getting questions around this data science ml part now it’s your responsibility to watch this one till the very end because

Whatever you have asked me so far i’m gonna put those question in front of shri khan so that he can answer it and his experience is something which will be really helpful for you to start your journey in data science so shrikan next question and this is the

Common i asked this question to everyone so how how did you start your journey into the data science like was it by choice or just a random decision so in my undergraduate final year again just to be clear here i studied in uh in a very small engineering college i knew

This because i was the first batch in engineering college so in my btec final year i started really liking this subject called ai neural networks image processing etc and thankfully i got a very good gate rank which was number two and i ended up at an institute of science i still

Remember the very first week at the institute of science which is again in bangalore where i i went to a professor professor chenowship and i said hey i want to learn this thing called ai and i heard that you are one of the best profs

Around here he gave me a list of courses to take so all this happened before data science and machine learning were popular there were actually no jobs when i was interested in that field it so happened that i got lucky as soon as i graduated yahoo labs came in for

Placements to an institute of science okay and we were one of the very early people who joined yahoo labs as research engineers without having a phd so it was a combination of luck a little bit of a little bit of interest in the subject certainly because uh going through the

Grind of a lot of heavy mathematics at the university of science was never easy okay but then we also got very lucky like obviously like you initiated that part like you showed that interest to reach out to your professor and know more about the aiml part this is amazing

Let me know in the comment section because we all are into this this circle where we take some decisions very randomly right and mostly happens with these students so anyone who has take that kind of decision let me know in the comment section because for me like moving into

The data engineering was something just i i got to know about it and then i showed my interest into it in my first company and that’s how i moved it so i was definitely i can relate with your story so you started with yahoo labs then founded a company metric labs then

In amazon and then again founded a company applied roots and now working with scalar to empower the data science and ml community so in this journey right so which part was be more challenging for you and what was the face which you liked the most

So i would actually put it this way at every stage it was a learning experience at yahoo labs it was how different academia or a university is to a workplace right i had to unlearn a lot of what i learned when building my first startup we were phenomenal techies we could build all

The tech in the world but we didn’t know how to build for the world how to sell it how to market it we had no such skills so at amazon i i often tell people that at amazon i did my mba because in those five years i learned to

Build products i was one of the earliest scientists in the advertising org i got to interact with very senior management on how key decisions in product design product building product showcasing are actually done to customers clients so the customer centricity of amazon literally became my ethos

So i often tell people that at amazon i learned to do i did my mba at amazon and then when i started the next startup we bootstrapped the whole thing we never raised any external capital and it was one hell of a journey i had five other co-founders work with me a phenomenal

Team so i would say that every stage has its challenges at scaler today our challenge is how do we empower as many people as possible as possible in an instructor-led program with deep mentorship so every stage has its own challenges every stage teaches us new things at every stage we make mistakes

That’s the beauty of it and we try to avoid that next stage so of all the things again there is always recency bias in our minds so if i have to incorporate that recency bias i would say prayer roots had been a terrific journey okay but even in the

Last four months what we’re building at scalar is is is terrific and i see a huge road map of what we can do and how we can impact the next generation of learners i think that’s the same thing audience is looking for like something very crisp thing should come into the

Data science domain so that they can learn and i think that is the gap you are trying to fill as well right into this edtech domain and data science symbol everybody knows uh it’s at its peak and it’s just the i hope this is not the peak i hope the

Peak is away from us yeah but like very very fast growing things right lots of folks want to move into it so not just folks moving in i also we also talk with hundreds of companies uh on understanding what they need what they want our students to be equipped

With correct that’s one of the key tasks that we do all of our team actually sits in one on one discussions like these with industry experts and finding out what they want yeah and i mean the amount of requirement has simply exploded over the last couple of years

Now there is a huge spectrum of roles in data science that companies want to fill desperately including our own team at scalar right we are we’re hiring very aggressively and we’re looking for the best minds to join us right so like you are just trying to upskill the students

According to the industry not just a very old school curriculum of data science yeah so that’s something that i learned very early in my career a lot of beautiful elegant theory that i learned in college was not applicable in the real world and i learned it very quickly

Thanks to my team at yahoo labs so now we don’t want learners to make that mistake so we go back to the industry we talk to hundreds of actual data scientists hiring managers all the way up to vice presidents and we ask them what is it that you want and we repeat

This survey every four or five months so that we are not lagging behind from what industry needs it has to be data driven at the end of the day otherwise how can we be data scientists or machine learning scientists exactly completely agree and i think really appreciate the vision you

Are on and i think it will definitely help all the aspiring data professionals uh lots of people want to move into the data science and like even this is something which is very very popular among the college grads as well right they are exploring these fields so now be the college grads or

The entry-level freshers or working professionals working in different domains like testing profile software engineers or tpm program managers they also want to move into it but the main challenge is how do i transition into the data science because finding the right region and finding the right roadmap and finding the right resource

Everything is difficult nowadays so what’s your take on that part so first let’s answer the question about the reason the reason right i would i would suggest people don’t hop on to a bandwagon just because it is popular so i would say if you have that curiosity if you like math

Or if you have if you want to be a sherlock holmes i call data scientists the sherlock holmes of data that’s because i just i can i can easily get a few gb or few terabytes of data now as a data scientist or a data analyst or a machine learning scientist or a

Machine learning engineer my job is to make sense out of it so if you have the mindset of making sense of just raw numbers this is the field for you that should be your reason that you feel passionate about finding insights from raw data using a bunch of mathematical statistical or machine

Learning tools or even coding how you do it come secondary the reason should be that you want to be the sherlock holmes of data that should be the right reason exactly again if you don’t want to be that please don’t get into this field this is the wrong field for you you will

Not enjoy it number one number two if you want to get into this field of course you have to learn the tools the the techniques that are used so there is surely programming okay so first and foremost you have to know the basics of programming again you

Don’t have to be like a software engineer or a data engineer like you the basics of programming you certainly have to know sql basic databases etc most importantly you have to have a strong applied foundation i’m not saying theoretical i’m saying applied foundation in mathematical topics in the

Context of machine learning again it’s not that you should have a four year degree where you’ve studied tons of mathematics no you have to have you have to know basic probability and applied statistics you’re doing some linear algebra some calculus what is required for machine learning and then you learn a bunch of

Machine learning techniques of course most important amongst all this is you have to solve real world problems the common mistake that i see a lot of people do is they just learn a bunch of equations bunch of libraries but they don’t know how to solve a real-world problem yeah

So that is the key aspect you learn all these tools and solve a bunch of problems and build a strong portfolio of work that is the core essence that’s how you get into data science correct so what we have done on the same end when when we were designing the whole scalars

Data science and machine learning program is we said hey let’s go to the industry find out what problems they want us to cover in our program yeah and we have tens of case studies we have close to 70 80 case studies today in our program which are actually inspired from

Problems that our industry partners have told us and we do very case based so every concept you learn we solve a real world problem with that and that’s how you learn when you solve 80 problems that’s like having two to three years of real world experience in

Data science exactly so we have taken what what we think are the most important and backed by actual data from lot of interviews from industry experts and said let’s design a case study driven program at scalar yeah yeah i think this is the most practical way to learn anything irrespective of the data

Science data engineering yes exactly the best way is to actually build data pipelines pipelines correct correct and this is great i think this this is something which these guys are also looking for uh where they can get an environment of practical learning not just something uh going with the

Programming and typical data science stuff so this is great and you can let me know in the comments section like uh if what kind of road map you are following and i would be really interested to just check whether you are on a right track or not like whatever

Shri khan mentioned so that would be good so great now moving on to the next question shrikant since you talked about the portfolio part you mentioned that part and the practical learning cases studies so uh you talked about it but anyone let’s say who’s new into the data science part so

How can they like build a strong portfolio like how to even begin with that what are something they they can explore in order to build those kind of practical projects somehow let’s say self-made projects and like anything which can uh just help them to stand out

From the crowd in the short listing and the interviews so i would say that for every concept you’re learning even simple libraries like numpy pandas very simple libraries that we use in data science pick up a data set there is no dirt of data sets today you can go to

Platforms like kaggle or there are tons of open source data sets open sourced by some of the world’s largest companies get the data set try to look for insights don’t just do data analysis for the sake of doing it a lot of people make this mistake that

I have some data i have to run some code i’ll show some graphs and i’m done no no no your job is to get the most valuable insights from the data so i would say from the beginning onwards till the time you do state-of-the-art deep learning models try and pick up slightly challenging

Problems and for every concept that you learn it could be as simple as pandas number or a simple probability concept try and apply that on a real world data and that way actually you build the real skills that are needed for example if you go to an interview very often there

Is something called scenario based interviews in data science right you’re given a real world scenario and you’re asked how do you tackle this problem if you have not done it it’s very hard to think from that framework of mind yeah right so my suggestion is build a series

Of portfolios from the simplest ones to state of the art right for example if you’re learning deep learning there are so many publicly available computer vision data sets or i would say something much simple take 10 of your friends click five pictures of theirs in different lighting conditions build a

Simple convolutional neural network which is which is a deep learning model to detect whether to do let’s say i just give a new picture which of these 10 friends are these this is very simple module one yeah like we have we have learners who have actually taken their car in india

They’ve recorded the data using webcam and they’ve tried to simulate an autonomous car using a simple i20 wow so actually we have a case study like that that we do using u.s data this learner said hey i can just grab my data i just have to put a webcam and he

Just grabbed this data and i was mentoring this student at applied roots and he he grew phenomenally well because he did something that was non-obvious that was exciting interesting and not just for the sake of doing it but for the actual fun of solving it correct

Yeah i think business crux is also very important to understand right uh for the sake of doing just don’t do that list i have created this project and put it in the resume a lot of people do projects to put it in resumes not to gain insights or learning

That’s unfortunate they need to understand this difference like i whether you are prioritizing the short listing or resuming or your actual learnings because that was something which will be helpful in the long run i think in the three idiots movie and somewhere they say that go for excellence successfully anyway exactly

So same thing here also i forgot the exact hindi wording of it so but but the logic here is you build the best you solve the problem the best you can shortlisting right it’ll be it’ll happen eventually correct and i think this is the need of

The time as well like the way interview pattern has changed right if you are just stick to your uh typical skills or let’s say just technical deep dive into deep learning or these theories and all probably you can crack the first screening rounds first and second

Technical but after that it will be more about your practical understanding yes how can you solve the business cases studies and that would be your day-to-day activities in the companies as well exactly i do remember myself in amazon as well and that’s the best part i learned there my manager like uh kept

Me pushing for this part invest your two three weeks just to understand the business problems yes right interact with the team he gave me that free hand just deal with this project interact with the clients and understand why why you are even why are you even solving this problem

And whatever they are saying like are we supposed to exactly do the same or we can do it in a different way so this is the main part and i think completely agree with your point just to add to that same on the same thread when you solve a problem always ask

Yourself if you are solving it at that company as a data scientist what are the most important things that you will solve for who is your customer or who is your client and what are they looking for in this analysis and try and solve for that right because the whole code

Part or designing the things won’t take much time because the initial phase is very much important with understanding the whole business problem crux while you are building it that is i think the whole 50 percent part of your entire product life cycle just to add to that in our world we do

We spend a lot of time cleaning data also which probably a data engineer might have a different objective of course understanding the problem is mandatory so is cleaning pre-processing that we often overlook as tk challenger but that’s if you don’t have clean solid data all the models you have built are

Just garbage right right yeah again you guys can let me know in the comment section you have created projects just for the sake of shortlisting or just to get the actual learnings because in my college time i literally made these mistakes i copied projects from the github and all showcased in my resume

And literally didn’t help me out at all i will tell you i’ll tell you a fun example during my master’s days in the summer vacation we were actually solving a very interesting problem which is uh this was i think cricket season so we said from the cricket video

Itself can we this was with professor charanjeep at the machine learning lab as a college student we wanted to automatically extract force and success and automatically create the highlights we just did it for fun this was just a fun fun project that a couple of my friends and i were doing

It so happened that when we were interviewing at companies the interviewers were so happy to listen that we did it for fun we wanted to create automated highlights from a cricket match right and the interviewer said cool guys tell me how you solved it and we would spend almost 40 minutes in interview

Discussing how we solved it the techniques we used why they worked why they didn’t work and that shows initiative at the end of the day a company wants or a manager wants his team to take initiative to solve problems right and it so happened that what i did in college was super

Helpful for me for many many years later yes this is cool this is cool a very interesting story and the fun part is something which is really really important i think you should enjoy the whole process and why you are building it i think we all are on the same page

For that part so she can i have one more uh like frequent ask question you can send that is dedicated to our non-tech folks on folks who are coming from completely nonsense background so again i have seen folks uh like dominating in this part like the data science ml and

Majority of people are coming from this background actually non-tech and non-cs but still there is a like a huge huge number of people who want to move into this data science part and uh completely coming from non-cs they don’t have any programming background they find it very

Very challenging as well that how they can even move into it whether companies uh will shortlist for the interviews because they are not coming from the cs background and all and uh she consists you have taught folks as well right you have seen this journey how even companies are

Recruiting and even in amazon right on the yahoo labs and other companies you would have interviewed the folks as well so based on your experience what would you suggest to them that how gracefully they can move into data science or the data analyst part and if you think there are

Some myths uh related to the non-cs folks who want to move into it uh please try to let’s break it down sure so i’ll tell you both sides i don’t want to just paint a very rosy picture i want to paint a realistic picture first i have worked with students who

Come from non-cs non-technical non-math non-science like bcomp or m-com or mba who have no technical background who have successfully gone into data science careers amazing a lot of them again realistically speaking a lot of them actually become data analysts and then they work their way up into data

Scientists that’s a typical path there are exceptions there are people who come from non-cs backgrounds non-tech background so become data scientists automatically okay but not a vast majority of them 10 20 maybe 30 percent of them become that realistic i’ve seen 30 percent of people become that a vast

Majority become data analysts there are different titles here some of them could become data analysts product analysts there are different variations of it but these analysts actually are still doing data science they crunch data they use statistical techniques mathematical techniques some bit of programming some tools like tableau sometimes sql to

Fetch data and actually create insights out of the data at the end of the day nobody asks you where the insight came from the inside could come from an excel sheet or a shell script or a python script or some spark code that you have written yeah the

Insight is the important part so my suggestion is for everybody especially non-tech non-cs people if you have that sherlock holmes mindset of trying to find patterns in data trying to find insights in data you should pick the basics of programming you don’t have to be an expert software engineer you have to

Know basic tools you have to know basic probability statistics and all these are learnable yeah if you learn it from a practical applied standpoint this is not rocket science again i have worked with about about tens of thousands of learners at applied routes and i teach a lot of

Students at scalar who are non-tech non-cs people and they’re able to grab probability stats as long as we give real-world examples and teach them in the right way and these people many of them will become data analysts or product analysts which have terrific compensations today yeah like at scalar and similar startups

Fast growing startups we give very competitive to entry-level software engineer salaries for data analysts because we are looking for the best of the best guys here now these guys after working as data analysts for a couple for a year or two they can gain more depth of expertise more deeper mathematical

Concepts become better at programming and they they can go from data analyst to data scientist roles so it is doable but you cannot skip the part of learning the foundational techniques and tools right if you think i will learn one library and i will become no you have to

Learn the tools that are used you have to know basic probability basic statistics basic mathematical tools and you always have to have that sherlock holmes mindset okay what is hiding here let me try to figure out what is here that that if you have intuitive insight your degree doesn’t matter anyway

And i think finding those inside it’s a uh something you are just seeing some picture and visualizing trying to imagine like what is happening behind the scenes and trying to figure it out so this is cool and what are some myths like you have heard from the folks that

Uh non-cs folks can’t do these things if you have a lot of people say that but it’s not true and thankfully these myths are being busted by people who are getting into these roles like i’ve spoken to some of our learners who come from completely non-cs background and they actually compete

With cs folks when it comes to some of the data analysis tasks so it’s all about how much effort you put in how much how much intuition you are building to solve real world problems of course let’s be also realistic that they have to put in more effort than a

Cs guy yes let’s not fool ourselves otherwise exactly a cs guy already knows sql csg already knows some programming might know a little bit of probability and stun they have upper hand they already have they they i just at the start of the race itself they’re ahead

Correct so you have to work hard let’s not ignore that fact there is no shortcut to hard work you have to work hard but it is very doable again not just to become a data analyst i’ve also seen some non-cs folks become data scientists yeah directly because they also learned the depth of

Mathematics because they fell in love with it correct correct see lot of people probably we were not taught mathematics in the right way and hence we start hating math in school but once once it is taught in a proper way like for example when we teach probability and stats we actually take

Real data from coveted vaccination tests into the classroom we get data from cricket matches into the classroom and people love that how math is applied and people who have never studied probability and stats they say hey we want more i want to learn more when you teach calculus from an applied

Standpoint people say the calculus fear is gone all the fear that they have gotten in the 11th and 12th is gone now so it’s all about the right perspective of learning and right perspective of teaching especially if you’re a teacher you have to teach it the right way if you’re a

Learner learn it the best way most applied way possible cool and then this is really helpful for all the folks who are watching and belong to the non-serious background i think this will be motivating for you guys as well let me in the comments uh like how are you

Feeling now after listening to shri khan’s word just to add to that one more thing that we have learned from our own audience is uh we’ve just we’re just launching the data analyst track at scalar okay so we initially had the data science and machine learning track but

We learned from our audience that there are some audience who are coming from non-cs and non-tech backgrounds and we thought let’s start this whole new track which is a which is sort of like a fast track for them to get into the data science careers

So we saw this again when we talked to companies companies said hey i have 10 data scientist positions i have 30 data analyst positions can you give me people then we said cool what do you want they gave us a list of topics that they want

People to be expert at and we learned it from the industry and we have just we just about to launch the data analyst track some of our students are already enrolling for it we’re starting it from this month onwards i think this is the exclusive information here so we will provide the

Link in the description for sure these programs and uh as a non-cs let me know in the comment section which one would you prefer the data science in a like at your start or the data analyst profile which one do you find a bit easy to start with this is the question

Basically for the folks who are coming from the entire three colleges right and uh i think majority of people are coming from those kind of engineering colleges they are doing good nowadays but still there are folks who feel really demotivated and they have a dream to

Crack big tech forms like the man right and amazing product based companies but again the main challenge probably could be the tire three college part and especially they are also interested in this data science and machine learning and the challenge could be they are not getting the kind of environment right

The way people get entire one colleges the community and guidance from their seniors like that so again you have mentored these kind of folks as well so it would be really helpful you can guide the audience who are coming from the atar 3 background how they can prepare themselves to

Move into the companies like mang and especially for the data roles cool so i can i can completely empathize with them as i told you i was the first batch in my btec college and it’s been one hell of a ride for me great right so i’ve also seen students

Who come from smaller engineering colleges or smaller science and math colleges amazon doesn’t care whether you come from a stanford or whether you come from iit or whether you come from a small college that’s a fundamental thing but also remember i’ve been a hiring manager at amazon i can

Only do maybe a few hundred interviews every year correct imagine if i’m the hiring manager i can only do 200 300 interviews a year i can’t do more than i don’t have time right so what do i end up doing i end up picking people based on some pedigree or some prior

Experience that’s a reality because i can’t interview 10 000 students but still companies like like the top fang like companies or fang or fang like companies the aspirational companies as we call them they are now going and hiring people based on skills that people have either skills that people can showcase again

There are multiple ways of showcasing skills for example in the software engineering world there are all these hackathons competitions uh the code chef all these platforms like interview bit right interview bit is one of our platforms and again in our in our software engineering ecosystem we have

Seen people who whose college name i’ve never heard but they’ve gone to the best of best companies and done phenomenally well so my point here is build your skills in the long run that’s the only thing that matters i’ll tell you from my own personal experience i’ve studied at

In instead of science which is india is one of the top universities i have batch mates from my college who have not continuously learned from a tier one college they have not continuously learned and they’ve lagged behind i’ve seen people who whose college name i’ve never heard of but you have

Continuously worked imagine if you’re working for your career for 10 years nobody can beat you correct an iit jee exam or a gate exam or a cat exam doesn’t define you in the first place so please prepare yourself be realistic prepare yourself for the long haul go behind skills

In the long haul nobody can beat you even a stanford phd or a stanford professor can’t beat you in the long run number one number two but don’t be in a hurry some people say hey i will work for three months and i want a job at frank

That’s a reality of it there is so much competition so my suggestion is give your best learn the core skills for example in data science learn basic probability basic statistics programming get your foot in the door first again right now there are so many very fast growing startups for example at

Scalar i don’t care which college you come from if you have skills i am willing to hire you and we pay more than bank companies by the way for entry-level roles and also for senior roles like i am telling everybody we have about 50 data science positions in our

Teams right now in scalar and we don’t care what college you come from please bring skills to the table we have team mates whose college names i don’t know and they’re being paid more than fang like folks of their own batchmates amazing because we care about skills

So if you are if you come from a small engineer in college get your foot into door first fast growing startups there is nothing like learning at a startup in a large company it all becomes too complex get your foot into a startup build things learn skills

Then sky is the limit why do you want to even join bank then go start a startup what the heck are we doing here correct this is very very motivating honestly like i’ve been through the journey it’s not hypothetical i i’m the real world example here correctly correctly um i think this is

This is something uh which you guys need to understand like that’s what in the beginning i said uh nowadays they are definitely doing really really great i’m very much active on linkedin and every day i see post people like again those colleges probably i didn’t heard their names and folks

From that college getting into these man companies and your point is important to listen here that you can start your journeys from startup like this is these are the best places to learn and start your journey i have also been into that phase like when i joined paytm they were

Completely into the startup mode right people are just standing on our head so you learn a lot there and i think we also work with about a few hundred companies at scalar when i talk with their careers team they say verma don’t worry about the place they come

From get me people with skill skills we will get them job right there are like hundreds of startups fast growing startups which are competing with fang like companies and we have like 500 600 companies forget about fang these are all startups right i think the number of the number

Of companies that scalar graduates will be working at i think it will easily reach a thousand soon now given that scale our career stream says don’t worry dude don’t worry about the college and all that get me people with skill skills we will get them through yeah because companies want that

That matters a lot and uh the interesting information like data science hirings are open at a scalar as well so yes yes please please we are looking for great people of course we do not compromise on skills our bar is a inch above fang like companies but so is our

Compensation is 10 inches away from fan like companies that’s amazing that’s amazing cool so now moving to the next question and that is uh like no one can know everything like any domain uh nobody can be a probably master of it they can reach some limit of it uh

So again similar question into this data science ml uh when someone can feel confident that now i’m pretty much prepared and yes this is something or i have achieved this much off into the data science ml part right so how much is it so my suggestion is this if you are

Getting into your first role right just do a few case studies some basic probability stats some basic machine learning basic computer vision basic nlp this field is so vast that i will be i’ll make a fool of myself if i say i know all these fields no this field is moving so fast

That i even tell a lot of our students and some of my colleagues that i mentor and i say if we stop learning and if we stop spending five hours a week on continuously learning we become a dinosaur in five years in this field that’s the speed at which you have to learn

So nobody is perfect if you can solve a few real world problems using basic statistical techniques basic ml basic computer vision basic nlp you’re ready go ahead in case you fail in an interview no you learn what you don’t know right that’s the key who knows everything even the

Interviewer doesn’t know dude that’s the reality of it i’ve been an interviewer in hundreds of interviews there are so many concepts that i learned from the interviewee because i didn’t know this correct i was shocked by how beautifully some interviews solve the problems so i have been fortunate to have learned

From my interviewees exactly and this is the typical process of even cracking the interviews you will definitely fail in couple of interviews and you will learn actually that yes what kind of mistakes you won’t be repeating uh in the next companies so this is the best process

Cool so this is the last question srikanta i have for you and this is again related to the monetary part right and i think this is the main motivational factor for each and every one based on the today’s time the requirements and the aggressively companies are hiring and they like this

Demand and supply part so on an average if someone who’s like entry-level pressure into the data science and machine learning so how much average salary they can definitely expect at least in india and that too in good product based companies the average probably will be skewed because there are large salaries also so

Let’s talk median median yeah the median salary will be about 11 to 12 lakhs in india today the median the lower end of the spectrum will be about six lakhs okay the higher end there is no limit like there is i mean all your fan companies pay about 20 25 ish right

And we at scalar pay an inch more than that or 10 inches more than that so there is a lot of demand there is a lot of requirement of really good folks that’s the catch so if you’re good sky is the limit but realistically speaking a median that

We typically observe is 11 to 12 again i’m not i’m taking a median of all the people yeah you could be from tier three tier two tier one or stanford doesn’t matter but this is the median typically that we observe and i think this is a decent start as well like to

Just start your journey in the uh it industry i think i remember my days i started with five flags per annum that was a lot of money then right but even today a data a good data analyst can easily get 10 to 11 lakhs forget about data scientists at scalar

We hire data analysts and we work with hundreds of companies who say i want a product analyst i ask them what is the comp like 10 to 12 lakhs easily and if the person does well we are willing to go above so they want good people who can take

Data make sense out of it that’s the key so even for data analysts the compensations are very good today i think with these skills you can double down and just triple of these numbers in just one a year and two yeah the sky is the limit yes i know data scientists in

India who make uh four or five crores so the sky is the limit great and great amazing so all the wonderful insights were in this podcast and i think this will be really really helpful for the audience because on my channel most people come from this data background data engineers analyst non-technologists

So whatever question i prepared these were actually came from them i just included all these things so i think these were pretty well answered and one more important thing so shri khan keeps on taking free master classes on scalar right and here like he will teach some really cool things related to data

Science and machine learning so if you are interested then feel free to enroll in these free master classes link will be in the description and definitely you’ll learn cool things from srikant so i hope it truly helps those people who are starting their career or the learning journey itself in data science

Cool cool and definitely we want to help more aspiring data professionals so just like this video in big numbers so that we all together can help all the aspiring data professionals and all the important details and program links will be in the description do check out the

Hiring related links also will be there for the data scientist and analyst post and feel free to put your opinions and thoughts related to anything right if you have any query for this she can’t put it into the comment section i’ll just convey those messages to him sure

And yep i’ll see you next time with something cool again related to the data background till then bye bye so that’s what i had for you guys in this podcast i’m pretty sure this was really really helpful for all the aspiring data scientists and machine learning engineers if you find it

Informative make sure to give a like and share it with your friends and also share your thoughts and feedbacks in the comment section and also if you are new to the channel make sure to subscribe the channel and press the notification icon i will see you guys in the next

Week with another amazing podcast till then just stay safe stay home take care yourself and your family too

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