NO Master’s DEGREE to DATA SCIENTIST @ Microsoft 🔥! She Cracked It In 1.5 YEARS 🔥 35+ LPA CTC ❤️

#Masters #DEGREE #DATA #SCIENTIST #Microsoft #Cracked #YEARS #LPA #CTC

You can expect between 30 to 40 also 30 to 14. wow I applied through career portal except like this is a myth also that it’s not only about ml algorithms I have not done any Masters but still I managed to get this role at Microsoft hey everyone welcome back to e-learning

Bridge I hope you guys are doing good and staying safe so I am back with another amazing and interesting podcast for the aspiring data professionals and my lovely data community and this podcast is going to be super helpful for those people who are on the entry level

Stage and dreaming to crack top Tech giants like the Microsoft Google Amazon and Facebook especially for the data science role and today in this podcast I will be having a discussion with shelja and recently she joined Microsoft for the data and applied scientist role she started her career in 2020 itself and

That too as a data engineer and just within one and a half years she was able to crack Microsoft and that too for the data science role she will be sharing her entire experience moving from data engineer to data science role complete interview process for the Microsoft that

Too for the data science role the preparation the resources the kind of projects she made how this roadmap will look like for the newcomers how you need to prepare salaries of data scientists in top tech companies that means everything she will be explaining here which will help you to start your data

Science career so make sure to watch this video till the very end and also like this video in the big numbers let me know in the comment section which company do you want me to cover next for the data science podcast I will bring data scientists from those companies and

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Alright so thank you so much shelja for joining my podcast and I am really really excited and I know my audience is also very excited because they were kept on asking me please bring some data scientists right from good companies and at least from the bank so here is your

Podcast guys we have a very very talented data scientist from Microsoft so shelter it would be really great if you can give a short introduction about yourself to the audience and little bit about your past academic background or anything related to professional background first of all thank you so

Much for having me here I’ve been following your content since a long time and it has actually helped me in my preparation Journey So yeah thank you so much talking about me I am a computer science graduate I did my engineering from banasity vidyapeeth Jaipur I passed out in 2020.

After which I joined fractal analytics as a big data engineer there I got a chance to work with many fortune five minute clients in finance and supply chain domain and the text tag that I worked basically was on spark Hadoop High Sequel and I was working on Azure

Cloud so Azure services like databricks data factory data Lakes synapse analytics so these were the tech Stacks I worked on and after which I joined Microsoft and here I’m working with the data and applied scientist awesome really really awesome so looks like this is a transition from data engineering to

Data scientists a very interesting story and hope this will help all like the folks who are into these kind of transition States so her experience will definitely help you out so after completing your graduation right you started your career as a data engineer at fractal analytics right as you

Mentioned so when and how did you figure out that you want to make a career in the Big Data especially data science kind of domain because you are already working as a data engineer right so very interesting point I I want to definitely know about this story so to answer that

I’ll take you a little back in time so in my college times in my third year so before that I was only aware about C C plus plus Java so we were we had to do a minor project and I was assigned a mentor she was specialized in NLP so she

Wanted me to make a project on the same so that is when I started to explore python data science Big Data all of these fails which I find very fascinating so that is when I decided that this is something I can make my career into and I am interested in so

After which uh joining Franklin analytics was another big great thing that happened in this journey because it provided me a very good base and as fatal is the core AIML based company so there I was I was able to learn a lot of tech Stacks basically that they use in

Data engineering domain so yes this is how my journey was into data science and data engineering so this is really great and you guys can also let me know in the comment section if any one of you uh who is trying to switch into the data science domain and I’m a data engineer

She’s a data scientist so let me know your choice like if you are at the starting phase of your career what will you pick data engineering or data science so now you’re working as a data and applied scientist at Microsoft so it would be really great if you can share

Your interview experience with Microsoft for the data science profile like how many rounds were there and what all things the interviewers were focusing on and it would be really helpful for the folks who are targeting companies like Microsoft for Microsoft I applied through career portal itself so this is something I’ve

Heard from many people that when you apply from on job produce your resume never get selected yes so on that part on that part I can assure you this thing from my example that that is not the case if your resume is having you have the required skill it does get selected

So that is how my resume got selected and after which uh overall there were four rounds and three out of which out of four were core technical rounds and fourth one was the hiring manager so if I talk about like the in individual grounds so the first round was basically

To test my domain knowledge and mainly about spark and Big Data Concepts so starting from the questions regarding spark architecture and how we optimize things all problems we Face data streaming this problem out of my memory problem so all of these things like overall spark knowledge how much I had

That was tested in that and then we had questions from SQL and we all know how much important SQL is for people in the data domain so questions regarding joins to CTS two windowing functions so from basic to even higher level SQL was asked and then just to understand and test me

On the basis on my problem solving capabilities I was is given small Snippets that I solved in Python so that tested me on various python Concepts as well so this was my first round in the second round and third round combined I would say that was uh kind of more focused on

System design or I would say case study kind of so I was given question a case study where I had to design end to end like basically how the data comes into my system what my system will look like what all tools and tools and text I can

Use to process the data what optimization techniques I’ll use how will I store the data what will be the refresh Cadence My pipeline will look so the entire structure I had to come up with so we all know this thing that in the in these questions there is no right

Or wrong answer uh you adjust basically on your approach and how how good solution you come up with also they used to come up with feedbacks like this may not work since that you can try this out so we all know data science and data this data field is basically a field of

Exploration so I used to take those feedbacks and come up with a different approach and like we can try this out also maybe this can work in a better way so yeah this was overall my second and third round uh there were questions on DSA as well so but yes the questions

Were basic to medium level not not the one that we say that a very tough one so we can expect questions from array string till linked list also but not questions like complex problem DP problems I did not say it’s such questions so yeah and the last round

Being the hiring manage around so here the hiring manager round was a kind of a fitment round as well but yeah overall background that I had all of the projects that I worked with so the interview went around it so the recent project that I’ve worked on uh what

Program I was solving in that project and what approach I was using what challenges I faced and how I was able to come up with Solutions what was my contribution in the project and all of these questions were there in that and apart from that questions that tested

And like do I fit into the company’s requirement their role requirement job requirement is basically so that was tested in that round so yeah these were the overall four rounds I would say they were more focused on technicalities and your skills that were required two motivating factors for the audience was

Watching this podcast right first you mentioned the uh job application part and same query I have also received like we don’t get an interview call if we apply on the career page so this is sorted out here is the example she applied on the career page and she got

The call so never hesitate and never get demotivated sometimes maybe uh that profile is closed or not uh active right anymore then in that case you won’t be able to get the call if your skills are matching then definitely you’ll get the call and the second Point ddsa part this

Is the most often asked question for all the aspiring data scientists how much coding is required like whether companies will ask DSA questions or not so according to salsa yes there will be questions but you need to maintain the easy easy medium till medium level don’t

Go beyond that and don’t even waste your time practicing things just beyond that part so this is really amazing so one like for this amazing interview experience right now and also shelter is helping all the aspiring data professionals so you can connect with her on LinkedIn right I will provide the

Profile Link in the description just reach out to her for any kind of doubt or any kind of query is and she will definitely help you out there are lots of folks who want to become a data scientist but don’t know the exact path because Internet is just filled with

Confusing stuff as well and this happens with everyone at least from the fresher because they won’t be able to get the right track So based on your preparation based on your journey in last two three years right can you please share a crisp roadmap for the same which actually

Worked out for you so my advice for all the aspiring data scientists and freshers who want to go into the data domain is uh like start from scratch and when I say scratch is try to focus more on skills like Python and SQL so if you

Start from the very basics of python and SQL and then take the process a bit fast so when you think that okay you are now good in Python and SQL then depending on if you want to miss your career specifically in core data science then you can go forward and learn ML and

Specialize in that and if it is about co-data engineering then once your act with python and SQL then you can go and learn about how distributed computing Works how spark architecture works and all of the other big data tools so after this you so the motive here is to have

An overall end-to-end development understanding of a project because like this is a myth also that it’s not only about ml algorithms there is a lot of data cleaning data restructuring that happens before that so if you understand in for the data domain people it’s very important to understand the data because

That is the Crux of everything so if you understand the tools and text tag that is why I mentioned that Python and SQL is very important initially so if you understand what your data is how can you clean it how can you restructure it and later how you apply different algorithms

And techniques to get insights from the data that is what a data person is expected so this roadmap that I would suggest for everyone is to start from Basics and then step wise acquire these skills sequentially and that will help you out thank you so much chalja for

Explaining uh your journey at least the roadmap part and how it worked out for you and you guys can also let me know in the comment section based on your experience what do you think what are the top five skill sets require for a data scientist to crack the good

Companies another important question and that is like is master’s degree actually required to get into the data science domain and I hope I have the answer in front of me like you are not having a master’s degree but is still live I would like to hear from you whether it

Is required or not like you very rightly said and this is something uh which is one of the other myths that people have that for data science role specifically Masters degree is required so like you said the very big example in front of you is my itself I have not done any

Masters but still I managed to get this role at Microsoft so yeah Masters is not required but if you see the jde sometimes you see that the minimum years of experience required for these kinds of role if I say applied scientist is three to five years of experience

So in that case nowadays I’ve seen that companies are more aligned towards getting skills rather than exactly matching the year of experience so I the best the suggestion here would be focus on skill rather than focusing on that master degree is the most important thing and this much years of experience

Is the important thing if you have the relevant skills then you can get it if I got it you can surely get it yep yeah because you are having like somewhere around 1.5 years of experience and you crack the job role which requires three to five years of experience right so

This is definitely a realistic example in front of you so moving on to the next question like you were coming from a data engineering background but still cracked Microsoft in 1.5 years for the data and applied scientist role so how can someone coming from a different job

Profile can crack interviews of big Tech firms with less practical experience as well because right you are into the data engineering and data science have some like different kind of expertise and the requirements as well right and this is the point right where other folks are

Also trying to make that kind of switch like working let’s say as a Java developer software developer and trying to move into data science so how can they actually crack it and in this case do companies even shortlist their profile or not like just because of

Their past experience yeah so as per my experience uh I can I can say this thing like when I wanted to switch my role into data science so my preparation strategy was like I used to go and check for the job profiles and what are the requirements what are the skill sets

They want and how does that align with the skill set I have already okay so the skills I thought that they are not as such apt and I’m not very much familiar with so I started my preparation on that particular skill set which were different from the one that I already

Had so this is one thing and second thing like I already mentioned that if you have the required same set resume obviously plays a very important role in this that as we all know that if the first step resume selection doesn’t happen then surely you’ll not be able to

Chance to prove yourself in the interviews so do make sure that resume highlights all of your skills in a very right manner so that uh obviously your resume gets selected and then you have the chance to interview so my suggestion for the people who are coming from a

Different job profile and who want to make a career in data domain would be to firstly upskill yourself with the required skill set and try to match at least 60 percent to 70 of the required expectations and once you think you’re upskilled then only try to apply and there are various

People like Shashank itself who have already given a good roadmap of of the required important skill set you can definitely refer that and I think that would be enough for the people who want to switch I think this is the great strategy you guys can also follow it

Just look at the different companies job description and figure out what all things you know and what you are not knowing as of now and just work on that part and then you can mention it in your resume and definitely you’ll get the interview call you were actually

Preparing for your interviews related to data science or even let’s say trying for a switch so obviously you would have consumed some resources some probably blocks preferred YouTube channels or anything like that or even books so if you can suggest those resources to our audience as well that would be really really useful

So in my preparation Journey so I did refer a lot of sources so I I firstly pen down all of the important Tech stack that I wanted to learn and upskill myself into so starting with SQL I practice SQL on lead code okay so I referred to various record discussion

Section to come up where some few few folks have came up with very good solutions to questions apart from that I used to participate in the lead code contest also just to give get a fair idea like where do I stand because sequel was something I was not so good

At and understanding the importance of SQL in data domain I did use I referred to lately it Go and coming to python second thing python python for python we have multiple sources which we can refer to starting from freely available YouTube channels and even so I in case of python I was

Already very much familiar with but for the advanced different python I referred to a few sources like learned python.org and Geeks for geeks is like the Bible for all of us and same same I did for DSA also like I mentioned I did not uh did very high level DSA so basically

Medium level I refer to that and uh coming to spark so for spark I referred mainly to two of the websites one being the main website for spark that they have a very good documentation for the people who want to understand spark architecture same for Hadoop and other

Is sparkexamples.com where we get to know small small and Native and integrities of spark very well apart from that I have done a course from great learning also just to have a fair idea of what big data is what cloud is and my other suggestion would be there

Are various good courses for AI enable and python available in Coursera even I have undergone few of the courses so yeah these were overall um the resources I refer to in my preparation Journey really helpful shells are for the audience write whatever things you uh pointed out during your preparation Journeys another

Important question right because this is related to the monetary part the compensation and salary everybody want to knows about it okay now she is working at Microsoft how much money she would be making right so shelter I won’t be asking your exact salary for sure

Because that is not a tickle and that is not even allowed but at your experience level since you would have interviewed for other companies as well you would have negotiated as well with Microsoft and and any other company so what you have seen right at this moment at your

EXP experience what is the current salary band these companies are giving to the aspiring data scientists right I’m talking specifically about Microsoft Amazon and Uber and these kind of uh top-notch companies so that stat would be really really helpful for entry level data scientists and the range which I have seen

It but yeah surely these numbers are not the same for everyone and there are few factors that do affect these numbers so like you mentioned that I was interviewing with multiple companies so if you have a counter offer in your hand that surely adds up in negotiating a

Better number from these companies and like for my experience when you uh this this two to three years of experience rate you can expect between 30 to 40 also 30 to 14. wow 30 to 40 lakhs amazing and I think it will include the base part in stocks and uh yeah so when

I say 30 to 40 it’s including all of the components what is definitely a very very amazing start at least even your two years of experience right I I was not I was just getting 6.5 Flags per annum when I was having two years of experience this hurts but I’m really

Happy that companies are definitely giving these much amount to the freshest amazing amazing lots of folks are confused as well and for every job profile for everything there will be some myths around right so what are some common myths about the data scientist profile if you have uh like heard these

Kind of myths and you have a different opinion for that so can you just burst those myths yes sure decision and I think for data science roles there are more myths so the myths that I have seen and uh I wanted to highlight is first of

All and do not run for data science roles just because data science is the most hyped and it’s the kind of most demanding one so do not run for that that even if you don’t have interest even if you had don’t skill set you are running for data science jobs just

Because water science is one of the most hyped role in the market secondly on the compensation part also I have heard this myth among people that data scientists are paid much higher than any other thing so I think when we compare in the compensation part I don’t think now it’s

Actually very different uh if we compare it with the SDA rules also so data science is not as such hyped in terms of compensation also and the very last thing is People Are People expect the data scientist every time 100 it’s about only implementing ml algorithms and you

Are only doing machine learning and all of these things but that is not the case it’s kind of an 80 20 ratio only 20 of the time we are actually implementing these algorithms 80 of the time you are involved in cleaning the data transforming the data all of these

Essential things so it’s not only about ml algorithms so yeah these are the myths I’ve heard and they need to be versed out amazing amazing yes at least the last one I do agree on that part because people get interested for the data science profile oh my God will be

Doing mlai part and these this stuff but yes the reality is something different when you actually move into the companies so thank you so much shelja for just uh like bursting out these myths and I think this entire session would be really helpful for all the aspiring data scientists who are

Watching this podcast and I find it really informative the thing like you shared about your journey to Microsoft the resources the entire interview experience breakdown so thank you so much for that thank you so much from my side and from the entire audience side joining me on this podcast and sharing

This wonderful experience thank you so much it was very nice interacting with you awesome so that’s what I had for you guys in this podcast so I’m pretty sure you would have enjoyed this entire conversation with shelja and if you find it informative make sure to like this

Video again share it with your fellow friends and feel free to you put your thoughts opinions and queries in the comment section and also subscribe the channel for more such kind of amazing podcasts I will see you guys in the next weekend with another amazing podcast

Till then just stay safe stay home take care yourself and your family too