System Engineer to Data Scientist 🔥How?

Key Takeaways

[Music] in every job. It's the mainly thing is how you are [Music] the how you present yourself and how you are consistent and what is your main goal. Accidentally I joined in computer science that is very great. uh that's the god's grace I joined in computer science for a tier three or a tier 2 also it is very difficult to get campus placement my main project I also worked on some internal project I myself found out some internal project which is a machine learning project and that's how I keep on increasing my uh skill set on that and that's how I whatever problem statement uh in the data science field I'm able to solve yeah college studying students can they really become data scientist in 2025 if they're picking up people would be in mindset. She's from computer science background for her it is very easy and uh that's what every people mindset will be. That is a interest in me. Okay, I need to learn this genai. So because it is moving forward nowadays because sell every company if you see means they are selling their product using the gen. So five years non background is it possible for them to move into this space of data science and also the data analyst and data scientist role. There is some difference you should know the basics data science, machine learning, AI all the concepts this learning is it easy or difficult? So consistency is the key for everything. Data science with machine learning question every Saturday. for so how are you doing? Yeah, I'm doing great. How are you? I'm fantastic. So this is an unexpected call but thank you so much for accepting and trying to share values to our audience. Yeah it's my pleasure. Great. So I know your name is Sema. That's it. So can you tell or introduce about yourself just in 90 seconds about who is Sema? Where have you studied and what is something you're doing? Yeah as everybody know now my name is Sema and I'm basically from Karnataka. I studied in Dawang uh that to government engineering college and I studied uh in computer science stream and uh I passed out in the year 2019 from that my career journey started. So I uh till now I changed three companies. Mhm. So the first company that I worked with is uh uh Infosys uh based on the campus hirement and the second is the latent view analytics and now I'm working as a data scientist in city. Okay, that's great. So thank you so much. I have one question. So you are from computer science background, right? 2019 graduate. So how would I see you as an inspiration? because you are from a computer science background and uh there is a saying that if anybody is from computer science background it is very easy for them to get into IT and today the main challenge is many are getting graduated from computer science IT but still they are struggling that is the untold truth but I see you that you are from computer science background 2019 how did you uh is it easy for you to move into this IT space so can you share some tips about it yeah the the main thing in every stream it's not in every job it's the mainly thing is how you are according to me. So how you present yourself and how you are consistent and what is your main goal that is that is the main thing comes in everyone's life uh in the life as well as well as in the job also. So yeah, I accidentally joined in computer science. That is very great. Okay. Uh that's the God's grace I joined in computer science. But yeah, that's also one of the thing that helped me in uh joining the IT sector. But I uh since I uh was in Infosys, I also saw some of the people who also from mechanical background, civil background. It's not about the only the computer science the term. It's also about how we are consistent and how we are gaining the skills and how we are uh occupying it and then how we are implementing and showing it to the people that's also matters. Wonderful. So it is very clear that it is that no matter you are from whichever the background you are still it welcomes you if you are able to match their expectation. Yes. In 100% it's like 80%age your computer science background will help. Still for the other people who are from the mechanical civil background it's also about the skill how we are right in the IT the mainly they in the interview and all they'll ask you the question right they'll not assess you based on which background you are and all they will know are you do you know the topic and do you know how to implement do you know do you have the problem solving skills so those are the things which are mainly very important for people to switch to IT that's what my thought is great so it is very clear I can take mechanical and I can even try there but if I find my interest while studying my engineering degree to move into IT it is always welcoming me if I learn what exactly they need completely with respect to coding and if I'm able to solve that this uh Infosys and Vipros are able to give me a chance in their campus interview itself right all right so my next question to you is have you completed your graduation in tier 1 or tier two or tier three which category your college is coming under because you said you got campus placement right so can you talk about that. I studied in the government college which is tier three. I uh because uh my mindset was not to join to the engineering initially. My mindset was to join to some other BSE agriculture but uh accidentally I got into engineering college which is a government tier three college and tier three college as everybody know like we do not have that much uh teachers like who professionals who can teach us okay which is a who can show us the right path and who can guide us. So we ourself learned everything and in our college also there was there is no placement at all like the no no company is coming because only one company if I'm not wrong came so I got selected for that but uh that's also not of my interest it is based on the electronic things because I like coding and I wanted to do some analytic stuff so then I went to some other colleges and then I got placed in Infosys. Yeah. So tier three is very difficult because nowadays all the companies are focusing on IIT IM people. So for a tier three or a tier two also it is very difficult to get uh campus placement right which is very true that is what I'm just telling in my channels. So if you're studying in tier one colleges you need to pay a huge amount of money to them and the college itself will take care of you for the placement. It doesn't matter which company you're going but definitely people will be getting some placement within four to maximum 25 lps also freshers are able to get but here majority of the people are middle class so they not able to get money to study in that of that kind of colleges and even they are not having that kind of good IQ level to crack their interviews but they all dreaming to study but it is not practically possible for everybody to study in tier one colleges now majority of people are tier 2 tier three particularly tier three and you have made successful from tier three college which is really great because you have you you said that there is very less kind of companies are coming to colleges and you got to know there is an entry which is happening outside your college and you went there and you cleared it. I see that there is an internal push in yourself which pushed to you that you have to get placed before completing the college itself and can you talk about yourh friends who are studying with you were they also able to succeed like you or how many were able to get placed how many were not placed in the 2020 time so I'm just talking about that point of yeah you can speak now yeah so in out of 70 people in my class I think 20 30 got placed with different companies but other people still they were struggling and then the group discussion everything. Uh I also saw some of the people who after completing they joined some of the institute and then they learned and they upskilled themselves and via which they went to uh they joined to some of the companies. Great wonderful I got to know. So now it is the one takeaway point till here what I've got is if you're studying in college you will definitely get placed but for that you need to be very aware what companies are needed and you need to develop your coding skills your communication skills and your aptitude logical reasoning skills so that companies are going to come and you need to also keep an eye in the market which kind of companies are coming where in your particular city and you need to go and attend. So you know you're not going to trust only on your college for placements but outside also and tier three it's going to be very challenging for them to get placed. So that is what the takeaway till here. Now I'm moving into your second phase. You got a job now and you you're working in Infosys. So what is your position and what is your roles and responsibilities there? Yeah currently I'm working in city but I started my career in Infosys. So yeah in Infosys yeah uh as everyone know I guess like we'll get a 3 months training there. So it's a random training. uh before joining Infosys I also wanted to highlight one more thing I passed out in June 2019 my joining date is on December 2019 so I have a gap of 6 months so I utilize that gap to join some of the internships so that's in that internship I worked on data analytics stuff so that's how I got to know more about okay what is data okay there is a role called data scientist okay this is a very booming technology at that particular time So that's how I got interested and I joined Infosys. Infosys not directly I got a training on machine learning. They thought about like Python, machine uh sorry uh Java, Angular those stuff. So they put me in the Java related uh uh this projects but my interest is was on the machine learning thing. So what I along with my main project I also worked on some internal project. I myself found out some internal project which is a machine learning project and that's how I keep on increasing my uh skill set on that and that's how I moved my career from uh machine learning like normal system engineer to senior data analyst in the second company which is latent view there I completely worked on machine learning stuff okay and yeah now also I switched to city and financial service based company and here also I working as a data scientist Got it. One question. You said when you're working with uh before working with Infosys, you you were doing some internships, right? Yes. How did you get that internship? Was it paid or unpaid? How was the journey? How did you get it? Yeah. So, uh that internship mainly I was uh support it is it's I got it from the Googling itself like the LinkedIn and all. So I just called it's a startup and they were agree they took some interview and they understand okay she's able to do it if we able to teach her. So I got into it. It is a paid internship. Uh I got paid for 4 months uh that I worked on. Okay. Clear. So one tip before we move into your next from system engineer to your senior data analyst push into the next company. So here people who are studying in college the one question what they will be asking me is how do I get internships now that the paid internships and what kind of uh research that they have to do because you said you googled and you got to know the opportunities right how you did it can you share some tips for them so that they will also do that research and find some internships for themselves with paid yeah I have few tips uh the first one is like uh we have so many platforms right so we have uh LinkedIn which is a very great platform form for all the job seekers and also to to understand the upskills what is going on in the market nowadays and we also have a now indeed these are some of the platforms so I went to those platforms and I searched for the internship and then who whichever popped up which is matching your skills and applied for those and also one more point is I uh one more trick that I use is I always uh look into the company which is whichever uh I am interested on and then I uh send a request for the people who are like HR or who can director who or a manager and then if they accepted and then I will showcase my skills. These are my skills and I want I'm very interested and can you please uh get me the internship so that I can uh contribute to your company and so on. So I get a feedback also. those from few of them. Those are one of the trick that I used and also in now also if you search or sort it down for the internship you also you can get some of the things like in now the one thing is always daily you need to go and update yourself so that it shows that you are active every day then it will catch the eyes of the HRs or anyone's so these are some of the step okay great because in 2019 uh people who reached out through LinkedIn the volume is less Yeah. Right. But now uh the same strategy many are actually following and in today I'm getting at least 10 messages for internship opportunity from many people. All right. Even uh people who have 12 years of experience, 5 years of experience also want to do some internships here. But one thing which I'm seeing is as everybody are doing this in 2025. So in 2019 to 2025 there is a 6 years gap right. So now okay if I'm reaching out reaching out is fine that is good. But is there any new ways through which if they are reaching out they can get an impression of that particular person because of which they will want. Can is there any tip that you can share there? Yeah. Uh thinking about it uh there is a five years gap as you said from that time to this time and there is a many changes happened because the genai which is ruling now is come into picture. So now everything is available uh in the internet like in because charg you can search and everything will come right. So nowadays it's like more what you will give what you put into the table. So if you have some uh like experience or if you have some exposure or if you have worked on some of the projects already and if you showcase them okay see I did these projects I have these the these projects and I have a knowledge of market and I can work uh on these particular data set I have these data mining skills and uh modeling skills. Uh so nowadays my point now is like you have to implement and show that time you have to show your skills like yeah this these are my skills I'm I I can able to contribute now it's like show it to me that's the main difference I saw now so you have to work on some various projects and yet you can put it in the GitHub and as a public and then you can show it to them yeah these are the projects that I have worked on the influence is very good in terms of that got it. So result is more than words. Yes. Yes. Okay. So that's what you're suggesting. Great. So now I'm just moving from the faster job internship. You moved as a system engineer in Infosys. But by the time you got to know the power of data analytics, AI, machine learning and everything. So now you moved into Infosys. Uh but you're not working in this particular space. Uh but that's when you were upskilling yourself, right? While working in Infosys. So what is your upskilling phase? what are the things that you have studied before moving into Infosys as a senior data analyst? So upskilling in a sense. Yeah. The my theory was like okay if you can see so many videos uh in YouTube so I was uh continuously following some of the YouTube videos so on etc. All those things is one part. The second part is how you need to implement it your right you need to gets your hands dirty then only uh the the real skill will pop up and then only you'll get to know the doubts and then when you get to know the doubts and that's how you'll upskill yourself. So that's why I choose apart from the main project that's why I choose to work on some internal project which is not necessary for me but uh because of my interest I wanted to work on it I wanted to switch my career to the data science field because here system engineering is some other role I don't want to continue here so that's how I upskilled myself uh by doing the project and then uh doing it and learning it together and that's how I understand the entire machine learning concept how it is the I get into the deep of code how it works everything and then I'm able to crack the uh latent view and then they they understand okay she can able to do the machine learning stuff so that's how I get into senior analyst position there wonderful so basically you learned by yourself self learning yes and second thing which I'm able to understand is you were working on things which was not put on your head right yeah so the problem you said you were working in some internal projects right so the point is have you identified some problems within in infosys where AI or machine learning can solve you identified it and picked up that kind of problem statement and worked on with a and ML or how that particular process how have you worked yeah so in infosys like there is some portal like internal projects so we can go and search there there's already so many internal projects but there also you need to give an interview so they'll not directly just take you as okay come and work for the internal project I gave an interview there for the interim project itself and then I got selected for that project and then I started working. It's already existing uh problem statement. I did not introduce any uh new thing there. It's already existing problem statement. I worked along with them. Okay, great. So you had a team within you and because of which you had some spark and you have already a sol you leveled up and fastened your learning pace and you got upskilled and then you moved from Infosys to the other company latent a senior data analyst. So how was the shift? So because here from system engineer to senior data analyst it's completely not related. Yeah. So how did you handle the shift from uh IT one background to the data background? So how was the interview phase and what kind of questions you encountered with? Is it challenging or is it easy? Yeah, it's challenging only because uh yeah uh this is the internal project right because most of the my time was going in the main project and I only used to get uh 1 hour or 2 hour for the internal project but because of my spark I gave four hours for that project and that's how I I scope up with like okay this machine learning everything by the end of the I think I worked for 2 years in infos by end of the two years I understood the concept everything so then it made easy in interview process because I learned all those things right so whatever they were asking I'm able to understand and able to answer it nicely and yeah I'm pretty much confident at that time that uh whatever problem statement uh in the data science field I'm able to solve yeah wonderful so I feel that because you had a team you worked on it and you witnessed it you were super confident in the interview phase because of which you correct great so now you moved into infos as a senior data analyst right correct yeah infosys to latent as a senior sorry infosys to latent analytics as a senior data analyst so now here what kind of projects you are working I don't want to get into so much detail because yeah so you can just say a bit about what kind of whether it is a machine learning problem statement or deep learning uh how that experiences can you just share a bit idea so that people will know what how exactly industries are focusing on and accordingly they can also level up themselves yeah in laten it's completely different. Yeah, it's like uh machine learning plus the there is some one concept called econometric. So we have uh uh some library like uh there is a term called causal inference. So yeah, so it's a completely new terms for me because earlier I worked on data science stuff uh like logistic regression, random forest, XG boost see all these stuffs are the one thing but uh here along with that we also have one more thing called causal inference which is very new. So we used to uh there is basically I worked in a marketing analytics uh horizontal. So our thing is like we wanted to understand how the how to improve the campaigns all those things. So uh along with the machine learning again I got to learn some one more technology which is causal inference and there we developed one P and we successfully uh introduced it to the client and that got converted and then yeah wonderful I see as inspiration here now because you were ready to take up things which you have not learned before. Yeah. So that is one thing which I'm able to understand because here people would be in a mindset. She is from computer science background. For her it is very easy and that's what every people mindset will be. No matter they will never see others hard work or their mindset. But that's what because if a person is able to succeed from CS background that person is not an inspiration at all. That's what everybody but now I'm able when you're sharing more I'm able to understand that you are having that internal character within yourself that you're not ready to uh you know just stay there but you are ready to upskill what is the company's problem you are ready to level up and then grow and only those people can succeed and you are one of the witnesses among it so now my next question so you now working in late info analytics as a senior data analyst now you're making a shift to another company as a data scientist now yeah I worked in as a senior analyst I got promoted as assistant manager and now I'm And within late analytics as system manager. Great. So within how many months or uh weeks this happened? Yeah. Uh in late I worked for 2 years. Uh within one year I I got shipped. I got promoted because of the project that I that we did a P right. So that got successful. So that got converted. Maybe because of that I got promoted. Okay. Because of you companies started getting more businesses. Yeah. Hopefully I'm thinking that. Yeah. Got it. Fine. So now now you're an assistant manager. So what made you to quit latent analytics to move into the other company? Yeah. Because I see that you are they are promoting you right to a different level. That should be one factor because of which you had to quit this company and move here. So what is that factor? Yeah. So yeah I yeah as I said I got to know about the machine learning stuff. Yeah. Uh that is one thing and I got to know about the causal influence econometric stuff. That is one more thing. I wanted to upskill on the deep learning thing. So I am not able to get the projects which is uh uh related to the deep learning. So as I at that time uh there is a boom of jai right. So uh there is some there is a interest in me okay I need to learn this genai. So because it is moving forward nowadays because everyone is sell every company if you see means they are selling their product using the geni. geni is there means okay something is there then people are interested okay genai so that's how I got into okay geni I need to understand in order to understand the basic geni the basic is deep learning so I wanted to move work on the deep learning stuff but I'm not getting that there so that's how why I shifted to city and now yeah I'm working in in deep learning right now great so what is your company name city right so how many months you're working with city uh it's past 6 months. Okay, great. So I think your experience is great. So now are you working with JA related stuff? So deep learning kind of projects. So Jai related stuff it's coming up. It's not it but uh it will come in the few future but in the my previous company as a P as I I worked for some GIA stuff for 2 to 3 months. I know few bit of ja because I that also I learned by myself and I'm I'm always like a techie person like okay if something new come I'll try to understand that and I want I will try to implement it to see okay what it is because some so many people are talking so currently I'm working in deep learning LSTM decoder encoder model and transformers so on great so till now I got to understand about your professional journey from 2019 till 2025. So now I'll ask some general questions because today everybody wanted to move into this a space data scientist they also wanted to become people whoever is from 5 years nonat background they wanted to make a career transition. So I just I'll just ask some general questions to you. So whatever the opinion that you're having in your mind you can just give us an advice to them. So 5 years non background is it possible for them to move into this space of data science? If it is possible, how they need to take steps? If it is not possible, what is the reason? uh I think it is possible because I think everything is possible in this world because or it's only depend on you because all things are if you made a decision okay I want to do it and if you're consistent and if you get a right people help to get there if you know how to do it if you did some research on that then you can able to do it everything it's not only on the data science stuff or something in life also it is my theory Okay, that uh first you have to be correct and first everything should be inside you. Okay, I want to go there then every path will open up. So that is my strategy. So everything is possible. It's 5 years is nothing. If you are consistent uh if you get some if you join some institution where they are teaching you uh the data science stuff in two to three months and you can if you're consistent there you'll be able to grasp it or if you're able to understand it implement it and then if you show that skills to the in the interview and all if you are prove it then yeah of course they'll hire you why not okay great so in your company current and also the last companies so where there any people who made from nonIT to this IT background with 5 years or 8 years or 10 years of uh irrelevant experience to move into this particular space. Have you have you seen any kind of people making the shift in your company? uh particularly I do not recall but one example I can give is like people from whoever studied economics also like the arts economics in uh IIT they were able to make it to the data science as a data scientist role because economics is completely different right as compared to the data uh science role but they were able to make it so that's one example I have apart from that I don't uh I don't have any idea Okay fine. One question is freshest college studying students can they really become data scientist in 2025 if they are picking up? If yes what are the opportunities and what kind of companies they need to target? If no what is the other areas that they have to target? Yeah, if if they are interested means yeah they can definitely they can become a data scientist but my internal like uh example or my thing is like first you understand what it is then you can see for internship or you can do some internship because directly as a data scientist it is for me I don't know it's difficult because my journey is also started as a analyst data senior data analyst then assistant manager then data scientist so you uh data it's and also the data analyst and data scientist role there is some difference quite like quite a little bit of difference in the way they work also. So yeah freshers can make out but uh yeah uh the companies are like uh they can mainly focus on fractal latent ponetics like analytics company like some of tread ends genad uh and they can also yeah if they're very in they can also fang companies why not right so they can uh understand if there are any uh openings are there if for the freshers if they're asking or if they want for the fresher also So if they ask some of the like they they do they have some experience so then they can join some institute and then they can get the certificate okay they got in implemented something and then they can get into internship or the project okay clear now one more question we talking about the list of companies correct DSA data structures and algorithm so is there any interviews you have cleared almost two to three company latent analytics and now the other company Yeah. Have you encountered any interview rounds with DSA? Uh no uh not it because uh I think for a data science field DSA is not that much important because I never encountered any DSA question. There are a few but that is very basic question that anybody can answer that but uh I'm not sure but in order to get into the fang company also like maybe they can ask one round DSA but I also heard some of the people like I always connect with the LinkedIn people I al always touch okay what is going on and all I'll ask some of the people I I talked to some of the people in Google and all they were like okay the data structure is not required you should know the basics of uh data science. Statistics is very important because that is the basic of all data science. The statistics, the regression and uh the deep learning these are all very important in order to crack and yeah obviously mathematics because statistics means mathematics of course it will right because the main reason why I as this is the same claim which I'm doing it from since 2020. Okay. But still people are there. I have to study DSA to clear data science interviews. So if they wanted to study this DSA what kind of companies they can target and is it easy for them because their first goal is to get clear in Mong companies okay other companies are not good for them that's what they feel they are not even carrying how can I get placed in mon companies as a fresher that is what their question so if that's the case do you do you have any experience or have connections with people or can you share with your experience if they wanted to clear what should be their mindset and how they can just prepare what what should be their road map you can just share yeah there are two things as a fresher I can see in one angle and as experience I can see in another angle as a fresher I I think as a fresher you have to uh unlock what DSA is you have to study because uh as a fresher they don't know right they don't have any experience they what they'll ask so they'll ask data structure right because they will see how you'll unlock the code uh the bread first search all these things comes under uh the link list everything. So as a fresher definitely yes I think they will ask data structure uh in order to clear the M companies but uh as a experience they will ask because as a experience why they'll ask experienced people means they already have something in their table so they wanted to fill that particular person to uh satisfy those things right so they will only focus on those skills so as experience I feel they only ask uh about the uh data science stuff which is required. That's what my point is. Yes. Understand? So one straightforward question. Yeah. Is learning data sets difficult or easy? Data set. Data science. Data science, machine learning, AI, all these concepts. Is it learning uh this learning is it easy or difficult? The reason why I'm asking this question is people know linear algebra, integral calculations and all the things and they're getting afraid but they having a dream that they want to become a data scientist. Somehow they're ready to put the effort but they are seeing that data science is very difficult. I can't make it but I want to make it. So this is the kind of mindset that they have because of which they many are not taking action to learn. So this is my question to you again learning data science, machine learning, A, ML, deep learning is it difficult or easy? Uh it is not difficult. Uh it is the only thing is it will take time. The that is the main thing you need to give time for that and uh if you understand the things and if you unblock one by one because it will start from statistics. If you understand statistics then the linear regression will come. So which is indirect related to the statistics. Then you can go to the decision tree, random forest, XG boost, then the deep learning, then everything is interrelated. So my thing is start with the basic, understand clearly and then go step by step. So the only thing is it will take some time but it is not difficult. Understand? So two last questions. Yeah. Uh first one is now everybody got to know it is they can also make a career transition right you made a career transition from system engineer to data scientist now so there are lot of free resources available in YouTube many certification courses also free available so how should one choose between free certification courses or free learning from YouTube versus paid courses. So what is your is it good to opt for paid course if yes what are the criterias that they have to consider to go for this paid course or do we have all the materials available online if I'm studying in online what should be mindset if it is paid course what should be my mindset so it's also depend on the people to people right because uh it's based on what is their financial uh uh thing is so yeah uh in YouTube also you can get so many resources nowadays you can learn from YouTube also But uh the only advantage you get in the paid courses you'll you'll get a teacher or like you the one person who you can directly ask. So you have to for example you get into some question or while implementing you stuck. So whom you will ask. So but in the paid courses if someone is there to guide you so you can go and ask and you can clarify those doubts right away. Right? So that's the one of the thing and and somebody's there to back you and uh somebody's there to guide you and somebody is there to give you hope. Okay you can still make it. So if somebody is there it is very good uh for someone who is like okay afraid of okay can I make it or not? If they start in a YouTube and somebody will will they'll not uh complete the playlist itself right if in one playlist they'll see second playlist they will see and if they stuck somewhere they'll leave that entire thing they'll not able to understand right the other part so that will not happen the paid course that's what my thing is so whoever want to switch their career I think it's better to take a paid course and then uh it is good because you'll get one-on-one with the person and then Somebody's there to push you. Got it. So last question. What is your last advice to people who wanted to move into this a space as a data scientist or a engineer? Can you give one last advice which will make them motivated to step into this and learn and what would be the challenges that they need to encounter if they taking the decision to study this and get into this space. So the first thing is you itself because you have to decide where you want to go. First thing is like you have to be consistent. You should not change your decision. Okay. Now uh today you will uh you'll see okay I wanted to become a data science and you studied for some 2 to three days and then you get oh no no this is very tough I don't I want this then you go to some other then you can switch. So that switching that you should be consistent you should be focused. So it is you that everywhere that you you can shift you whatever you can do that's the my point from starting I'm telling that what it is you that you make a decision that is one of the thing and the challenges is like yeah motivation right so somebody will lose motivation after some months okay do I need to do it or not okay I'm not able to do it so consistency is the key for everything so that is the only advice I'll give consistency can change everything even though you're not your IQ level is not great right if you compare with the normal person and the person who is very very good IQ the person who is consistent is more uh successful than the person who is have a more IQ that's what my point is so consistency is the key for everything all right thank you so much wish you all the best yeah thank you yeah all Right. So the main reason the game is always about you versus you decision and if you are staying consistent definitely you are going to win. So he had a good team. So in case that has to be So child at the end of the day. So power you need to own. So in the particular series every week you can just give a like. reality. So, this is your AO John signing off. See you in the next episode. My