**Overcoming the Mindset Shift: From Non-Technical to Data Science Professional**

In an insightful conversation with our team, one of the students shared their personal experience of transitioning from a non-technical background to becoming a data science professional. The student had eight years of experience in roles such as marketing, sales, project management, and realized that they needed to make a significant mindset shift to pursue a career in data science. This transformation was not easy, but with the right guidance and support, it is entirely possible.

The student acknowledged that one of the biggest challenges they faced was adjusting to the level of quality expected in coding. As someone who had previously worked in roles where quality could be subjective, they found themselves struggling to meet the high standards required in data science. However, through their journey, they came to realize that the quality in coding cannot go up and down, and it is essential to maintain a high level of quality throughout.

The student praised our course for providing them with the necessary tools and guidance to make this transformation. They appreciated the in-depth content and the emphasis on practice, which helped them develop their programming skills in Python, SQL, and R. The student also stressed the importance of self-learning and taking ownership of one's own progress. They encouraged others to reflect on their strengths and weaknesses, create a personalized timetable, and practice consistently.

**Key Takeaways for Non-Technical Professionals**

For those looking to transition into data science from non-technical backgrounds, our conversation with the student offers valuable insights and advice. Here are some key takeaways:

* **Develop your programming skills**: Python, SQL, and R are essential tools in data science, and mastering them is crucial for success.

* **Practice consistently**: The student emphasized the importance of practicing regularly to solidify knowledge and develop problem-solving skills.

* **Create a personalized timetable**: Reflect on your strengths and weaknesses, and create a schedule that allows you to focus on areas where you need improvement.

* **Don't just complete assignments – understand them**: Make sure you grasp the concepts behind each assignment, rather than simply completing it for the sake of completion.

**Motivating Working Professionals**

Our conversation with the student also inspired us to highlight the potential of working professionals with five to ten years of experience. Many individuals in this stage of their careers may feel uncertain about pursuing a new field or may be hesitant to make significant changes. However, our course can provide the necessary guidance and support to help them overcome these challenges.

The student's journey demonstrates that it is never too late to pursue a new career path. With the right mindset and support, anyone can transition into data science and achieve success. Our alumni have already started hiring current students, creating a positive feedback loop that motivates others to take the leap. We encourage all working professionals with non-technical backgrounds to consider our course as an opportunity to transform their careers and unlock new possibilities.

**A Chain of Success**

Our conversation with the student has created a ripple effect of motivation and inspiration. Alumni who have completed our course are now hiring current students, demonstrating that our program is not just effective in transforming individuals but also creating a positive impact on the industry as a whole. We are grateful for this chain of success and look forward to continuing to support working professionals in their journey towards data science careers.

In conclusion, transitioning from a non-technical background to becoming a data science professional requires a significant mindset shift. However, with the right guidance, support, and practice, it is entirely possible. Our course offers a unique blend of theoretical knowledge, practical assignments, and self-learning opportunities that can help individuals develop the skills necessary for success in data science. We encourage all working professionals to consider our course as an opportunity to transform their careers and unlock new possibilities.

"WEBVTTKind: captionsLanguage: enhi friends uh today we have tarun locha one of our students here uh thoroughly first and foremost congratulations on making the successful transition to a data scientist role congrats from all of our team thank you sir i'll just give a brief introduction about your background so that students can connect better so tarun has about seven and a half to eight years of work experience at companies like tcs and edu sarti which is a interesting startup in education and he has worked at edusardi for about i think five six years as a business analyst and now we are moving to a data scientist role uh at a company called akakai technologies uh again again akakai technologies is a regular hiring partner of ours okay you'll also encounter some of your colleagues like sahana and sharma if you need any help they'll be there to help you they're all alumni of the applied ai course feel free to reach out to them right has been it's a very interesting startup that builds solutions in nlp computer vision etc and most importantly i should say you completed all the assignments successfully and you have very nice blogs that you put on your linkedin profile correct that is very good because that showcases the maturity of work your actual work because this is the work that you have done as part of our course so that's something that i would recommend all students to do to to showcase their work on their linkedin profiles and things i would actually get uh referrals timely referrals from like i would get uh referrals for placement this company in fact was it was referred by placement stream so i would like to thank uh the team for this and uh so so they would uh eventually give me interview calls and they would actually uh write it to me so this company wants collaborative project details so please write and send us send to us so they were very easy to approach and i would constantly get emails and calls and feedback from the placements team and and this was very helpful plus i i also eventually started applying to get parallely so i did not only like stick so okay there we always should also help students to work with our placement cell but also to leverage your existing network correctly or like that that's the best way right why limit ourselves to only a small narrow window we strongly encourage our students to try across a wide spectrum of opportunities in addition to what we offer so we have had students who got an offer through our placement cell but they got a better offer outside also we recommended them unbiasedly as if we would recommend a friend or a colleague saying boss this is a better one please go it's okay we will we will explain to the company that hired your placement cell because your career is more important at the end of the day correct correct similarly i i got one offer from my from my search i got from but this time your office was better than mine in fact the kind of role that that it was there so it was the perfect role to enter into thank you with that background let's get into the discussion the core of it i wanted to understand what your interview experiences have been especially because you're going you have like almost close to eight years experience in a business analyst role and you're going into a data scientist role okay uh uh to begin with actually i started my career with tcs and then uh i went into a project manager profile first before uh coming into the shoes of business analyst so uh i was completely doing something else and then this ai thing was a big challenge for me uh thankfully uh uh you know going through your course completely and watching videos like like this motivated me enough and i kept kept pushing me kept pushing myself uh and finally uh there was this company called akaike technologies i and thank you to you guys who referred it to me so uh there so they had given this uh assignment a big assignment to solve first it didn't it was so there was a problem statement for sql so i had we had to write certain sql queries that was a mandatory step a step that had to be completed and then there were two other parts uh one was for nlp and one was for computer vision so these two parts were optional so one was mandatory so i tried to complete all of them uh though i did not have uh in-depth knowledge about the computer vision stuff but i read about it because we were given about three days to complete it so this was something which i learned from applied ai because the assignment used to be so hard and eventually uh the thing that we learned from applied air was learn to learn and i was able to implement this thing there and eventually uh almost finished because few questions were open-ended they did not have a close ending so they wanted to see our approach and probably how much efforts we have put in from our end and uh so i was able to submit that just it's a very standard process that we see a lot of startups even large companies do which is like we had another company recently called jupiter.money which was trying to hire some machine learning folks and one of the things they did along with the screening test was give some anonymized data and ask students to build a first cut solution it's an actual problem that their team because their vp of data science is a good friend of mine they said this is a problem that we're solving we want to see how your students tackle it so it's a very standard approach that lot of companies take and i'm happy like the biggest takeaway for me is you're learning to learn because correct that's a skill that will live with you all your life correct great great i want to understand how challenging was this task compared to the typical assignments and self-case studies that you've done in the course um the questions were tricky i would not say that they were straightforward and if we compared to the applied ai assignments so uh these are few questions were almost at par and what few questions were below the uh below that uh challenging level so uh if you have solved all the assignments from applied ai you would be able to solve it but yeah few few things like like i said you have to learn to learn so they give you enough time to pick up few things yeah and and the same question can be solved through variety of technologies so they did not like constrain you to that you have to use this or that you can use anything that you want very good very good because this is this is how you would work in a real environment right you get a problem and you try to build a poc in a few days it's trying to simulate that very good so apart from this task what other levels of interviews did you have so once we cleared this uh so they so they combined three rounds so basically ujwal took my interview and he's also from applied ai and i was a little this was little converting me to me i thought i was speaking to one of my alumni and and it i was a little confident while talking to him honestly speaking uh but yes so he told me that he has clubbed three uh rounds together so this round went for about two and a half hours and and uh it so it consisted of coding plus mathematics like to begin with so there was a coding around and and including of some math probability questions on probability and statistics and then there were questions on about uh oh sorry first we started with assignment discussions itself so there was a discussion so first we had to discuss i have to tell them what step i took while solving the assignment and what went wrong what went right if you had more time how better uh could you have done it's a presentation of your work correct correct so so first step was presentation of my work and then there was coding plus mathematics round and then there was data science for the business round and uh so all together it took about so we started at seven and it ended at 9 30. uh so this was one shot everything is over yeah so i was at relief that if so it's done for one so how hard were the questions and how deep were they mathematically either the programming questions or the statistics questions or the core machine learning questions like uh uh so coding round was so it was like numerical python so if it like in the in initial part of our assignments we had to solve few mandatory python uh assignments so if you have solved that you can easily tackle this so i was able to solve questions and then uh follow-up questions were like can you reduce the time complexity what is the time complexity of this can you reduce it what are the other ways of doing this so questions related to this i did face few challenges regarding reduction of time complexity but then there were other questions okay you're getting stuck here so what next can you do so uh this this was about uh coding and then about statistics and and probability uh whatever your thoughts are whatever they're in the videos it was on the similar lines a few basic questions very very basic questions but when we moved deeper and deeper the question the depth of the questions kept over actually getting a little more challenging so can you give us an example so that uh the audience also can get a flavor of some of these like like one question that i recollect is that okay so there's a data set all right and you do not uh no so so there's some misclassified points in it so you have to figure out uh what are those misclassified points so there are some errors in the labels and and you have to figure out so there are 5000 points what technique would you apply why would you apply and how would you come up with the solution so if you so i was able to give a first cut solution in fact if it worked i was happy that i was able to solve that but yeah if you just know the uh models that's not enough so whatever you have taught like around the model the bounding cases the boundary cases the limitations of models all those things are very important so uh yeah so that was the uh question flow like what about the data science and machine learning part because that's that's the other piece of the treatment of our interview right right so uh the other part was like so they would give us a business problem uh so just like in the course there are amazon business problem one of the case studies and so similarly on similar lines there are business problems that they would give us and we had to draw the architecture how would you solve the uh image data how would you tackle the text data how would you tackle the numerical data and what sort of uh whole model that you would build like a big picture end-to-end system that correct correct so they wanted to see our approach how would we tackle this is it a linear problem is it a classification problem what sort of a problem is it how would you deal with it would you want to have probabilities in the end or not and and similarly so they just want to see your whole approach how you tackle the problem so it's it's more like a discussion rather than a rather than a question answer thing got it got it that's that's a nice form because these are the types of discussions that you would have with your colleagues on day-to-day basis right correct joining the company okay so with all this another thing that i wanted to understand is as a business analyst prior to this transition and prior to joining applied a course were you coding regularly no i was not coding at all in fact uh i had not seen i had not coded for five years or so before the only thing that i coded in pcs was i learned a bit of c sharp and sql during those days um and yeah that during the training period so five years you have not done much of coding so anything coding becoming good at it would have been no coding nothing uh it was uh in fact in the initial few months of the course was a nightmare for honestly speaking because it demanded so much for me but i can see uh the results now had i not put in those efforts had i had the course not asked us to put in those efforts i would not have cleared this round honestly that's true especially our students who do not actively code like for example you do not code for five years as a business analyst and as a program manager so it would be like a like a big big shift for you the initial part for non-programmers non software engineers is slightly hard but that struggle is important because if you don't predict your effort you will never be able to do your self-case studies on your own that struggle actually made me transition honestly perfect so one other very important question is somebody with eight years of experience you have work issue your work related pressures family issues i mean all of us go through that so what was your learning strategy and how did you balance your regular work with this whole learning journey especially given that you are not coming from a programming background yeah so first disclaimer that i want to put it here is that i took exactly one year to complete this course i could not do it before 10 months or eight months like i've heard people doing completing in within three months so uh so first of all do not compare yourself with others everyone has has different journeys and people who come from non-programming background you need to see this a little differently you need to put in a little more effort and uh it might be a little frustrating in the beginning but then a never give up attitude helps you a lot if so i would write emails whenever i get stuck i would write email i would get a feedback from your end i would get so they would never answer you with the solution because that's the part that you have to figure out but yes the support was excellent honestly speaking i thought after writing like emails for one question like 10 emails for one question they would just say why don't you do it why don't you do it on your yourself but i always got very good responses and eventually step by step by step so initial few assignments were i found it difficult but then i little gradually i got the hold of it and gradually i started taking little lesser time compared to uh previous so uh consistency is the key that's the that's the first uh takeover that i would give to students you have to be consistent i was not consistent in the initial days because pandemic had hit the business really bad and mentally i was not present so i was present physically but mentally i would think about business also the current job that i was into so it was little difficult situation and in fact in between the course i got uh suffered with the corona my family got served with corona so there was a lot of time uh which which got wasted there but yeah if you remove these two parts i try to be consistent as consistent as possible so uh for the day like my day would get up around uh get over at around 6 6 30 so in the nights i would study and weekends i tried to put in more number of hours so as long as like five to six shots on weekends each day and and that was my uh strategy so first i would uh so i made this mark so i divided 30 assignments into 10 10 10 chunks so first let's focus on 10 and and whatever videos i need to complete to finish these 10 assignments first i would do this then i would revise so my and then i would go for next 10 and then this would this was my strategy but yeah revision is the second most important thing so i would study and i would keep forgetting the earlier parts so that was one of the trickiest things i thought why not to opt for mock interviews and so that with the more you practice for tests the easier you will it will be for you to crack the interviews the real interviews so mock interviews were really helpful in fact one of the interviews i gave with you the final one yes and and uh it went average but the suggestion that you had given me like revised revised revised and that's what i did so eventually during the course you need to uh put in extra effort if you're non-programmer you have to go through it and number two you have to be consistent number three you need to make a proper timetable number four you need to revise so after every 15 days revise whatever you studied so once the course is over then i came up with a strategy like i would so in a week i would give two days for ml machine learning two days for deep learning one day for sql one day for python and one day for miscellaneous topics such as aws and and other flask techniques so that that strategy for interviews because you can get a call from anywhere because that's that was the thing so very nice strategy of revising by breaking the week this shows your program manager mindset like yeah you can see that in your planning so very nice i also remember our mock interview that i think i was one of the mock interviewers yeah it's a member of second case study because it is slightly different from the normal okay i remember that second mock interview because i think i answered a few questions related to that because very little data the challenges you can't apply like complex techniques you have to be very careful when you do it correct it was very short i guess i was the person who made the shortest data for the case study one uh so it was it had 62 points only yeah very little data now how do you build a model or how do you build something around it that's a non-trivial challenge without doubt and that's an exciting challenge everybody does big data because you can just throw it through the most complex model you know if you don't have enough data what do you do that's a good challenge cool now another thing that i have is as an experienced professional i'm sure this transition is not straightforward for you so what challenges you faced either in your learning or your interviewing process especially for somebody from non-programming background eight years of non-relevant experience a lot of it right of course some of your learnings as a program manager and business analyst will surely help you as to be a mature data scientist surely but still there is a lot of different stuff that you have done in your prior work right as an experienced professional what challenges did you face in your career transition um the challenges were huge like uh so basically basically there's a gap so when we apply for interviews so i would say i have seven to eight years of experience and and then people would want to hire probably say zero to three years of experience of data scientists or three to five years of data science experience how do i show that i do also have some knowledge related to your work and and how do they segregate all my years of experience and and take out the number of relevant experience so these sort of challenges were definitely there uh i for most of the things i would not uh get calls only because my profile was business analyst so i thought okay let me apply let me keep applying irrespective of of the role like ml engineer nlp engineer data scientist or business analyst or data analyst so i was open to all the roles because uh i wanted to first make transition and then eventually grow so uh first thing is like do not first of all undervalue yourself okay it's okay that you do not have a technical experience that's fine but you do have some knowledge related to business you do have some pro project management role or business analyst also you can at least find what business problems are and how to tackle so you do bring in something you do have no knowledge yeah we have dominant knowledge we do have communication knowledge we do bring out bring something to the table surely i like maturity for example right right like even in your preparation your revision strategy of spending one day on machine learning one day on this that shows the structural thought process correct correct so i remember the the fresher the roon i remember this that one and this thiru is is way better than the fresher term yes so exactly so or so never undervalue yourself but at the same time do not over expect also see we do have certain experience but we do not have data science experience so we have to be little flexible like flexible in terms of of packages also flexible in terms of roles also but thankfully like i am happy with the kind of role that i got with the kind of remuneration that i have and uh and with the kind of company that i had like today only we had a had a group discussion and it felt really good like in in the times of pandemic when people are struggling to get jobs i i do have a job in fact the kind of role that i wanted i wanted to be a part of deep learning technology and that's what i've gotten so do not over expect and do not undervalue yourself you have to maintain a bridge and be open be open do not uh shy away of telling of what your experiences were i completely told him like the ceo i do to have a hr wrong with ceo itself and he asked me like from tc still here what was my rules and what i went through my ups and downs in fact i did have one more one more offer from other companies so uh do not shy away from telling what you did and the challenges that you faced so this shows your mindset and and and once you are able to like tell you uh tell your experiences completely i'm sure the other person they also need people who can actually behave maturely uh tackle the problems completely they can make structure and eventually communicate with clients also the client communication is also important and it's part of data science so uh just just go full-heartedly do not think uh that that you won't be able to do it very nice very nice points don't undervalue as well as don't over value your prior experience right because we see a lot of non-technical folks who think i can never make it to data science which is not true you have a lot of domain expertise and lessons that you have learned in 5 10 even 15 years of experience that you can translate into data science careers correct if i can do it anyone can do it i'm telling you if they if they put in enough hard work and your course is the right course to change that mindset a lot of there are a lot of courses in the mind in the market but uh the way you teach us the way your methodology and and the assignments like i i remember one for one assignment i had submitted for six times and they would still not accept it if it's not at par yeah so far under quality like we're like we're not going to compromise it's okay to take time we will we will give you hints but you have to solve it correct correct correct so that shift there's a shifting mindset that happens throughout this course so all the non-technical people will be able to relate it so you there's a shift in mindset once you put yourself completely the shift in mindset because the quality in the bar which is expected uh because we come from marketing sales project management and other roles here the quality can go a little up and down but in the codes the quality cannot go up and down so uh so that's a perfect course for you very nice to hear that what suggestions would you give for somebody with like eight years experience especially given that most of your experience is non-programming non-software engineering what sort of suggestions will you give for people with your sort of experience on what should what should they focus on for career transition into data science uh see over the course that i've realized that you really really have to be good in programming skills so python sql r must so you need to be really really good at it uh go go in depth uh so so your your videos if you watch them completely so there so there's a there's enough content there and and the most important thing is to practice out and and so the assignments were there which which made us practice so apart from the assignments also if you get self learning times that's very important you know most of the people would do certain assignments quickly but few will not be able to do so basically what i'm trying to say is every topic is different for every person for some uh that topic can be very easy for you this topic can be difficult and vice versa so you need to actually sit down separately and and look at yourself look at your journey of every each month what have you learned and what have you not learned and you need to actually make your own uh performer basically your own timetable so for example like there were few techniques like uh so k nearest k nearest neighbor is one of the easiest topics right but i've found certain difficulties in lshs and and other cosign similarities and other topics so though it was easiest but i did uh take out the time to actually go in depth because i was not very confident about it perfect perfect so you need to actually take out time and for uh and figure out yourself what topics you're lacking at and and do not just let them go put them into practice and do it and uh same goes with the assignment do not do it for the sake of completing it do it understand it and and and just do it in a way that you want to put your theory into practice this is the best way to solidify your own knowledge so uh this this was the thing which which i applied and then nice talking to you taran thank you on behalf of our whole team and all the students just like the way you benefited and you motivate you got motivated from our previous interviews i'm sure a lot of working professionals with five to ten years experience will get motivated from yours and it's a very nice chain that some of our alumni are hiring our current students it's a very nice feeling to have so thank you very much thank you for taking thank you so much sir thank you so muchhi friends uh today we have tarun locha one of our students here uh thoroughly first and foremost congratulations on making the successful transition to a data scientist role congrats from all of our team thank you sir i'll just give a brief introduction about your background so that students can connect better so tarun has about seven and a half to eight years of work experience at companies like tcs and edu sarti which is a interesting startup in education and he has worked at edusardi for about i think five six years as a business analyst and now we are moving to a data scientist role uh at a company called akakai technologies uh again again akakai technologies is a regular hiring partner of ours okay you'll also encounter some of your colleagues like sahana and sharma if you need any help they'll be there to help you they're all alumni of the applied ai course feel free to reach out to them right has been it's a very interesting startup that builds solutions in nlp computer vision etc and most importantly i should say you completed all the assignments successfully and you have very nice blogs that you put on your linkedin profile correct that is very good because that showcases the maturity of work your actual work because this is the work that you have done as part of our course so that's something that i would recommend all students to do to to showcase their work on their linkedin profiles and things i would actually get uh referrals timely referrals from like i would get uh referrals for placement this company in fact was it was referred by placement stream so i would like to thank uh the team for this and uh so so they would uh eventually give me interview calls and they would actually uh write it to me so this company wants collaborative project details so please write and send us send to us so they were very easy to approach and i would constantly get emails and calls and feedback from the placements team and and this was very helpful plus i i also eventually started applying to get parallely so i did not only like stick so okay there we always should also help students to work with our placement cell but also to leverage your existing network correctly or like that that's the best way right why limit ourselves to only a small narrow window we strongly encourage our students to try across a wide spectrum of opportunities in addition to what we offer so we have had students who got an offer through our placement cell but they got a better offer outside also we recommended them unbiasedly as if we would recommend a friend or a colleague saying boss this is a better one please go it's okay we will we will explain to the company that hired your placement cell because your career is more important at the end of the day correct correct similarly i i got one offer from my from my search i got from but this time your office was better than mine in fact the kind of role that that it was there so it was the perfect role to enter into thank you with that background let's get into the discussion the core of it i wanted to understand what your interview experiences have been especially because you're going you have like almost close to eight years experience in a business analyst role and you're going into a data scientist role okay uh uh to begin with actually i started my career with tcs and then uh i went into a project manager profile first before uh coming into the shoes of business analyst so uh i was completely doing something else and then this ai thing was a big challenge for me uh thankfully uh uh you know going through your course completely and watching videos like like this motivated me enough and i kept kept pushing me kept pushing myself uh and finally uh there was this company called akaike technologies i and thank you to you guys who referred it to me so uh there so they had given this uh assignment a big assignment to solve first it didn't it was so there was a problem statement for sql so i had we had to write certain sql queries that was a mandatory step a step that had to be completed and then there were two other parts uh one was for nlp and one was for computer vision so these two parts were optional so one was mandatory so i tried to complete all of them uh though i did not have uh in-depth knowledge about the computer vision stuff but i read about it because we were given about three days to complete it so this was something which i learned from applied ai because the assignment used to be so hard and eventually uh the thing that we learned from applied air was learn to learn and i was able to implement this thing there and eventually uh almost finished because few questions were open-ended they did not have a close ending so they wanted to see our approach and probably how much efforts we have put in from our end and uh so i was able to submit that just it's a very standard process that we see a lot of startups even large companies do which is like we had another company recently called jupiter.money which was trying to hire some machine learning folks and one of the things they did along with the screening test was give some anonymized data and ask students to build a first cut solution it's an actual problem that their team because their vp of data science is a good friend of mine they said this is a problem that we're solving we want to see how your students tackle it so it's a very standard approach that lot of companies take and i'm happy like the biggest takeaway for me is you're learning to learn because correct that's a skill that will live with you all your life correct great great i want to understand how challenging was this task compared to the typical assignments and self-case studies that you've done in the course um the questions were tricky i would not say that they were straightforward and if we compared to the applied ai assignments so uh these are few questions were almost at par and what few questions were below the uh below that uh challenging level so uh if you have solved all the assignments from applied ai you would be able to solve it but yeah few few things like like i said you have to learn to learn so they give you enough time to pick up few things yeah and and the same question can be solved through variety of technologies so they did not like constrain you to that you have to use this or that you can use anything that you want very good very good because this is this is how you would work in a real environment right you get a problem and you try to build a poc in a few days it's trying to simulate that very good so apart from this task what other levels of interviews did you have so once we cleared this uh so they so they combined three rounds so basically ujwal took my interview and he's also from applied ai and i was a little this was little converting me to me i thought i was speaking to one of my alumni and and it i was a little confident while talking to him honestly speaking uh but yes so he told me that he has clubbed three uh rounds together so this round went for about two and a half hours and and uh it so it consisted of coding plus mathematics like to begin with so there was a coding around and and including of some math probability questions on probability and statistics and then there were questions on about uh oh sorry first we started with assignment discussions itself so there was a discussion so first we had to discuss i have to tell them what step i took while solving the assignment and what went wrong what went right if you had more time how better uh could you have done it's a presentation of your work correct correct so so first step was presentation of my work and then there was coding plus mathematics round and then there was data science for the business round and uh so all together it took about so we started at seven and it ended at 9 30. uh so this was one shot everything is over yeah so i was at relief that if so it's done for one so how hard were the questions and how deep were they mathematically either the programming questions or the statistics questions or the core machine learning questions like uh uh so coding round was so it was like numerical python so if it like in the in initial part of our assignments we had to solve few mandatory python uh assignments so if you have solved that you can easily tackle this so i was able to solve questions and then uh follow-up questions were like can you reduce the time complexity what is the time complexity of this can you reduce it what are the other ways of doing this so questions related to this i did face few challenges regarding reduction of time complexity but then there were other questions okay you're getting stuck here so what next can you do so uh this this was about uh coding and then about statistics and and probability uh whatever your thoughts are whatever they're in the videos it was on the similar lines a few basic questions very very basic questions but when we moved deeper and deeper the question the depth of the questions kept over actually getting a little more challenging so can you give us an example so that uh the audience also can get a flavor of some of these like like one question that i recollect is that okay so there's a data set all right and you do not uh no so so there's some misclassified points in it so you have to figure out uh what are those misclassified points so there are some errors in the labels and and you have to figure out so there are 5000 points what technique would you apply why would you apply and how would you come up with the solution so if you so i was able to give a first cut solution in fact if it worked i was happy that i was able to solve that but yeah if you just know the uh models that's not enough so whatever you have taught like around the model the bounding cases the boundary cases the limitations of models all those things are very important so uh yeah so that was the uh question flow like what about the data science and machine learning part because that's that's the other piece of the treatment of our interview right right so uh the other part was like so they would give us a business problem uh so just like in the course there are amazon business problem one of the case studies and so similarly on similar lines there are business problems that they would give us and we had to draw the architecture how would you solve the uh image data how would you tackle the text data how would you tackle the numerical data and what sort of uh whole model that you would build like a big picture end-to-end system that correct correct so they wanted to see our approach how would we tackle this is it a linear problem is it a classification problem what sort of a problem is it how would you deal with it would you want to have probabilities in the end or not and and similarly so they just want to see your whole approach how you tackle the problem so it's it's more like a discussion rather than a rather than a question answer thing got it got it that's that's a nice form because these are the types of discussions that you would have with your colleagues on day-to-day basis right correct joining the company okay so with all this another thing that i wanted to understand is as a business analyst prior to this transition and prior to joining applied a course were you coding regularly no i was not coding at all in fact uh i had not seen i had not coded for five years or so before the only thing that i coded in pcs was i learned a bit of c sharp and sql during those days um and yeah that during the training period so five years you have not done much of coding so anything coding becoming good at it would have been no coding nothing uh it was uh in fact in the initial few months of the course was a nightmare for honestly speaking because it demanded so much for me but i can see uh the results now had i not put in those efforts had i had the course not asked us to put in those efforts i would not have cleared this round honestly that's true especially our students who do not actively code like for example you do not code for five years as a business analyst and as a program manager so it would be like a like a big big shift for you the initial part for non-programmers non software engineers is slightly hard but that struggle is important because if you don't predict your effort you will never be able to do your self-case studies on your own that struggle actually made me transition honestly perfect so one other very important question is somebody with eight years of experience you have work issue your work related pressures family issues i mean all of us go through that so what was your learning strategy and how did you balance your regular work with this whole learning journey especially given that you are not coming from a programming background yeah so first disclaimer that i want to put it here is that i took exactly one year to complete this course i could not do it before 10 months or eight months like i've heard people doing completing in within three months so uh so first of all do not compare yourself with others everyone has has different journeys and people who come from non-programming background you need to see this a little differently you need to put in a little more effort and uh it might be a little frustrating in the beginning but then a never give up attitude helps you a lot if so i would write emails whenever i get stuck i would write email i would get a feedback from your end i would get so they would never answer you with the solution because that's the part that you have to figure out but yes the support was excellent honestly speaking i thought after writing like emails for one question like 10 emails for one question they would just say why don't you do it why don't you do it on your yourself but i always got very good responses and eventually step by step by step so initial few assignments were i found it difficult but then i little gradually i got the hold of it and gradually i started taking little lesser time compared to uh previous so uh consistency is the key that's the that's the first uh takeover that i would give to students you have to be consistent i was not consistent in the initial days because pandemic had hit the business really bad and mentally i was not present so i was present physically but mentally i would think about business also the current job that i was into so it was little difficult situation and in fact in between the course i got uh suffered with the corona my family got served with corona so there was a lot of time uh which which got wasted there but yeah if you remove these two parts i try to be consistent as consistent as possible so uh for the day like my day would get up around uh get over at around 6 6 30 so in the nights i would study and weekends i tried to put in more number of hours so as long as like five to six shots on weekends each day and and that was my uh strategy so first i would uh so i made this mark so i divided 30 assignments into 10 10 10 chunks so first let's focus on 10 and and whatever videos i need to complete to finish these 10 assignments first i would do this then i would revise so my and then i would go for next 10 and then this would this was my strategy but yeah revision is the second most important thing so i would study and i would keep forgetting the earlier parts so that was one of the trickiest things i thought why not to opt for mock interviews and so that with the more you practice for tests the easier you will it will be for you to crack the interviews the real interviews so mock interviews were really helpful in fact one of the interviews i gave with you the final one yes and and uh it went average but the suggestion that you had given me like revised revised revised and that's what i did so eventually during the course you need to uh put in extra effort if you're non-programmer you have to go through it and number two you have to be consistent number three you need to make a proper timetable number four you need to revise so after every 15 days revise whatever you studied so once the course is over then i came up with a strategy like i would so in a week i would give two days for ml machine learning two days for deep learning one day for sql one day for python and one day for miscellaneous topics such as aws and and other flask techniques so that that strategy for interviews because you can get a call from anywhere because that's that was the thing so very nice strategy of revising by breaking the week this shows your program manager mindset like yeah you can see that in your planning so very nice i also remember our mock interview that i think i was one of the mock interviewers yeah it's a member of second case study because it is slightly different from the normal okay i remember that second mock interview because i think i answered a few questions related to that because very little data the challenges you can't apply like complex techniques you have to be very careful when you do it correct it was very short i guess i was the person who made the shortest data for the case study one uh so it was it had 62 points only yeah very little data now how do you build a model or how do you build something around it that's a non-trivial challenge without doubt and that's an exciting challenge everybody does big data because you can just throw it through the most complex model you know if you don't have enough data what do you do that's a good challenge cool now another thing that i have is as an experienced professional i'm sure this transition is not straightforward for you so what challenges you faced either in your learning or your interviewing process especially for somebody from non-programming background eight years of non-relevant experience a lot of it right of course some of your learnings as a program manager and business analyst will surely help you as to be a mature data scientist surely but still there is a lot of different stuff that you have done in your prior work right as an experienced professional what challenges did you face in your career transition um the challenges were huge like uh so basically basically there's a gap so when we apply for interviews so i would say i have seven to eight years of experience and and then people would want to hire probably say zero to three years of experience of data scientists or three to five years of data science experience how do i show that i do also have some knowledge related to your work and and how do they segregate all my years of experience and and take out the number of relevant experience so these sort of challenges were definitely there uh i for most of the things i would not uh get calls only because my profile was business analyst so i thought okay let me apply let me keep applying irrespective of of the role like ml engineer nlp engineer data scientist or business analyst or data analyst so i was open to all the roles because uh i wanted to first make transition and then eventually grow so uh first thing is like do not first of all undervalue yourself okay it's okay that you do not have a technical experience that's fine but you do have some knowledge related to business you do have some pro project management role or business analyst also you can at least find what business problems are and how to tackle so you do bring in something you do have no knowledge yeah we have dominant knowledge we do have communication knowledge we do bring out bring something to the table surely i like maturity for example right right like even in your preparation your revision strategy of spending one day on machine learning one day on this that shows the structural thought process correct correct so i remember the the fresher the roon i remember this that one and this thiru is is way better than the fresher term yes so exactly so or so never undervalue yourself but at the same time do not over expect also see we do have certain experience but we do not have data science experience so we have to be little flexible like flexible in terms of of packages also flexible in terms of roles also but thankfully like i am happy with the kind of role that i got with the kind of remuneration that i have and uh and with the kind of company that i had like today only we had a had a group discussion and it felt really good like in in the times of pandemic when people are struggling to get jobs i i do have a job in fact the kind of role that i wanted i wanted to be a part of deep learning technology and that's what i've gotten so do not over expect and do not undervalue yourself you have to maintain a bridge and be open be open do not uh shy away of telling of what your experiences were i completely told him like the ceo i do to have a hr wrong with ceo itself and he asked me like from tc still here what was my rules and what i went through my ups and downs in fact i did have one more one more offer from other companies so uh do not shy away from telling what you did and the challenges that you faced so this shows your mindset and and and once you are able to like tell you uh tell your experiences completely i'm sure the other person they also need people who can actually behave maturely uh tackle the problems completely they can make structure and eventually communicate with clients also the client communication is also important and it's part of data science so uh just just go full-heartedly do not think uh that that you won't be able to do it very nice very nice points don't undervalue as well as don't over value your prior experience right because we see a lot of non-technical folks who think i can never make it to data science which is not true you have a lot of domain expertise and lessons that you have learned in 5 10 even 15 years of experience that you can translate into data science careers correct if i can do it anyone can do it i'm telling you if they if they put in enough hard work and your course is the right course to change that mindset a lot of there are a lot of courses in the mind in the market but uh the way you teach us the way your methodology and and the assignments like i i remember one for one assignment i had submitted for six times and they would still not accept it if it's not at par yeah so far under quality like we're like we're not going to compromise it's okay to take time we will we will give you hints but you have to solve it correct correct correct so that shift there's a shifting mindset that happens throughout this course so all the non-technical people will be able to relate it so you there's a shift in mindset once you put yourself completely the shift in mindset because the quality in the bar which is expected uh because we come from marketing sales project management and other roles here the quality can go a little up and down but in the codes the quality cannot go up and down so uh so that's a perfect course for you very nice to hear that what suggestions would you give for somebody with like eight years experience especially given that most of your experience is non-programming non-software engineering what sort of suggestions will you give for people with your sort of experience on what should what should they focus on for career transition into data science uh see over the course that i've realized that you really really have to be good in programming skills so python sql r must so you need to be really really good at it uh go go in depth uh so so your your videos if you watch them completely so there so there's a there's enough content there and and the most important thing is to practice out and and so the assignments were there which which made us practice so apart from the assignments also if you get self learning times that's very important you know most of the people would do certain assignments quickly but few will not be able to do so basically what i'm trying to say is every topic is different for every person for some uh that topic can be very easy for you this topic can be difficult and vice versa so you need to actually sit down separately and and look at yourself look at your journey of every each month what have you learned and what have you not learned and you need to actually make your own uh performer basically your own timetable so for example like there were few techniques like uh so k nearest k nearest neighbor is one of the easiest topics right but i've found certain difficulties in lshs and and other cosign similarities and other topics so though it was easiest but i did uh take out the time to actually go in depth because i was not very confident about it perfect perfect so you need to actually take out time and for uh and figure out yourself what topics you're lacking at and and do not just let them go put them into practice and do it and uh same goes with the assignment do not do it for the sake of completing it do it understand it and and and just do it in a way that you want to put your theory into practice this is the best way to solidify your own knowledge so uh this this was the thing which which i applied and then nice talking to you taran thank you on behalf of our whole team and all the students just like the way you benefited and you motivate you got motivated from our previous interviews i'm sure a lot of working professionals with five to ten years experience will get motivated from yours and it's a very nice chain that some of our alumni are hiring our current students it's a very nice feeling to have so thank you very much thank you for taking thank you so much sir thank you so much\n"