Ask Me Anything AI ML Executive Program by IIIT Hyderabad and TalentSprint
Hi, everyone. Good evening. Thank you so much for joining us today. We are late by three minutes. I apologize for that just trying to get the system set up over here.
We are good to go. I
think we have almost 50 odd people who have joined us today. Thank you for joining in. I hope all of you are safe. And I have children with me, we are going to try and take you through I have some very, you know, small slides, primarily talking about opportunities, because I guess that's what you know, we are all here for to try and understand what are the opportunities that are there. And then we are going to go directly into you know your question and answers. And we're gonna try and answer as many questions as possible. So my name is Ernesto Bhattacharya. I'm the Senior Director here at talentsprint. I had admissions for all executive programs here. And I had a career of more than a decade primarily around helping professionals make better career choices and better decisions. Before this, I used to head the admissions for the Indian School of Business iasb, their main MBA program, as well as I worked for a company called Pearson, which is the largest education company in the world. And there I used to, I have had the pleasure of working with almost 3035 odd fortune 500 companies primarily in areas of designing competency management systems, leadership identification, high potential identification, etc. So that's been my passion all my life. So I'm going to talk to you from that perspective. I am honored to be joined by cherington. Today, he is an alumni of our AML program, children. And if you can hear me, can you help us with a small introduction? about yourself?
Yeah, sure. Hi. So Good evening, everyone. And this is Jonathan here. And I pretty much have been in this industry for roughly about 25 years. And it has been, you know, pretty interesting, long journey. So I'll talk about more when we catch up. But in brief, we are in the last 25 years, I've worked across technologies, heading verticals, heading horizontal practices, which is in terms of technology. And, you know, both in startups as well as in larger, you know, the Big Four, as well as the large consulting companies. So pretty much had the, you know, variety of working in all kinds of setup. So I think, last year, at the same time, I was also working your shoes in terms of evaluating, because every five years, I tend to upgrade myself in new areas, because that's how this industry is, if I don't upgrade every five years become obsolete, right. So, I mean, I tend to do that. And I was in the US when I was just coming back. So I was planning to you know, pick up something which is interesting. So one of them was blockchain, the other was AML. And looking at the, you know, immediate opportunities, and the various pros and cons that are evaluated at that point of time, I zero down on the triple IDs program, primarily because of several reasons. You know, I'll get into it when we have the next set of q&a. So that's in brief. Yeah, so I think very excited to, you know, kind of interact with all of you. And, you know, feel free to ask all your questions, doubts you have, because I was in the similar shoes a year back. So I'll be happy to share my analysis. And, you know, on what basis I picked up and also post the journey, how it was so far. Alright.
Thanks, Sharon. Thank you so much pleasure having you here today. So we have a, I have a very small presentation. It's primarily a talking little bit about talentsprint. The genesis of the program and some experience, you know, regarding what we have been able to do with this program this in case you don't know, this is one of the older programs in the country when it comes to AML programs. We are currently recruiting for the 16th cohort, almost three plus years old is how old this program is. And we started off when AI ml was just starting to be a big buzzword in the country, obviously nowhere near you know how prevalent it has become right now. So a little bit about talentsprint. And that's, you know, that's something that's changed in the last couple of weeks is we are now majority owned by the National Stock Exchange, one of their subsidiary companies called NSE Academy. In case you don't know, you know, we are a 10 year old company, we we've had this journey of around 10 years now. And we started executive education about three, three and a half years back with the IML program incidentally. And as of last, last week or last Tuesday to be precise, we have been majority bought out by National Stock exchange through subsidiary company National Stock NSE Academy, whereby NSC is in I'm sure most of you know is the largest stock exchange in India. It's, it's a, it's a pioneer in so many ways, it's one of the largest stock exchanges in the world. And for them to own us majority owners, and in the next couple of years, we're going to become a completely part of NSC, where they'll have 100% ownership down the line speaks volumes about, you know, talentsprint adds a lot of credibility to the kind of programs that we run. And that's something that's, that's happening as we speak, we are, you know, planning to do much bigger things in the future. And this will open up a lot of opportunities for many of our learners as well. But if you really look at what we have been doing, fundamentally, we are going to remain the same, we are going to help, you know, the NSA Academy to scale up and and reach more professionals in various stages of their learning journey. What we have been doing for professionals right now is that we have been working with top institutions to provide executive programs, thanks to the fact that the Government of India has mandated all these top institutions to start looking at executive programs to deliver these programs, to you know, a larger audience and not limited to the very limited number of students who could participate in and to generate their own resources, you have all these top institutions, which you could otherwise not get into, you know, you would have to do most of you would have, you know, crossed the age where you would be getting into them, you have these executive programs, which you could do so we work with nine and we are in a niche, so to speak, in the sense that we offer market creating products. So like a IML, for example, we started with three years back, we've just started a program on, you know, ai in marketing, so to speak, that's what I am Calcutta, again, a program like that does not exist in India before, we have a first program in digital health and imaging, which is kind of usage of AI ml. And, and you know, deep learning in the healthcare space, again, first of its kind program in India, which we run with IFC. So similarly, along those lines, so we offer these programs for our learners so that you know, you could get the benefit of an association or, or top tier certificate from a top tier institution, and, and you get to progress in your career, etc, etc, and have the skills we also worked with a lot of blue chip companies like say Google pega, systems automation anywhere blue prism in the space of robotic process automation. These are primarily for the graduate students, not for executives. But so we work across both functions over there. So over the last couple of what the last three years, we would have impacted the lives of about four or 5000, you know, working professionals, and in terms of graduate students, it will be somewhere in the range of three, three and a half lakh undergraduate students. So just coming down to the end here I'm gonna talk about is just some opportunities. I'm sure most of you would know that, but I thought I would quantify some of the things so that people have, you know, some idea of the scale that things are working at, if you really look at it, investment in AI has been growing. This was an IDC research study, which says that, you know, it's been growing at a CAGR of 40 to 45%. The pandemic has probably, you know, accelerated this. This was a study, I think it was late last year study IDC, a PwC, incidentally, has come out with one of their reports, which says that by 2030, it's expecting AI to contribute around $15.7 trillion to the global economy. And that's, that's a large amount, if you really look at that, which also talks about a lot of opportunities that are there. Large scale adoption, that's something that's happening, and I have two slides on that. And then we'll be done. If you really look at that. Just taking that from the PwC study. This is a direct, you know, this is one of the benefits that they looked at. If you really look at it, a lot of the growth is going to happen in China, etc. But
developed Asia, that's where India is their 1.9 trillion, so almost $900 billion is what the growth or the contribution of AI in the economy is projected by the year 2030. And that's that's a that's a huge amount of money. You know, India by itself. For 2.8, or trillion dollar economy, so if you really look at that point 9 trillion to come in the next 10 years by when I mean, you know, the current government says that they're trying to go to a $5 trillion economy by 2024 2025. I don't know if that will happen or not. But that's still going to be a significant contribution, if you really look at that. So from a working professional perspective, or somebody who's just coming into the job, role, many of you will be coming in as well many of you would have would have would have want to maybe transition. This is still a very lucrative space and a space with a lot of opportunities that you should be a part of, if you're really looking at that. And if you look at these slides, and these are just wonderful, I have eight sectors, which I'm covering, if you really look at it from an IP perspective, a majority of the it clients will be a part of these eight odd sectors that are their financial services, a majority of the Indian it workforce, which according to NASSCOM is around for 4.5 million professionals, which is around 4554 4045 lakhs professionals, majority of them work actually in financial services. Now, the PwC study here, what they are talking about is I could not reproduce the entire study in the in the slide, but it was talking about the impact of artificial intelligence in specific sectors in the broad sector, and then the sub sectors are given there on the screen for you. And these were ranked in terms of the impact that they will have on rating of really scale of one to five, if you look at that these three broad sectors, in, you know, in financial services, which forms the lion's share of, you know, Indian it, companies, clients that they have the time around 3.8 out of five impact in AI and this, so, you know, professionals who are there currently in it in financial services, if you want to pick up a skill in AI, you would probably be in a great position to leverage that skill, because that sector is going to be, you know, bfsi, in this example, is going to have a huge impact. It's, you know, 3.8 out of five, if you really look at that. So that's, that's the kind of the the average across all of these sectors and retail technology, communications, etc, was in the range of 3.1, out of five. So, huge amount of opportunities are there. Now, that's a great thing that we all know this, but then there is on the flip side, there's a huge skill gap as well. If you really look at that. This was another study. I missed who the study was, I think it was part NASSCOM or somebody else.
Only. Just want to make sure that everybody's over here, because I see a couple of people saying unable to hear that will remain able to hear somebody say screen not visible. Are you able to follow? Can you just confirm whether you're able to hear? Can you see the screen? Here? Yes. Okay, so I think if somebody's still facing an issue, it could be a local zoom setting, just like that.
Alright, go ahead. Thanks. Thanks. Thank you. So yeah, we still have a huge skill gap when it comes to you know, the DD the total the actual skills, the core skills that you need to work in, in the sector. So which means that there's still a huge amount of opportunity that's there for you to, you know, exploit, so to speak and make a career for yourself. This was a NASSCOM study done early this year, this was right before the pandemic aid, if you really look at that we are looking at a shortfall of one lakh 40,000 work for working professionals in AI. So companies need people, they don't find people. And when we say they don't find people, we are talking about people who can actually go ahead and execute projects, who can conceptualize at various stages. It's not that you know, they are only looking at, you know, very senior AI professionals, but it's across the skill gap across seniority in the industry. So and then globally, you know, Gartner is predicting this is again, you know, the numbers keep changing, but just I'm just trying to talk about the the opportunities that are there. We have had almost 2400 professionals, you know, graduate and I'm going to bring in Toronto over here for, you know, this part of the presentation. That's that. So if you look at this, we have done this study across a majority of our professionals of the learners that who pass out of our program, what we did is that we asked them, What is it that they are looking for in a program. So, one is that the first topic that came up was that the program should be for professionals needs, you have many other programs, which say that, you know, coding experience is not required, etc, etc. We are not that, you know, we we, we want you to come in because as a working professional, we will not be able to teach you coding this probably, if you want to learn coding. And if you want us to teach you coding and then do that, it's, it's a long program, it, it becomes a very long program is a six month long program. So we expect you to have those basics and you know, if you have that basic, you will come in over there, they also meet a lot of focus on hands on and group work, that's, that's also something that's very important. And lab exercises, etc, they need to be very practical oriented things, which is something that is the third thing that came out. And finally, pure learning is something that's very important. And, you know, all of you, I'm assuming who are here with work experience, you would understand that, if you are learning as a part of a group where you know, people come from various sectors, where people come with various years of experience, doing various functions, etc. You can all learn from each other's experiences. So that's something that's very important. And one thing where the AI ml program of AAA Team talentsprint really exists, you know, really does very well is the fact that the average years of experience for our program is in the range of 7.5 plus years of experience. So a lot of very senior, serious professionals, they tend to choose our program. And that also speaks a little bit about the DNA of the program. It's it's for it's a serious program, if you really want to pick up skills, and and you know, you you value, a peer group, you value serious learning and you are not swayed by, you know, swanky swanky ads, etc. They tend to choose our program. So that's that's the typical audience that's been I'm just the numbers are there on the screen for you to understand profile experience. Also, if you really look at that a majority of them are above, above 10 years of experience. So children can, you know, I'd like to bring you in over here and talk a little bit about, you know, your journey in terms of you know, you You said that in your introduction, you have been in the sector for almost 2025 years now. And why did you decide to get into, you know, what was a little bit about your journey so that we can set the context up? And and why did you decide to get into AI ml and blockchain? And, yeah, so what led to that? Sure, sure.
Yeah. So I think I kind of was heading the bfsi space, and from which then after that, I moved to HLS, which is healthcare and Life Sciences. And, you know, the previous two years I was in the US and I mostly at that point, you know, I've been in leadership roles at that point. So I was also handling the client partner role, you know, working with various CIOs, what was happening is, you know, many of the interactions, you know, though we do the straightforward, which is the BU business, right with clients, the CIOs, the cxos, they also had has the budget, right, essentially the budget for innovation to driving innovation, every CIO has that. So you talk about we talking of, you know, Citibank, we're talking of JP Morgan Chase, benaki, of AIG, MetLife, you know, some of these in the space as well as if you look at Philips healthcare, Thomson scientific and copy of some of the ones that used to work very closely. And every CIO has a mandate to kind of show innovation to the board of directors that they report to. Now, everybody would speak about, you know, hey, I want to do something on artificial intelligence, Ai, ml, you know, do some deep learning and so on. Right? So what was happening is, you know, they said that, why don't you and there is no question of any RFP here, because unlike a traditional bidding process, if you really have a very focused solution, that you can show them, that with the data that they already have, in their current structure, and show some results, I mean, they're really ready to give you kind of a project to start off small, and then you can expand on it. So in order to have those meaningful conversations with clients, what I realized that, you know, it was like, you know, without knowing the subject, you know, we would kind of you know, always depend on the practices or technology folks who are in ML, you know, get to the practice, you know, technical folks who are already kind of educated in that space within the company, and then come back. So it's always like your online offline discussion. And when you you know, having a lunch or a, you know, dinner with the CIO at point of time, you're not able to immediately respond because of the lack of knowledge. So that basically creates a showstopper to you know, give a proposal right at the point when they are hot right? I mean when they're asking for it, so So that became like a handicap and I realized that was becoming a handicap. And the good thing about for example I was evaluating the other side for BFS is blockchain whereas blockchain was more focused on the other side. But AML is like a big horizontal, where you can apply in every industry, when went after it, whether it's automobile, whether it's healthcare, Life Sciences, you know, banking, insurance, anything, right? So it's a white, it's like a broad spectrum antibiotic. Absolutely. That was one reason when I zero down, I thought, let me first focus on the ML piece, because that's more relevant for me. And also, without having the knowledge you're like, you know, having you have an elephant in front of you, and you're blind. And you're just figuring out, you know, you know, what is it and you don't even know how to attack the animal in front of you. So I think that was a primary reason, because I was anyway planning, you know, with my family to move back. And then I started to be honest, and I compared all the competitors of triple it has robot talent spraying. So I did the frozen canvases, when since I was in the US, I picked up the programs that was done by Stanford by Colonel by MIT, I mean, damn expensive, you know, though, like, I didn't want to waste that much of money to be very honest, number one, number two, I didn't look for a full time program because I was working number three within India, I've evaluated you know, the triple it banglore program, we have a two year long program charging some five lakhs, they give some diploma and a degree from you know, one of the UK universities and I was not very kind of, you know, inclined. So those and plus that was great, I think greatly sent us a bunch of Coursera, Andrew enji, all of that, you know, which I kind of you know, evaluated. But finally, when I also realized and that one was AAA at Hyderabad and I was coming, I was basically in Hyderabad going to be based on Hyderabad. So that definitely an inclination. And the second thing is, I think, definitely looking at the Gartner and Forrester reports, which I kind of very closely look at, on an ongoing basis, it was very clear that this market is going to keep expanding over the next few years, right, that is next 510 years, you pick up this particular skill, you will make reasonable amount of money. So that basically was the driver for pushing, you know, and getting into it. I mean, it really needed a lot of hard work. Because, you know, for unlike for young kids, you know, with less than two years or five years experience is easy. But for us, we do push a lot more harder, right. But I think it was worth the time spent, you know, and meeting the brilliant professors of triple ID Professor meeting, Professor ashokan, of talent spraying, you know, being the chief learning officer and the flexibility. And the fantastic The good thing about the learning was, I think all the guides, who were doing their ms are the PhD at triple it, and they're like completely hands on. And you know, the kind of way they support during the labs. I mean, that was fantastic. And each and every particular lab that I can remember and recollect, I mean, each of them was a very practical situation that you would encounter if you're working even in a company like Google or Facebook for that matter, right. So I think it gets you to that level of capability where you can confidently talk to anybody in the industry, as a client. And for example, if you know, the people looking for switching jobs, I mean, it gives you have complete confidence to talk at a particular level, where you can actually clear potentially an interview, of course, after you get in, you have to be able to work as well. So for that you would need to get some experience. So I think these were the primary reasons
I would say, absolutely. Thank you so much. to everybody there on the call as well, you can send in your questions through chat, if you raise your hands as well, we can Yeah,
I think there are some questions already read through, we can go one by one. And if anybody wants to speak, because sometimes, you know, the intent is not clearly understood when you type few words. So you can unmute and ask your question. That will I'm sure one question, you know, I think it will address a good number of people's, you know, kind of thought process as well when we respond. So I see one anonymous attendee is the question is, will, what was that will data science be included in this course, along with the animal? So I think, I don't know how you define data science, because the moment you're working with any type of data, right, essentially, whether you're capturing data, whether you're handling data, where you whether you're programmatically, you know, kind of refining the data, all of that, if you look at you're basically involving, in processing that data, so there is a complete data science, in machine learning is essentially advanced way of handling the data where the machines are learning from the input and output. If you look at all of your software, people, I'm assuming, right, so in software, if you see the typical good old way of handling software, you take the requirements document, you create the requirements document, write the use cases, so that the input, and you basically know that the customer has this problem, which has to be automated, and hence the software has to be designed so that you get a particular output, right, that is your traditional method of software programming. Now in machine learning, it's exactly the opposite, right? in machine learning, you're basically taking all the data that is available in the organization, all the inputs and outputs. Now you take the input and output and then you train the machine, right, you train the machine based on the algorithm so there's a lot of mathematic some statistics involved not to get scared at all at the level of your class 11 and 12, which we tend not to remember, or we would have studied but not appreciated it. In fact, when you do machine learning, when you learn the mathematics and statistics and revise that of 11, and 12, you'll realize, I mean basics of like linear algebra matrices, differential equations, how important they were we we never understood why the hell were we studying it in 11, and 12. But when you do machine learning, you exactly understand, oh, this is where I apply these mathematical topics, right. So if all of you have a notebook and a pen handy, feel free to keep it handy. I'll talk about few important things, you can note it down, because that will help you towards making sure that you have made some kind of a preparation for you know, getting ready for the program, right? So feel free. Can you repeat the mathematics? Okay, let me do one thing, let me go one by one. So the question is data science, okay, this is not about you know, for example, data sciences, a huge vast area, remember that. So this is a specific area, which is handling, ai ml, which is artificial intelligence, machine learning. Also, the third fourth module focuses on neural networks, deep learning, right. And then you also will get access our understanding of tools at a high level. After that, you have to kind of specialize yourself, which is on like TensorFlow, kiraz. You know, all these are not mentioned in the curriculum, but they get covered when the professors teach, and the professors are from triple it, you know, they've studied all over the world, and the best universities in the US, and some of the best papers that they publish, as well as you will have industry practitioners, like we had different people from, you know, the startups who are doing data visualization, a fantastic one, you know, and then we had people from data scientists from Microsoft, so you will have a lot of people who are actual practitioners from the industry also will come and teach you. So that's, you know, something which you will get a great exposure towards. This is useful for mechanical engineering candidate. So let me keep answering one by one, right. So I just answered this. And then. So in the last, I think three or four weeks,
I think I spoke to close to about maybe seven, eight, mechanical engineers, all of them had an mechanical engineering, see what's happening in the mechanical spaces, you know, the jobs are getting automated, right. So obviously, the number of jobs are reducing, right, because there's bots taking over, if it's a software lead, or the robots actually taking over. So that will be conducted continue to be the trend. And hence, if you're a mechanical engineer, who has actually done a four years of mechanical study, and after that, if you learn, you know, things like RP as well, as you know, AML, after maybe couple of years, once you specialize, you actually will have an edge to apply for a lot of jobs, which don't kind of you know, exist today maybe because they will look at people who know mechanical engineering, handling the various mechanical equations, pieces, it could be space side, it could be on, you know, the robot side. And you can apply AML, right, you need to have some understanding of programming mathematics, Mathematics and Statistics, and then take it forward to do the modeling and, you know, come up with solutions. So remember, it's easy for a mechanical engineer to learn, say Python and ml, and go to the industry with a higher level of capability, as compared to teach a computer science or a data scientist, mechanical engineering, and then take into a mechanical job, right? So if you're in the mechanical industry, for example, Tesla is coming next year to India, right. So you see this driverless, you know, automation happening for driverless cars. So you will see the number of kind of use cases that are going to increase there is a lot of look at the space industry, it's kind of going to boom, very soon, it'll start going even more in India, with the kind of mission that rangamati is personally also trying to push towards, right. So there would be new jobs that will come up. But you know, I think the folks who start investing today, possibly you will get the returns over the next two to five years, if you can, you know, stay invested in this. So one minute, let me just try to cover was this question? This is from double Sam from telecom domain ran part of the base, we're basically doing quality Okay, making sure the quality of the network and I'm curious to understand where and how can I apply in the mechanic Okay, that telecom domain. So, I think that will lead it basically, if you look at in the telecom sector, C versus VSS, I mean, this is obviously you know, a huge amount of application which is there. On the right hand side, for example, you're talking of your into the quality of the network itself, see quality of network. I mean, if you look at even today, if you look at that, lab, for example, gives you an option, wherever you have a problem with the data or voice, it asks you to select your location and report the data, what essentially any telco is doing today, they are just trying to make sure that they're capturing the current time timestamp and at the timestamp, the customer is in which location with what kind of an issue right? And then they starting then they run the test reports and they apply a lot of algorithms, which is internally all machine learning in order to figure out first figure out the problem itself. Where is problem, because if you see that there is Wi Fi calling today, you don't even have to, you know, pick up a tower, good old days, you know, everything was a dependency on the GSM towers. So there is a huge number of use cases, you just possibly, you know, go to Google and find out I am not. I've been out of the telecom domain for almost seven, eight years, I used to work with British Telecom, at&t and Vodafone, some of the projects. But off late I've not been but I know there's a lot of innovation happening. And there is a lot of you know, MLK, use cases that are also then the telecom domain. So feel free, I think you should just check what specifically can you apply? But yes, I think that wherever there is data, wherever there is data that is not studied, whichever is not kind of, you know, taken to the level of forecasting or prediction. I mean, that's where I think you have an opportunity ahead of you. Let me so I answered, I think this is useful for mechanical engineering candidate, I think I've answered that, then we have when we have free resources to learn machine learning, why do we have to a very good question. Exactly. So I think I will, I totally I, I had about 170 people in our batch, okay. And all of us tried out all the free courses and as well as some paid courses, like, you know, you have this offers 15,000 rupees Coursera or Udemy course, for 550 rupees, all of his bought all of that many of them, you know, and then after that, you know you Andrew energy is there, there are free youtube videos, even if you go and subscribe to talentsprint. Today, you will see lot of free videos are there, right. And I think you should go and at least pick up the Python programming videos, which are there from Professor ashokan, as well as some of the other you know, the folks who teach Python, so just try to go through those videos. Now to answer your question. A lot of
you know, discipline is what is required, I think one is discipline. Second is the rigor and hand holding by somebody who's competent, right? So what happens is, you try this out for the next two weeks, pick out take your Andrew energy or anybody's video, and you know, just subscribe to a course or online program and try going through it right. I mean, you will get to a particular point, but there is a very 90% chance of stack falling out, because you get bored. After a point you realize, Hey, I'll do this tomorrow, I'll do this tomorrow, you know, what's the big deal? I paid for it? Or it's free? I'll do tomorrow that tomorrow never comes? Right? I mean, two years passes off, tomorrow will never come. So in the process, you will see that you another person who's gets maybe enrolled in a structured program, after two years where that person would be in where you would be right, you can try this as a test. Now, I think the same question all of us had asked, right, we should we do a you know, kind of this kind of a program and everything is possibly available free today. So in our patch, we were in ml 12, we had about roughly 170 people around the world. Okay. And you name a company. I mean, pretty much everybody was there, you know, starting off from whether it's Accenture, Cognizant TCS, Microsoft, Google, JP Morgan Chase, Wells Fargo, and in a bunch of other companies, there are people who took sabbatical, they came back from, you know, they came from Singapore, they came from Dubai to do this program, right? whereas they could have actually, you know, sat there in their own country without losing four or five months of salary, right, which is in foreign currency, yet they decided to come into this particular program. So definitely, you know, everybody evaluated the pros and cons of the program before they decided to go for it. And second thing is, I think it's the alumni, you will basically meet a peer group like here itself, when you meet people, you will see a lot of people are from, you know, some of the best companies and working in different, you know, domains. So you have a fantastic peer group, and 70% of the peer group, the push from the peer group helps you learn, it's like going back to college. Right? The amount of learning you had during college you accept or not, how much of our RBT is role based trainings your company does for one week and whatever. It's a very different ballgame. College learning, I would say, there is no substitute because of the rigor, the continuous focus on exams. So in this program, you know what I heard in our case, it was a four month program and we roughly paid about two and a half lakhs. I heard that right now it's about
six month program based on the feedback based on the feedback, it's a six month program. So you have two months more time. And I think also there's some scholarships that are being given. Yes. So I think it's a fantastic one. And then after we all passed out, you know, we all got the triple ID certificate and we become an alumni of triple it, which is a lifelong, triple ID kind of, you know, alumni status, you have an email ID from triple it, then you can engage with your peer group with a lot of people wanted to know that after doing this if they want to pursue ms, MTech or a PhD from triple IIT Hyderabad to they get a credit there is if you see the website, they do say that they give a credit for this particular program, right people who want to pursue that. So those are your other advantages. So and the other thing is triple it is definitely worldwide, you're getting a certificate from a pretty renowned institution, right? So that is another big plus point that you have. And I think the interactions the interactions that you have a one on one level with The professors with the guides, and then when you're stuck, right, you kind of have the peer group working together, if they can solve it, they'll as the guide. And if still that doesn't get solved, you have the professor. So that ecosystem, and the rigor in which you're basically going through the learning process, I think, is really a very balanced push for you to complete the program. And this is a very competitive program as well. So you have, you know, every Saturday, Sunday, you have to make sure you commit to it, and, you know, for six months, and then you you have a beautiful grading system, a self evaluation, which is every day before you sit for your daily class, there's a preparation material given to you the previous week. So you go through the preparation material, then Sunday, something called CFP check for preparation. So you basically evaluate whether you kind of prepared well enough. And after the day's lecture, you basically go and, you know, answer the quiz or the questions that are asked after the professor's class. So that's called check for understanding. And then there are many hackathons every week, and there is a major hackathon every four weeks, which is every month, so all of you will be part of a project team. So you will typically have depending on the size of the bag, something between four to seven people in each batch. So not everybody is going to be the same profile, right? Somebody could be hardcore programmer with two years experience a one year experience, somebody could be a CEO with 25 years experience or a vice president with 20 years experience, somebody totally from project management D has no clue about Python programming is not even interested in Python programming. Some people are hardcore techies, they just want to master python programming, you know, they will just translate all the mathematical algorithm code. So it's a beautiful mix of people. So when you when the project rooms, teams are getting created, it's a beautiful balance, where you know, all of you and we used to have in our batch, after office, we have a disciplined Scrum call between the team members. So we had about six, seven people, six people. And every night after dinner between nine and 10, or 10 to 11, depending on an office complex, all of us would get on a daily call to discuss what is the problem statement for the mini hackathon or the hackathon? What is the approach we should take? You know, do the analysis, do the design structure the solution properly, and then attack the problem? Right. So I mean, this is basically a typical how you do provokers projects in your, in your respective companies the same way you will do this, as well as you will have the same mindset of going back to a good college, and you know, do the program in a structured way?
Yeah, children. And there's one question from church data saying and I want to take that up, she asks that this is going to be an online program. So how effective will it be, since it's not a classroom interaction or orientation, nor can we go to the lab. So Incidentally, yet, Jonathan was part of the program where, you know, it was still classroom base, so to speak. But thanks to the pandemic, we've all learned to, you know, live and work online. Now, what has changed here is from the time that you know, the previous batches to the batch, which is now currently online, one thing is that this is an interactive live online class. So you actually get to talk you can interrupt the faculty just like you would be able to do in a class, ask your question, study groups can be formed live, and you guys can move on into a live breakout room and work the faculty can come in, at any point in time he or she chooses, the labs are completely online. And these are live mentored labs, instead of the physical labs that are coming in. So it's, it's not a very different environment, than in a classroom, you get to see the whiteboard of the faculty just like you know, notes can be shared. So all of that is actually live classes. So we have been able to simulate the entire live classroom environment. And one other tidbit that I'll put in is that we conduct a net promoter score, which is just kind of a proxy for you can see that it's customer satisfaction or student satisfaction score. That's a proxy from the cohorts which have gone online, which started off some time with the cohort, which started off in March, the net promoter score has actually gone up for the cohorts which moved online. So in terms of a learning experience, there has been no dip in terms of the ability of people to interact with each other and learn. So that's something that I would like to put in. And that's expired, primarily because unlike, you know, other online programs, which I understand that a lot of people would become, you know, would be comparing this to other online programs, most of those programs have pre recorded videos, etc. And they are not live in the true sense of the word. So this is an actually a live class where everybody is live interacting with each other. The platform allows us to do that. And this is a very robust platform. Incidentally, the same platform is being used by IBM, Calcutta to run its main two year MBA program right now as we speak. It's being used by IIT Jammu to run its engineering program. So So you look At the end, if an IIT is able to use it and all the other, you know it the executive programs that we run I in Calcutta executive programs that they run with us is run using the same platform. And these are completely these have always been online programs. The main MBA right now was obviously a classroom program, but it's come online. Thanks to that. So if these, you know, top institutions are using the same platform to deliver this, I would you would not find that there is too much of a difference between that, you know, the the classroom experience versus what you're doing right now. So that's something that's there also, there's been one change, I see a lot of questions are there in terms of placements, etc. Two changes have happened right now what we are doing is that which was which we are starting from the cohort 16 that we are going to be starting, that's going to start its classes, late December, early January, is the fact that you are now what we have is a career accelerator. It's, it's a combination of, it's something that will help you, you know, make the best use of all the opportunities, we have curated job postings that are available to you as a part of doing this program, we will help you out with your professional profile, you know, interview, prep all of that, as you make and stuff like that, and you get job postings that come in open whenever they are available. So we work with around you know, 700 corporates as part of the network. So that's something that's there, you can find the information about back on the website. If you really look at it, people ask whether it's a guaranteed placement or not. See, one thing is that it doesn't matter even if there is a guaranteed placement, right, if you pick up the skills, you will probably not need to worry about a guaranteed placement. And even if you don't, even if there is a guaranteed placement and you don't have the skills, you will probably not be able to hold on to the job for very long. So you should focus on building the skill up. And that's something that I've been, you know, telling people across the more than decade of, you know, advising professionals is that you know, pick up the skill part, if you have the skill, the job will follow you instead of you know, you running after a job. So concentrate on that. But yes, right now, with the career accelerator in place, we do have enhanced opportunities for you to land that job you have, you know, the alumni network that's there, that Chilton has been talking about is now spread across the entire spectrum of talentsprint programs. So we have an alumni network, there's a portal as a separate portal. So you can basically interact with all of our program alumni, which means you can talk to somebody who has done an I am Calcutta FinTech program, you can talk to somebody who's in digital health from IFC, or from the data science program that we are running with IFC, to a cybersecurity program, which just increases your opportunities to connect with people. All of these are very senior professionals, they post a lot of jobs on the portal. So all of this is coming in together. And we've kind of formalized this, this is a new change that has happened, which was not available before, which gives you a lot of opportunity. So this is in answer to all the questions who are, you know, where it's the placement, guarantee, job, etc, etc, there isn't a placement guarantee, because we don't I mean, you know, we've had patches by children in his back, there was nothing of this sort which used to happen, but they have all done it, etc. So now we are putting in some additional resources. If you pick up the skill, you will be able to land, you know, any job or be able to switch, you know, and companies have, you know, being able to switch to a career, etc. There are other prerequisites there that are there as well. You don't land a job, just because you have a degree from someplace, you may have a degree from Harvard, so to speak, or certificate from it. If you don't have the skills, you will probably not be able to crack it. So that's, you know, that's a couple of answers that are there. How many hours of weekend classes and how many hours of effort on a weekly basis for that
at your at your time, I think you had what three hours of classes on a Saturday, and then the entire lab sessions? That's right, right.
Yes. So I think it did vary from module to module. And in our case actually, we also had the last module that was online. The first few the first three were in classroom so I think it typically your Saturdays are classroom and Sundays would be your hands on project where you will be having, you know, development of the mini hackathon, hackathon. And on Saturdays, there are times where you could have a first half second half, full day class but in between, you'll be working on your exercises, the exercises will be given which you need to solve. And sometimes it could also be like the first half is class classroom, the professor and the second half could be, you know, exercises that you work on. And there's a lot of scope for you know, like, if you're falling short of the learning that you can always revise because, you know, I think after every module they catch up with the next professor. When he picks up the next topic, he starts with the revision. And you know, a different professor would give a different perspective or a spin for the same topic that we learned earlier. So that's good reinforcement. And I think the Hubble has a question about I've done this curriculum, I've gone through the curriculum extensive concern a six month enough, well, the same curriculum we did for four months. So if we were able to do four months, all of us working full time right on very intensive projects. So yes, if you're if you have a disciplined plan, you have to of course, you sacrifice your six months of vacations, and Saturday, Sundays you to commit. So that definitely will need some kind of commitment from yourself. And if you do that, then I think there's no reason why you can't do it. Alright, so one question is Santos, working as a lecturer? I'm interested in data science, is it possible, I think if you're a lecturer depending on whichever area, you know, you specialize, this is just to add an edge. By the way, I just I also tend to take classes. I'm a faculty, I'd take classes at IMD, that's more on the management side. So at IMT Ghaziabad shamshabad campus has a huge campus. So I take a more on the management side. And when you pick up any new topic, a new skill, it's gonna add up to your carrier. So if you are a professor or lecturer, I think if you add a IML to it, there's a very high likelihood that you know, the dean or the university Chancellor, whoever you interact with the head of the departments are going to look at you for taking these new classes which you have learned on the animal on your own. So I think definitely, you will end up getting better recognition, more opportunities. And at the same time, you want to specialize See, any subject after you learn, it takes a while for it to sink into your head, don't think that after six months, once you complete get the certificate, you will become a master know, once you learn the subject you to practice that right. It's like a typical experience, all of you who have gone through the experience, you can imagine what level of knowledge you had in your respective engineering, after passing out from the college, as compared to after you finished working three to five years in the industry, same thing will happen here, you will continue to mature with the level of knowledge that you gain in six months, pause that as much amount of exposure that you go through, you keep upgrading your skill, but then you will be able to talk the same language you know, I give this example. Today, possibly you can't even talk to whether it's clients or whether your internal team members or your projects which are working on AML. Because you don't have that AML terminology understanding or Python programming for that matter. But the moment you pick up this, you can start communicating with a larger audience who understand this language. Like for example, if you land in France or Germany, you better know the load local language, right? So tomorrow, if you do work on a project, which is an animal, this is a prerequisite right for your yourself to get upgraded. Only when you have upgrade yourself, you are eligible to even go to your current resource manager in your current company if they have an AML practicing that, hey, this is my new skill. I am capable, competent from a only IML and have a certificate from triple ID Would you like to consider and put me into a project? Right? So ask them to put you in a project. I saw some questions around tenant. And just
in terms of this question for the gentleman who wrote a gentleman or lady who's working as a lecturer in case you're interested in a pure data science program, we've launched a data science program with iisc, Bangalore, that's a 10 month long program. So in any if any of you are interested in only a data science program, you can look up that program as well. So that's that's one change. That's also happened. It's we are going to launch the first cohort for the data science program in the month of January. And yeah, so that's going to be with ISC 230. Children.
God. Yes. So I think one question was, what are the prerequisites for this course. So I would say, you know, for doing like many of us, we didn't have time to prepare and go, we just landed up for the course. And we just caught up, right, it was just a catch up game. But what I would say when I look back is, if all of you many of you may not have a programming background, but it doesn't matter. Because after I did the program, first thing I did is I told my daughter who's in grade six to start writing Python code. And today, she writes better code even than me. So I think that's a good plus point, right? You just need to be a student willing to learn. So even a class 10 I mean, sorry, a class six, who somebody is like 10 year old can pick up Python because it's very English, like very simple. So I think all of you should spend some time, bookmark the YouTube site of talent spring, and just go through the videos. And there are a bunch of other you know, Python courses. You know, just I'll just give you some of the points which you can note down python.org and there's a whole documentation of python.org. Remember, Python, mathematics, statistics are huge subjects by itself. You don't need to look at everything. Just focus on the one that is required for AML. So for example, again, I'm repeating somebody asked me to repeat. So what is it that you need mathematics for AML you need linear algebra unit. differential calculus, differential equations, matrices, you know, at a high level, just ask Google you'll get all the answers, right? What is the mathematics required for a IML? statistics, simple concepts of Mean, Median mode, when you have to do linear classifier, you come up with different algorithms, right? basic mathematics. So you have to enjoy, you're not doing some part of those, you know, mathematic revision of your class 11 and 12. Many of you have asked questions am a DB ama QA. So I think, for a person who's a QA, top company ID company, remember, every company is going to have AML projects today, if you're a big company, you will definitely keep landing ml projects, if you are certified in AML, that kind of testing that is required for AML is very different. Right? So I think, essentially, if you look at the the level of testing that needs to be done for traditional software that gets developed versus AML, are different approaches different. So you will be capable number one, to to start working on projects, which an ml either testing. Second, you could also take the route of becoming a VA, okay? You can, if you're good on a domain, particular domain that you're working on, you can even become a business analyst and apply your AML knowledge to even do requirements, you know, what kind of requirements are required to be captured for developing of AML algorithms. So that is the other plus point. Ram Jesus any other skill required? I think, as I said, Python, mathematics, mathematics for ml and statistics for ml, and skills. What does that required for interview for 12 years experience guide?
Now we want to cover that as a part of the career accelerator.
Okay, perfect. So even if you're a DBA, you're handling data, it doesn't matter, because you're going to be switching into a different carrier route. How are we doing on time? Because a lot of questions, just want to make sure
we understand another five to seven more minutes in trial.
I just want to make sure that I cover most of the questions that have come up and where is the course commencement and timing everything through you? I think it's in December, right at end
December, early January.
Yeah. So few seats are left, go and grab them. Because I'll tell you what, you know, when I was thinking about it is you know, just think about this, all of you. If you had to today invest a 2.5 lakhs. And that's what I calculated in a fixed deposit. It doesn't double in today's interest rate in 10 years. Also right on top of it, there is a 30% income tax that gets deducted. I decided that okay, if I put this 2.5 lakhs in my own carrier, right, can I make forget my annual increments and my promotions that will happen in the next three to five years? On top of that? Can I double this? By investing 2.5? Can I in three years earn 7.5 or 10 lakhs? If I've done that, my job is done, right. My investment from this program is taken care of. Plus, I have a like a rotary land club membership. I've got a triple ID membership. It's the elite membership, lifelong advocate to eliminate status with a complete peer group. We have WhatsApp groups, you know, people from Google responding to many questions from Microsoft, you know, JP Morgan, everybody's posting a there's so many jobs in this particular group company, you know, want to apply. And then many people are running their own companies, right? When you first run your own companies, we are tapping into the junior resources who are less experienced, and to help us on projects. So it's like a, you will see a complete different aperture opening up. So you should try to take advantage of that and learn to network because it's a network that's going to take you a long way.
Absolutely. And just wanting to hear you know, reverse inflation is working, you will pay 2.5 lakhs currently, you can come into the program with all of these scholarships that are there are probably around 1.7 plus GST so that so that to
six months of money,
yeah, it's become more value for money. There is a career accelerator which was not there. So unlike other programs, where things are becoming more costlier here, it's actually coming down and more values being added. So this is a great time for you to actually look at the program like this. So somebody was asking a question about scholarships. Yes, there are specific scholarships for women. If you look at the chat window, you have yojna and below guineas numbers coming in over there, have a call with them, they will be able to help you out and understand you know about the requirements for the applications.
So I'll just quickly touch upon some of the answers. How do I move to AI because current responsibilities are not that obviously, because you're in a current different skill set with the current you know, kind of job you're doing. Only when you acquired the AML knowledge you will be eligible to apply for jobs both internally within your company, as well as outside right. I mean, today look at just all of you research, right? Look at the LinkedIn, monster dice naukri. Look at the number of jobs at your level, you will see a wide number of jobs are getting posted right? You are not eligible to apply today. But if you take the program by February, March, I'm sure in three months you will have the confidence to update your resume and start applying and start giving interviews so that maybe by about next year, this time, you all will possibly be changing your career. Now zohaib said compared to doing an MSN course, related to AI on data set How good choice is the course at triple it has about it depends on you if you can invest two years full time in MS program, right and you know from a top university, which gives you a guarantee of a placement, right and it will evaluate the pros and cons. I mean, nothing stopping you if you're not working. Now, if you're looking for doing an MS for two years, it's up to you, right? I mean, you have to evaluate and check it out. But check out the price point and the value for money in all probability. Whatever a typical ms program covers or MTech program covers, you will see most of that syllabuses compressed and in MS, you will have theoretical professors alone teaching you whereas in this program, you will have industry practitioners who will come and teach you on what exact projects that they're working on. Right. So that is a different experience of learning from the industry practitioners. So it's a combination of both. By the way, I have been just looking at all the questions and answering in the q&a window, I have not gone to the chat window just so that all of you know I have not read any of the questions in the chat window. So I've just been, you know, covering the ones that were appearing in the question answer window. And I'm perfectly fine to extend this, you know, the call so that everybody can take benefit and
take some will take five more minutes. In the interest of the questions. I've also opened up a poll in case you would want to give some feedback, we are happy to get that feedback as well to all the participants.
Sure. So I think if any of you have a question that is not answered, right, please feel free to post it again. Preferably in the q&a box, then I can answer that. Okay, so post that question. If it's not answered, I'll be happy to answer that question. But, you know, I think yes, all of you do your homework, as I said, and make sure that you kind of commit your time for the six month period. And you know, this is going to be a great investment. As long as you make sure that you have the you know, kind of you need to have your vision, because I think all of you need to ask, Who said talentsprint might provide opportunities where companies are coming in, but you need to suppose you're a DBA, you're a techie, you're a be a project manager, Program Manager, Vice President at a leadership role. You need to have your clear vision, how you want to monetize the skill, it has to be your vision, because once you have your vision clear, you can easily materialize that vision. But it has to be clear from your side on what you want to actually do. Because opportunities will always will keep coming. Okay, any other questions? You can unmute and ask if you want to? which language you will be working on? Answer is Python. In this case, you'll be working on Python tabraiz what is the cost of this? It has already been covered. I think 2.5 lakhs for four months has gone down to as a special offer for 1.75 plus GST for six months.
Yeah, it was 1.7 plus GST, right,
right. What is the cutoff date to register for this program? Maybe it's today? I don't know. An answer.
We will, we'll have there's a earlybird. That's closing, I believe it's closing today or tomorrow. You can look at that and you know, in a couple of days, probably in a week and a half, we will try and close the batch.
Okay. And what interviewer will check for the resume a for fresher? Okay, experience all right of for a fresher or experience. The question is not very clear, what interviewer will check for resume as a fresher or experienced maybe you should read? Let me know answer what I understood. If an interviewer has to look at a resume a, first of all, if a person is hiring for AI ml, and if they see that, oh, this person has an AML program from triple ID that gives a lot of confidence of an interviewer. It gives a lot of confidence that triple it is a respectable Institute. It's not like a fly by night operator, right? It's not a course or a program or any program, somebody did it sitting with their friend, they would have done the papers and got a score all that nonsense doesn't happen right? So they will get a lot more weightage for a triple ID certificate number one. Number two, if you are a fresher, whatever stream you are, whether it's mechanical, electrical, electronics, computer science, the probability if you get an AML certification from triple A to get calls as a fresher and get a little more higher salary compared to a typical beat a computer science is always going to be higher. So I think I always tell this if you are a fresher, I mean it's a great opportunity to start your career with the IML because you're completely getting into an area instead of a typical you get hired with Infosys TCS and get into a production support project and keep on getting 3.5 lakhs 3.75. In the sudden you will have a journal you will have a backup you will have a flat you won't get promotions for next three, four years with the recession going on. You know, so instead of that if you do end up working on more high end projects, so that's what I would say. For QA. You mentioned the career path. Yes as a potential. So Katyn, it is your wait how you sell yourself if you're a QA, you already are a domain expert you understand testing you understand the software industry. Now if you really pick up ml as a complete understanding of mathematics algorithms Python programs mean right then you can become a white box tester for AML algorithms as well number one. Number two, if you are a good specialist in a particular domain, then you can definitely become a PA because you have an opportunity to work with the clients. But again, you have to sell yourself in the interview either internally within your company or externally. In my career, I've seen some of the best ways have been those who got converted from a QA job they were QA leads or you know good QA engineers, we took them as a B roll they went through the grind you know and of course we did we do a lot of BS who come from I am hiring eyes be hiring all the time. But I think some of the real good QA is also bs come from that you will track as well. So it is all up to you how you want to leverage your you know, knowledge and play as a it's like a cross you know, role it will change over your across your own app, an F tech from nit 14 years experience IDs. So I think I saw your question early. I was about to answer Lakshman para la sala Lakshman. You just look at the triple ID website. If you've done your MTech, and there's a score triple it has I think they have their internal examination or they have the All India exam for PhD right. So, you have to have that, you know, you have to get shortlisted, it's a competitive exam, there are limited seats. So, if you want to do a PhD, depending on which area of your MTech you have done, because if you have not done MTech in say, high end, you know, image processing, or you know, computer vision, or you know, any of those, that you will not be eligible for a PhD directly from triple it immediately. But the AML program will basically bridge that gap. So, to answer you, you will be eligible, but you have to figure out what is the eligibility criteria from the triple ID website? Okay, one important thing I want to answer, what kind of projects that we did, I think it'll be very useful for you to know. So first month, we did a lot of NLP natural language processing, which is, you know, bag of words, you have a complete, you know, bokhara comprehension, you figure out, you know, this speech was from which President for example, right, so that's basically natural language processing, then sentiment analysis. Then after that, we did a project, which is on voice recognition. So there's like, say call center data, right? You got a lot of data from call centers, how is the you know, temperament of the person who basically is handling that particular call? So is the is the customer upset? And then yes, no answers, in what tone right upset or what, what? So you can do a lot of machine language analysis, Fourier analysis, using the voice data, then second, we build chatbots. Right. chatbot is very important. That's become as part of the standard process in every industry. So you will learn how to use and make a chatbot. How do you train the bot? You know, how do you train it? That is one second is computer vision. So we had we made an app, when the app you basically would unlock your mobile phone using your image. So you would you would create your own algorithms and train the machine to detect your face. So if your faces and somebody else's, they're a friend of yours, it will not unlock? So and then the other was expression, for example, am I sad showing a sad face or a smiley face? It will detect the expressions as well. So those were all the kinds of projects we did and was very interesting, you know, some things that people in Facebook and Google end up doing. Hamid here 17 years experience in oil and gas industry automation background, he RP solutions perfect. Can I be eligible to qualify data science course? I have 14 years industry experience? So the answer is yes. I think see everybody, all of you are eligible, I think what you should think is, think about that you're getting fresh in this industry of AML. So whatever you have done is a bonus. It is already your past experience that is existing, that is an added advantage. Because after learning HTML, you will see today, you don't even know how to use the data in your for example, process automation that you're talking about. The AARP has a lot of data today, you don't even know how to use the data and provide a recommendation a prediction right? For example, you know, the kind of the, what is the impact of coronavirus pandemic, on your production of your oil, natural gas? Can you predict that? Right? Looking at the trend of last one year maybe yes. Okay. But today, you as a professional who's only into oil and natural gas may not be able to predict it. But the moment you have your ml knowledge, you will suddenly realize that, hey, I have so much of data in hand. And like everybody says right data is the new, you know, kind of oil. And the you end up using that to kind of restructure the input output and you know, train a machine and come up with new algorithms that will give you output about the same, you know, potential areas where nobody even thought about how to monetize that particular data. So it all depends on which area you want to whether it's in production within distribution, it all depends on which area you're thinking. So the question is, yes, there is second See, the traditional jobs are being cut, as you see, like the simple point would be more and more jobs are getting cut over time. So if you really want to be relevant, and even this investment will likely will not go away because once you have the six months of learning on top Perfect, you can keep on building, let's say you're building up a one floor house and you have a plan to create a five floor house until you create the one floor of your AML. You can think of doing, say a specialization in neural networks, right? Getting into the next level of advanced, you know, say deep learning those all you can keep doing only when you've picked up. Like, for example, how do you want to use Kerris and TensorFlow, there are separate programs, which you can just sign up. And once you know, ml internals, like when you do automobile engineering, you're taught how to design a pin inside a carburetor. But when you actually join a company, you don't bother to do that you take it for granted. Similarly, what you learn here is under the hood, and tomorrow with this knowledge, you can yourself keep learning advanced topics over time.
Benita Rao, I don't know programming, from which program, should I start, I'm very interested in this program. So Juanita, you should start with Python. And that's like English, very much like English. So as I said, you can sign up for the YouTube video, you know, videos, which are there from talent spring, and go through them. That's a fantastic starting point. And, you know, python.org, and you need to just sign up, you go to Google with your Gmail ID, Google collab. So that is your ID for writing the Python program on the cloud. And that's what you need. And for mathematics concepts, all of you can note down this, it's called three B, one B, it's a YouTube channel, it's a very, very popular channel. Okay, so for all kinds of different types of mathematical concepts, they have simplified the entire learning. So just just go through the YouTube channel, and you will see that you will get a bunch of videos on three v one v. Okay, so just subscribe to the channel is three, blue one, brown, three, blue one Brown. Right.
Time check that time check. We are at 640. Probably one more question. And then we'll wrap it up. You also have yojna. And we know the nice contact numbers, which are there on the chat. They are, you know, part of the team that you know, recruits for this program. You can get in touch with them. So last one question till until whatever you want to choose.
Yeah, I think I've covered the ones that were in the q&a. I kind of hopefully I covered everything there. I don't think anything is left out. Is there anything in the chat window that did not properly get answered? Now? I
don't think we kind of unwell going through that. I think we've answered most of the question oil and gas many of them actually repeated. That's I think, yeah, we we've done with most of the questions in case you have any more questions, you know, feel free to reach out to yojna. We know the knee with your query. We're sorry, we couldn't answer in the interest of time, you know, we thought that we want to wrap it up here. We're already 10 minutes past the time. Thank you again, Jonathan, for a very, you know, interesting session, it's great to see you know, your energy as always, and your willingness to answer all of the questions.
And to me, just having two questions, or Naga has as working as AWS cloud engineer. And with some experience, you can definitely leverage ml, or definitely anybody's working on cloud has a huge opportunity with AI ml, it's an add on Shashi Kumar, has that have reached around research and the research field for last 20 years? Is it useful for my career in the corporate field? First of all, I think it'll be definitely very useful in the research field itself, number one, number two, if you plan to kind of fields also kind of contribute towards the academic side? Yes. And corporate field depends if you're working with the industry, and if you have any contacts, or if you apply, so if your area of research is something which is in demand with the industry, definitely, I would say yes. So you know, I think, see, I think, for every question, I see everything very positively, because this is a very, as I say, it's a broad spectrum antibiotic. So you know, if you kind of feel are looking forward to, you know, quickly patch up your existing illness, if I have to call it in terms of, you know, not having the level of skill that I should be desired, or that I feel that I should be having to compete in this industry, right. So I have to be a little ahead of my peer group, so that you know, have more eligible in the competitive market. So my simple question would be, yes, go for it. And once you sign up for the, I'm in the program, I mean, within the first one of two months, only, you will realize the amount of learning that you will go through, and you will not regret for sure. I don't think there's anybody who would regret and it's a worthy investment that you're going to make and you will get the returns. But yes, create a clear vision for yourself. Create a mind map, create a vision on how do you see shaping up your career, you know, immediately within three months by say, February, March, update your resume a and apply and then carve out your niche area where you want to kind of get into?
Absolutely. Thanks, Jonathan. Thank you so much. You know, it was wonderful, talking to you today and getting your insights for the program. And your experience of the program. For anybody who's not yet voted, you know, it's the year still open, if you can, you know, let us know your feedback about the webinar. We will do more of these sessions. Perhaps we will get Jonathan again in one of the other sessions, to answer some of the questions that we could not. Thank you so much. And you can reach out to below the knee and yojna their contact details are there on the page applications are open for the 16th cohort of the triple it hydrobath talentsprint AML program. If you want to know more about the program itself, you can go to www.talentsprint.com and get those details. Thank you so much for joining us today. Stay safe. It's been lovely talking to you. Thanks again. Sure. And lovely to talk to you. Thanks. Thanks.
You know, I wish you all the very best all of you. So do well. And yes, I think we are part of the alumni group. Look forward to catching up with some of you again, take care bye bye.
Thanks. Bye bye.
Watch the entire interview here https://www.youtube.com/watch?v=35-AOKCiOzE