Decision Making with Professional Certificate Program in Data Analytics And Gen AI | Simplilearn

Key Takeaways

eight. Okay. Manoj Kumar, Johnson, Thomas, Reuka, hi Maria, Sundar Kumar. So there there are people from Mumbai, Nigeria, USA. I can see a lot of responses. Hello everyone. Hope you hope all of you are having a wonderful day and we are going to give you a really insightful session as well. Uh so yes, it's time and uh we are also live on both the platforms. For anyone joining us uh on YouTube and LinkedIn once again welcome to the session. I am Schwatha and I will be the host for today's session. We will be talking a lot about data analytics and generative AI in the session. So you guys can also introduce yourselves. It's wonderful to see you all today. So again we have Deepak Kumar from VIT, Brashant from Hyderabad. Hello uh Ashita Robin from London. We have a lot of people joining us. So, thank you all for sharing your responses. I will quickly share my screen and we can start the webinar without wasting much time. So, I hope my screen is visible to all of you. Can you all let me know in the chat? Okay. All right. Thank you for your responses everyone. So, once again, welcome to today's webinar. We are going to be talking about how you can make decisions with the right data skills and we have a wonderful speaker joining us today whose introduction I will give you once I take you through today's agenda and give you a little bit more uh details about simply learn as well. Uh so before we dive into the session, let's quickly take a look at the agenda. Uh so first we will explore the power of data and generative AI in decision making. We're going to walk through some real business applications, discuss challenges and best practices and finally we're going to take a closer look at the IIT Gojharti program that will help you build these exact skills. Uh Sara will also be sharing a lot of his personal insights and experiences with us. So this is going to be a really insightful session for everyone. uh a lot of value is coming your way and please stay till the end because we will also be sharing a form uh for the certificates of participation and before we begin the session a few ground rules if you have any questions please put them in the Q&A box so uh we will be taking up these uh questions towards the end of the webinar because we do not want to disrupt the flow of the session uh and please use the chat box for communicating anything that's important for the webinar please do not share share irrelevant links in the chat and uh for your attendance uh let me clarify we will be sharing a form towards the end you can fill it up uh share your full name and you will receive the certificate of participation within 24 hours of the webinar and uh we also get a lot of questions regarding the recording and the slide deck you will be receiving that as well but uh within 24 hours along with your certificates so now let me quickly take you through uh simply learn. I'll just give you some quick insights. So, are you all attending this for the first time? Have you attended webinars with us previously? Are you aware of it? Uh it would be great if all of you could let me know in the chat. First time. Okay, I can see a lot of first timers here. Yes, attended. So, participants who already know about us. So, for anyone who's new to today's webinar and to simply learn, I'll quickly tell you about Simply Learn. We have for me one of the most exciting parts of today's session. So introducing our guest speaker. Uh so our guest speaker for today is Sadharta Dash. Uh Sadharta has over 17 years of experience in building and deploying AI solutions and he currently leads AI strategy at Kimberly Clark and he's spearheading generative AI adoption across multiple uh functions. So he has also built really high performing AI teams and what really stands out to me personally is his passion for mentoring professionals. So for everyone who has joined us today, you have signed up for a wonderful session with a great guide and with someone who can help you with his expertise and experience. Uh so um I am very honored and excited to welcome you Siddharta. Welcome to the session. You have built a lot of multilingual geni agents drag systems. We would love to know more about you and your journey in data analytics and generative AI. So could you please introduce yourself? >> Thank you so much Shhata. Hi everyone, my name is Siddhart. Um so like you can say I have 17 plus years of experience. Uh I don't like to call it a 17 plus and all experience is experience but let's not quantify that. But I'll tell you like what all things uh that I have been in this industry and uh uh so see I have been close to 15 plus years in the data and I have been in the industry I started working in data science even when data science was not a word also. So in 201 uh 12 to 13 you know uh there was a sudden rise in statistical analysis and since then I got some interest in that. Prior to that I was working with uh uh you know Accenture uh you know in a BI platform but then I consciously made a decision to change my career into data science and all. Since then I have seen this entire roller coaster of data science from data science things moved into machine learning AI came in. Now we have the last language models and generative AI. So I have been part of all this journey. Um so my primarily you know I have lot of experience in supply chain and financial industry. Um and what I do normally is that I spear the ideas from the whiteboard to the final deployment on the servers on the cloud. Right? So I built teams, I set the strategy of how uh things should be rolled out, what kind of use cases should be undertaken uh and most importantly you know how to quantify the return on investment that a company makes on AI. Right? So that is my specialty and my area and uh I also do a lot of u mentoring uh outside of my work and uh I also work as a architect from the technical side you know fund as well right uh creating the end toend architecture and the system design for a large scale a application for any organization right so that's about uh me and uh hopefully you'll have a good time thank you I'll hand it over to Shwetta >> yeah thank you so much for that wonderful introduction I'm sure everyone's looking forward to learning more from you and your experiences. Uh so before I move on, I can also see a lot of uh uh you know emails in the chat. So please do not share your email ids in the chat. We will be sharing a form with you towards the end which you will have to fill for the certificates. So please uh do not share that in the chat. Uh so before moving ahead I think um this also brings me to one of the most important questions that I have for you Sadhart for today. Uh so you have spoken about your experience in data and AI and that's a lot of experience working with multiple teams as well. Uh so what is the power that data analytics and generative AI holds over decision making today and uh how has it changed over time? >> Um it's a good question. See back in the day when u uh uh you know data just came in like data analyst came in for the first time people used to make decisions but that was more based on intuition right and in the industry till today also you'll find people who will challenge the analysis done by data scientist saying that you know my understanding my gut feeling says something different why should I go so that thing keeps on happening but with the rise of the uh the data culture I would say uh so there has been a trend of making decision s which is backed and verified by the data. Right? Earlier uh when the data culture first got started uh you know there was a new concept that came into the picture called as business intelligence. Right? Uh so people used to get the data and they used to create some KPIs and and you know present it to the stakeholders to make better decisions. But most of the information you get from there are very much um I would say past based right? It is not something that you would anticipate in the future. It is more more of I would like to call it as reactive decision making. So you have a data then you make some reactions on how the data looks like and go for reactive decision making and uh with the time and all uh the data science and predictive analytics came to picture and people started uh to you know catch on to the trend that let's have some projected numbers for the futures and let's make our strategies based on that. So for example a company prior to 2000 or 2005 or 10 would be mostly like okay this has been my past sales record and uh let's add just a random 10 to 15% on the historical data and we'll say that this is how much we should target for next year that used to happen but with the with the current age it is not the thing. So people take a lot of things into account right a lot of variables into account that you know if this year we have invested in some new factory or we have made our uh you know the purchases supply chain and the acquisition more streamlined so we can aim for more than even 20 to 25% of projected sales for the next year. So that's how the trend has changed and with the help of the generative AI that has been the recent trend in the market u lot of things are getting addressed. Till now the industry is still grappling with a challenge that you know which all type of use cases are currently solvable with machine learning and AI and I think it will take another four to five years for AI to mature enough and for any executive to have a final understanding of what type of problems are actually solvable right but I think this gives you a overall idea of how the data analytics and everything has uh posted over a year and um from the job perspective uh I want to tell everyone one thing anything any job profile that is related to data is going to stay. You'll be surprised to know prior to 2000 the amount of data entire internet has generated even since the conception of internet came to picture from 2000 to 2025 that has been uh exploded like exponentially right more to four to five times of prior period of data is now getting generated. So because there are a lot of data get generated there is a lot of skills that will be required in the future to make sense of the data and that is what essentially means right make sense of the data and that is why these courses are extremely useful and it doesn't matter if you're working as core into machine learning or AI right or in any other functional area but to know about the AI is a mandatory thing right now in the today depending on how much of depth of knowledge you need uh for your job but to know about it in general is a is a mandate of the day and I see a lot of leaders and uh stakeholders who are now struggling even in my company right now I see a lot of people who are struggling to make sense when the organization pushes for AI adoption uh to sense of to make sense of AI and you know what are the things that got gets into it what kind of things can be done so I think all of us understand the importance of it today thank you Yeah, that was that was really informative. The way it has changed and how we look at data, how people interact with it and how decisions are made over time. Uh that also makes it uh kind of that also brings me to another question on why uh datadriven uh decisions are so crucial in today's time. And you mentioned about how it has changed over time like in the past few years. How do you anticipate it change in the coming years and why does it matter right now more than ever before? >> Right? So see the basic principles that is involved in generating insights from the data that is going to stay. Those principles will never change. For example, how we capture the KPIs, how do we do a uh hypothesis testing on the data, how do we predict something, these algorithms and everything that has been uh created much before even the internet came to picture. Some of the algorithms you'll see are from 1930s and 40s and uh the reason that companies still now didn't have AI and all in their culture because we had limited computational power and that has changed significantly over a period of time. So the principles of making or deriving insights from the data is going to stay but how it is done that something is changing right now. So earlier in the days when uh normal any programming language caught up you know attention of uh having more robust frameworks for any kind of statistical and predictive modeling Python was one of the front runners and everybody used to do lot of Python right uh so you have to do something you have to write in Python code you have to get the data you have to have a excel or a SQL or anything like that but those processes of covering the entire life cycle from the data acquisition to the inside phase that life cycle is now shortened because of only one thing that once your data collection is done how to make insights from the data is something that is uh right now is well and way more easy nowadays so give you a simple uh example prior in the day let's as a real-time project I used to work on customer review analysis right so for a product you collect all the reviews given by the customers what are the things that they are talking about what are the things that they are liking they are not liking so on and so forth let's take a small example Huggies diapers for that matter. So you take the Amazon reviews of Huggies diapers and you see that customer somebody's talking about the fitment in a positive or a negative connotation. Somebody's talking about the absorption capacity. Somebody's talking about the skin reactions right to kind of capture the topics from this text. We used to write like you know big set of codes earlier big set of codes right uh in Python I have to say that something like 500 400 lines of code is will required for normal analysis like this right now with the help of large language models you just call their APIs pass this text give a prompt and that that does everything and gives it back to you so that's what I mean like the way the insights and the way the data is kind of treated in a sense that technology is changing thanks to I so people don't have to be essentially good uh you know in coding but they can help make help of some tools and even with some basic knowledge of coding they'll be able to do more stuff compared to how much of effort was required prior to the AI bubble came in so that has changed for that matter and that will continue um you know you will see more such tools in the market which is like self-sufficient AI where you know a no corridor or something somebody who doesn't know anything about a coding can also do some clicks write some simple English and get the answers and all although right now the lot of industries are yet to grapple on that because of security reasons and lot of things but uh the way I'm seeing the future I wouldn't say that English is a proper program English is the next programming language to be honest with the prompt engineer everything coming to picture uh for business people right technical people are going to stay you can't have you can't write English and generate applications that field is also evolving but you know you can instruct the c the computer with plain simple English rather than having a programming language. That's how the industry is going to change, >> right? That that's actually wonderful and that's also very uh informative because we do see the change happening. We do see a lot of newcomers step into it and make things that we earlier thought was not possible and like you said getting insights from data has also become easier than before and these are some major changes that's happening in analytics and with generative AI. uh are there any other industry trends in the field of data and AI that you have noticed um and how how big do you think this industry is going to be in the coming years let's say 2026 how how do you anticipate it to change >> yeah let me add in uh uh just a simple common sense for all of us the population is not going to decrease talking realistic things we are going to have more people in the future more people right now how the trend of generation is going in people are more inclined to devices and all >> right >> so more people more devices more interactions more data generated more insights being made. So this trend is just going upward. This trend is not going to come down unless and until uh all of a sudden people decided let's give up all the devices. Let's not use anything. Let's go back to the primary ages. Right? So this is going to boom. Now so far the skill set is also concerned. Uh this the num the number of people that is going to be required to cater this kind of industry with respect to the analysis and the insight generation perspective right uh that is also going to change. So now you'll see more people who had a more programming background. Right now everybody is doing a course on AI just to come to some front on AI. A programmer is doing a course on AI. A business executive is doing a course on AI. A functional guy like somebody who handles a warehouse or a supply chain, he also is doing a course of AI because this is the thing going forward. It will take time to mature. There will be a lot of return trials and all but nobody can deny that uh this is the new thing that has been coming in. Right? So we saw that com thing that that you know came as an wave in the 90s and all and this is the next biggest thing that has happened to the mankind to be honest. >> Uh I would rate it >> the next biggest thing after the invention of wheel and electricity. Let me put it that way right. So you can understand the the magnitude of the opportunity that that lies ahead and in next two to three years trust me guys knowing AI is going to be a commodity. Everybody would expect that you know how to deal with. If you are a programmer you should know how to make use of AI in your programming. If you are a functional guy should know how to make use of AI. If you are a designer you are also expected to make use of AI. Like Canva, Photoshop everybody has come up with AI right? any application that is being rolled out in the market has certain AI features. Be it a corporate tool, be it a everyday use tool, everything has AI feature. So massive opportunity ahead. Yeah. >> Yeah. Thank you for saying that. And like you said, uh everyone has to upskill uh to keep up with the AI trends or you might not be relevant in the job market in the coming years. Uh so I I love how you explained that uh the amount of data that is going to be generated, the number of people using the data and with the population just increasing, we'll have tons of data, but figuring out what to do with this massive amount of data is going to be a challenge. So that actually brings me to one other question that I'm sure all of the listeners are curious to know. uh could you help us understand how professionals who are interested in this field can turn all of this data into real actionable decisions using AI? Uh maybe uh just help us understand the process that you would follow. >> Right. So and the answer will be more focused on the type of work we do as a data scientist not as a end user who uses AI. Right. Right. because all these phases data gathering analysis something that is not something that is to be done by end user let's talk from a uh from an analyst perspective see in every company uh the data lies in disparate systems so you take any any company for that matter let's say I'm taking my company Kimberly Clark for an example we have a SAP implementation so we manufacture things uh so in the manufacturing process we use supply chain you know we source the things uh make things available in different sites different manufacturing facilities rebuild things and uh these things are then tagged and they sent to different customers. So there's a supply chain angle to also gathering data from disparate systems that gives you a end toend picture starting from procuring your uh pulp tree pulse till a diaper has been made and a customer buys it in Amazon that end to end visibility is what that comes under data gathering. Now this data gathering uh why it is important because every company they don't have one single tool or one single database to have end to-end data. So now for some of the companies they have SAP implementation so that has a different database you have some other custom applications for uh supply chain transport you purchase some third party solutions also so their database is different so data gathering becomes the most challenging part in the entire life cycle. So if you have to just classify the amount of effort that is needed in that. So if you can say that 100% of effort is needed in a typical ML or AI project end to end 60% of effort will be required just to gather the data just to gather it and put it in one place right and making some adjustment to it so that you can connect the data have some kind of universal source of truth for your company to bring the data to that front another 10 to 20% will be required the final data analysis and the data visualization or you know using some kind of machine learning model to some kind of forecasting and all or predictions maybe uh it is additional 10 to 20%. So AI comes in the final 10% of the effort that is required >> right >> the 90% of the thing is like brute force job right now although some of these um there are some tools which is trying to facilitate that for example you know that to gather the data one of the languages that you have to be really dependent on is SQL structured query language right to interact with the database right now there's a lot of research going on the jai field that for a business person who doesn't know how to have uh or how to write a SQL code or a functional guy Can you give an interface where you can just write a simple query in plain simple English? Give me this, give me the the total sales for the last 3 years and there should be some kind of agent in the background which will convert your natural language into SQL code, fire it in the database and give it give back the result to you, right? So, so those things are also coming into the picture. So, after your data is kind of massaged and stored in a way that it is consumable, right? And there are multiple differences also. So you know we have different types of architectures right we have silver quality data gold quality data bronze quality of data in the entire life cycle the gold quality of data is what finally gets consumed by uh the analyst or the business to kind of make some kind of insight right so then after this stage comes a predictive modeling so prior to predictive modeling there was another thing called as prescriptive modeling or reactive analysis where you know you do some past trend analysis predictive modeling essentially tells you how to project a number in the future. What will be my sales, right? How much of uh customers are going to give away with my brand? They are going to shift to a different brand. Those kind of predictive modeling is something that is done right now. So there are some type of modeling which would require you to kind of predict a number known as regression. There'll be some type of modeling which would require you to kind of predict a class called as a classification classification type of problems. Right? And we use primarily this generative AI, machine learning and uh all these data science algorithms in this particular phase itself in the predic modeling. That's when your skill sets primarily uh on Python comes to picture. Right now the def facto standard for any kind of predling modeling is Python because it is widely accepted and it has come to a stage where more and more contributions are being made to its libraries. any new algorithm comes in somebody would make a commit to the Python's uh scikitlearn package or a new library would be introduced right then comes the genative AI and genative AI right now for everyone like I said genative AI what it is doing right now it is shortening the amount of time that is needed from making sense from the data to the insight right that that's the gra gap the genative AI is actually building up so now a lot of uh uh for an example if somebody is a programmer I don't know if we have some programmers they could relate There are now uh IDE tools being introduced in the market where you write prompts and it generate the Python codes for you or Java codes for you. Now you can just say that I need this basic features and functionalities. Can you just build an application? it will give you some although you have to make some modifications but yes your your uh boilerplate code is ready right now right so that's when the genative AI comes to picture the decision execution is that doesn't lie uh in the scope of a data analyst or in my scope as well right our job is to kind of make insights find out what are the top three insights and those insights doesn't have to be essentially only the insights the insights are qualitative when they are actionable so let's say that these are actionable able insights. If there is a insight but you can't take an action, what is the use of it? Right? And most of the time people you know fail to have this kind of distinction that an insight should be such a way that it should be actionable. Right? So deriving that actionable insight and presenting to the business for informed decision making that is where the you know execution comes to picture. It is not necessarily not necessarily on our head but we have to validate >> and we have to show some confidence in our analysis that yes you can take this decision based on our data. we have some confidence on that right it is not just like a flip of coin where you say that okay I'm 50% hopeful that this thing is going to happen in the future right it's not on that so that is the entire life cycle and that's how uh things moved I I gave some technical glimpses also things move from the data capture to the final decision execution but yes one more thing if you are a leader right earlier you used to have some kind of rudimentary assumptions about your business and that's why you used to take some decisions but now you have to make decision based on the data because the the skeleton or the DNA of the companies are changing you will not find a single company right now who just says that okay I just assume 5% of growth next year and let me have some strategy for that right everybody wants to see what is the forecasted number right so you know irrespective of which stage which industry which role you are in the understanding or having some kind of data literature is mandatory. If you're a functional guy, let's have a high level picture. Nobody is asking to kind of go into depth into coding and all. But if you're a programmer, if you are into application programming, I think it is a high time that you have some knowledge on data engineering or data science or AI to have some siloed example of, you know, one part of the entire data culture. Yeah. >> Uh thank you. That was a wonderful explanation and great insights. uh I'm sure everyone completely understood the process and I think it's also time to ask all our audience if they have have you all used AI data analysis before are you working on data analytics or are you completely new to this field uh so we would love to see where you stand are you someone who is using this do you visualize data do you use any of these tools okay I can see a lot of responses saying new so many of them are new to this okay great completely new five plus years only use chat GPT I'm familiar with AI tools new new new okay I can see a lot of beginners here uh so I think with that I can also talk to you guys about data visualization so like Sidhart explained previously u data visualization is also a key component when it comes to generating insights uh from uh the data that they derive uh so that also brings me uh and that's a part of data analysis. So we have added a few slides on visualizations using dashboards like PowerBI and Tableau in the coming slides. Uh so Sedata when you see something like this uh what real value does it bring to leadership teams because you also mentioned that the leadership you provide the data not entirely make the decision but you always provide accurate data to the leaders that how crucial could this be for someone who's new to the data and how can they impact how can this impact their portfolio or would this benefit them while applying for a job or you know transitioning into a senior leadership role. Right. So a very interesting u perspective. So uh to influence a decision maker in any company right I'm being very straightforward to make to kind of influence any decision maker in any company to take certain action uh you just I mean not just that you need a quality data but you should know how the data is presented right and uh that presentation or that story building aspect is equally important uh as the quality of the data or the accuracy of the data right So gone are the days where things used to be in Excel and people to share you know one Excel file to all the leaders and everything. Somebody makes a changes nobody knows. But now with the help of the data visualization tools coming to picture right uh you have PowerBI and you have Tableau primarily these two are the market leaders. Back in our days we had uh Microsoft reporting services. Somebody would have known known of uh you know Cognos and business object so on and so forth. But the dashboarding and the story building or reporting need is there in the industry and it is going to stay forever. See a typical executive when he starts a day he the first thing out of his company mailbox and you know internet he will open is the particular dashboard that he's entitled to use. A salesperson would go and have a look at the sales dashboard. A supply chain guy would have a look at the supply chain dashboard because see from a ground level perspective like if I am working in a company I have certain roles and responsibilities my performance appraisal is directly dependent on my KPIs. >> Yes. >> How do I track my KPIs? So how a chief executive officer would track his KPIs? How a financial officer CFO would track his KPIs? they will have some kind of a dashboarding right and what they want to focus on in the next 3 to four months is also something that they will get to know based on the current company's culture current companies trend and that information they can get from the dashboards right >> right >> so without having any information on present in a beautiful way you cannot make any justification uh on the decision that you are planning to take right so uh dashboarding and storytelling uh is equally important uh in the entire life cycle and this is the final stage right. So knowing PowerBI or knowing Tableau is very much essential nowadays. >> Yes. Uh thank you for saying that and uh now you would have already understood the importance of having these dashboards. So if you are new for everyone who's new to data analytics. So this is a dashboard uh created by PowerBI and this is also something that you will be learning as a part of the program that we will talk about in the coming slides. And this is another dashboard. Uh this is a Tableau dashboard example. We just wanted to give you a quick insight on what what kind of tools you would be using and what the outcomes would be. How will you be able to visualize the data. So you can just imagine having an Excel sheet with all the details versus having a dashboard where you can explain your insights and like Sedharta said clearly mention um uh you know your decisions with backing or why you are taking the next step. it would be easier to convince or to track your KPIs like he mentioned. Uh so and all the details that you shared uh Sadhhata those were great points and that's something everyone has to keep in mind and we also spoke about predictive you also spoke about predictive analysis earlier in action and how it has changed over time and recently it has become predictive analysis. So keeping uh the interest of time uh we cannot actually show the entire process of how uh we create a predictive analysis but we can definitely show the participants how it would look like. Uh so I'm just moving on to the next slide. So this is uh predictive analytics in action. Uh so uh we can actually train predictive models like you said using Python. Uh so how important are these skills to master especially predictive analytics? You did talk about how every leader looks for wants to see the numbers the outcomes in the coming years. So how crucial is this and how important can this skill be for a beginner or for someone transitioning into data and AI? See this is uh predicting modeling is the core pillar pillar in the entire process right the from the data gathering to the final insight generation or presentation. The most of the effort that will be required uh uh is the data gathering phase and after that you know we have two or three more stages of analysis and all the predictive model building part although the effort that is required is very less in the entire I mean in comparison to the entire life cycle but the the magnitude or the depth of the effort and the skill set that is required there is entirely different right so I let me just try to put things into perspective with the help of SQL you can get or extract data from any database right and uh you know you have to have to have some knowledge on some tools or something uh the entire ETL life cycle but just one or two more tools and you are done. But when it comes to do some kind of predictive analytics although you are going to rely on Python but knowing the details of the algorithms not just learning how to use but what do actually they do behind the scene when you actually execute them knowing those detail is actually important. So for anyone who is looking for uh making a transition to you know data science and once you make a transition to data science the career path goes like from data science uh you know you go to ML right now you don't see many data scientists in the market everybody is ML engineer right so you have ML and now people are like AI engineers and all but that is the career track and uh so going in that career track knowing your basic your fundamentals in the predictive modeling is highly essential for somebody who is a programmer who wants to get his hands dirty on the data. It is and there's a lot of effort that is required but and I will be very honest with you right this is something that you come to get into this field if you actually love it and it is a very interesting field right so the natural interest that you'll get you know learning that okay these kind of use cases are also solvable that gives you uh you know more wanting to kind of learn things in a much more uh deeper way >> right um and I think that's a very crucial aspect also have having the skill of predictive uh analytics like you mentioned. Uh so uh beginners and anyone with experience can also benefit from doing this and you need a lot of knowledge to do this as uh Sata clearly mentioned. Uh so now we have spoken about the kind of skills required the kind of dashboards one would be making and the steps involved in decision making. uh now I would like to know your perspective uh on how this generative AI and data analytics can be applied across business functions and it's not just specifically for data and AI it's spread across industries so uh could you share your insights on that >> yeah um let's let's let's try to pick up one or two more u u realtime use cases right and we we'll see that you know for different roles and responsibilities people carry out in the day-to-day types right so let's say that I am a programmer right my job is to write code to build applications if idea has been given to my team let's say I'm leading a team of Java and back end engineers talking pure technical things right I get an idea to build an application on top of that and launching into the market is let's say takes five or 6 months proper testing and everything being there in the picture with the help of AI that life cycle time has been reduced now to write your boiler plate of code if you just write prompts and give it to charge GP that will generate some codes for you. You take you tweak it and you implement in your application. So something that took months to build can is now possible to kind of build in weeks. Right? Now let's take an example of somebody who is into the uh supply chain industry for that matter. Right? So in supply chain for an example uh I'm talking about a real-time use case that we solved in Kimberly Clark. Right? See OT one time info is a fine that uh you know customers levy on the vendors. We sell our products to Amazon and Walmart. Walmart says that you have to give me all the product at once and within the time if you fail to do so we are going to levi some uh fines on you. You guys would be surprised to know do you know how much of revenue Walmart gets from finding his vendors just as vendors is close to 50 million. 50 million from one single vendor like from us it takes from Kimble it goes around like like that right so uh now a supply chain guy would be interested to know what are the ways I can reduce these OT fines how can I do that right so how can I ensure that all the orders are being uh delivered uh at one go without any partial deliveries and within the time how should I assem you know how should I kind of do some optimizations in my assembly line a finance guy would be interested interested to know how the portfolios are doing. Uh how the key metrics of the health figures of a company is doing right let's let's take case of a finance in finance you have like 50 60 different metrics ratios this that right and uh a finance guy day in day out would be kind of trying to decipher the meaning and you know create a story and correlate the things from all the financial dashboards and everything. What if we could build a AI agent that kind of simplifies that particular task for you? Right? So the amount of effort that is required for a finance guy to make sense of his financial data that used to take one or two hours can be quickly given as insight in capsules like something that he can decipher in 10 to 15 minutes. So irrespective of which position you are in AI is going to be a way of working going forward. Let me put it that way. Let's not just say that it is a skill to be developed. it is going to be way uh of working together like uh that that's the going that's the way going forward. So similarly in the uh you know manufacturing let's say I can give an example people try to find out given a product that has come out of the assembly line is the product faulty or it is not faulty like so for an example in lot of companies there's a lot of wastageers because of some packaging issues before it reaches the customer can you identify those things so that you will save cost from the logistic piece that you that you actually pay for you know return and new deliveries right so like in Myntra something you get a package let's say mobile but the mobile uh packages has been tampered with what you do you request for a return and you know a new delivery but the company had known some way from the manufacturing from the point where it is being assembled you know if from a let's you know you take a simple case like there is a conveyor belt and all the mobiles are passing and you have a camera fit which is tracking the different the angles of a particular package this part of uh the application is called as computer vision and it can detect from be packages pictures that the package has been tampered with and if you catch hold of that you have lesser burden to deal with now customer is not going to have a defect product or a tampered product right this is a real-time use cases of how people make decisions with the help of AI and machine learning in different parts of the industry and different verticals >> that's great insights and all the real world applications are really informative as well and these are some things that we come across regularly and now when you put all of this in context we also have an idea on how we can apply this across industry. So whatever role that our participants anyone join who joined this webinar is in you can apply it in whatever field you are in uh like let it be marketing manufacturing whatever decisions that you make whatever strategies that you come up with data and AI can definitely back you up on those decisions and that is such a stronghold that you can have in your first job or while transitioning. So that is something that you have to keep in mind. And we also have a few queries and questions uh regarding the roles and growth paths that are opening up. Now that we spoke about how the industry is growing, how more data is coming into place, could you share your thoughts on what are some of the new career outcomes and the roles that would come up in the coming years in this field? >> Right. Um okay, I'm kind of give my answer slightly broadly. Right. Yeah. >> We talked about the amount of data that the world is going to generate. So there will be two type of people with kind of skill set will be needed. There will be a particular skill set that is needed uh that is required to kind of collecting the data and storing the data and moving the data from one place to another. That essential part comes into the data engineering that is a different skill track but that is also equally important. The second part of uh the roles that you'll see which will have a boom in the future is the uh the part where you make sales of the data and we're talking about data analyst, data scientist, business intelligence analyst, machine learning engineer, right? Um I feel I should give a caveat. I think lot of the people who have joined here they would not know the difference between a data analyst, BI analyst, data scientist, financial analyst, right? They will not have. So let me just give a clear picture, right? And see the first role in any data uh analysis or post data collection type of work is a data analyst right data has been collected but the first task is that of data analyst. Data analyst task is to do data cleaning activities generating you know just in time or just in time required kind of insights by writing some quick code and preparing basic dashboards and everything. From the data analyst you you know then go for a business intelligence intelligence specialization where you get hold of a tool like a Tableau or a PowerBI these two are hot in the market and your job that you just don't try to decipher the data but you also create a story around it and you create a visualization how this story can be presented to the stakeholders that is the work of a business business intelligence analyst. Then comes the next stage is the data scientist right where you just don't know how to make sense of the data but you know what kind of predictions what kind of classifications that you can do right and uh you know how to apply these algorithms and typically data analyst they perform the analysis in a very siloed part right so let's say I'll give an example um and here in this example you'll see the difference between a data uh scientist and a machine learning engineer Right? So all of guys in the phone right so let's say if you if you have iPhone right it catches your facial recognition and unlocks itself right I have heard I I don't have iPhone uh but the thing is that the first model that was supposed to be built building a machine learning model that can instantly find out the facial gestures and correctly identify or probabilistically say that okay this is this person this is Siddhar this is Suhar that model will be built by a typical machine learning and you know data scientist or machine learning guy right but taking that algorithm and integrating within the device itself that's where the machine learning engineers come right so a data analyst makes typical uh oneliner kind of analysis from the data one or two liner kind of analysis from the data BI analyst has the skills of a data analyst but knows BI as well guys when you see this slide just don't think that these these are like different people in a team This is how your carrier role is going to be. If you are a fresher um if you're lucky you will get a initial role as a data scientist or normally what happens in the industry they take you as a data scientist but you are made to do more of data analyst work. Nobody's going to give you a task of building the models from the day one. That is how the career track is going to be right then you transition into BI then you get into data science. Pinel data analyst here is kind of a functional role right it's not highly technical I would say data visualization engineer and data or business intelligence analyst that both have lot of uh you know overlapping so a business intelligence analyst just deals with the front end data visualization engineer says that okay give me the data collecting the data from the database to the final front end visualization preparation everything something that I know I don't need extra hands that is what the data visualization engineer says Right. And uh you can see the average salary in the industry and these are mostly with 2 to three years of experience in any tier one cities Mumbai, Bangalore, Hyderabad, Delhi you will get in any company right now. So any data oriented profile job is going to be the highest paid job in the market and having said that in the entire IT industry the top two most highest paid in the jobs are AI and data engineers. If you would have known right now Meta and open a there are kind of they have scrambled the entire space. Did you heard of Alexander Wong? One person who was hired by Meta with $60 billion of package with his entire team. A Indian guy III Gojhati guy I think he got close to 400 or$500 million for his first job. Although you know there are a lot of riders into that guys. This is the amount Ronaldo used to get paid when he transitioned to Manchester United few years back. So you can imagine first time in the industry the industry is saying that if in this field of AI you are a guy who knows the things if you can prove you know you come and we'll give you that money it was never there nobody asked for this kind of money for a Java programmer right or a data scientist in the earlier days but now things are changing right so you can see you know where you actually stand and see the that's why I said the opportunity there is no upper limit to this right now 1 billion 2 billion people are getting placed although I'm not saying that all of us will be gu placed with that amount I pray we do so but you know the opportunity is massive >> that thank you for uh sharing that and I think now everyone has a comprehensive understanding of what is the difference between all the roles mentioned and how one can move from one uh role to the other and also the opportunities out there like you clearly mentioned there are ample opportunities it's just that you need the kind of knowledge to implement it and it's not just theory like it was earlier it's always it's all hands-on applications as well and that also uh brings me to a question that was shared uh so um this was shared by Kavita so she was asking uh while agents assist in decision making uh what impact does it have in the job sector for someone in data analytics and uh geni Um but uh I just want to ask uh Kavita I guess right uh >> but could you frame your question could you say what is your sentiment behind asking this are you referring to some kind of security aspect with respect to job if you don't know AI on that angle the moment you say impact could you qualify that with another quality let's say is it more about job security that you're asking ask >> yeah she's talking about job security >> yes see guys Um the reason AI is taking time to get adapted in the companies is because of the narrative that is being set currently that AI is here to take your jobs. Okay, these are some of the ugly facts of the industry. I'm telling very bluntly in front of you right the big 10 companies who are coming out with all these models right open a and all they are saying that you know it is going to replace the programmers it is not going to replace the programmers these this kind of narrative is being said that because you know companies will try to buy more of their AI products the narrative which is actually going to stay in the future and if something that is going to be there in the future it is not a narrative it is the truth AI is not here to replace you AI is here to coexist with you. You will be replaced not by AI. You will be replaced because you don't know AI. You will not be replaced by an AI agent. There are some type of jobs which will be replaced. never further out. See, um I'm being very honest. Customer support will be entirely replaced because the large language models you can use the large language models build question and answer uh Q&A bots, agents based on your company's uh private data also you can train them uh and uh you can build. So those jobs are going to be absolute I can say straightforward but lot of other jobs they are going to stay. A programmer's job is not going to uh go away, right? But you should it will your job will be in danger if you don't know how to use AI. Like I said I gave an example a typical programmer if I give you a small feature to build in Java you will take let's say I'll text let's say two weeks there comes another guy who knows how to use this AI enabled ids you have cursor right and visual studio also uh you can write uh you know you can do lot of work very easily with help of AI related features. He says I can take help of AI and I can build this feature in one week. So which of the guys should I keep in my team? You tell me. So did you lose your job because you don't know AI? No, you lost your job because you don't know how to use AI, how to cope up with AI. And it is the same not just for a programming guy. It is the same for any person for that matter. A design person, let's say. If I want you to design something in Photoshop to design a basic poster in Photoshop, you might take an entire day. Another person comes and hind says you know I know how to use Google Gemini and I can just I can I know how to write exactly the prompt and generate image and I can do some basic on that. So to generate his basic template he will take only 10 minutes changes modification another 40 45 minute done. So whichever the person is going to lose the job and what is the reason did you lose the job because of AI because of Gemini is there? No you didn't learn it. So it is irrespective of the IT industry guys. This is the matter of uh it it is it applies to all walks of life. If any work of your life you have a trend that is coming up and which is going to stay as a mandatory aspect of a like a skill set you have to write the train. You just can't stay as it said and say that okay I don't know AI my job got laid off and I am a victim. No, you have to learn it right. So AI is here to coexist with you and all the news you are seeing in the market like you know AIS will take your jobs and everything this the these things this fear-mongering is done by the top big tech companies in the industry because their products will sell. If this kind of narratives is not set you will not see that okay this particular concept has that much of power or magnitude. Don't get carried away with that. It's your job to learn AI. you can coexist with where AI is here to stay. >> Right? Thank you for that answer and I think that is a question that was always asked and that is something that a lot of people worry about just being replaced by AI. Whenever we talk about a course as well, they're like would my job go away if AI replaces me. And I think you gave a complete understanding of what the difference is a person who uses AI and one who does not and how the market is going to uh react differently to both the candidates. Uh so thank you for that and we also u I can see a lot of questions in the chat. Unfortunately in the interest of time we will be taking this up towards the end. So please save your questions till the end of the webinar. And uh that brings me to another uh question. Um a lot of uh people in this webinar are quite new and they would like to know how they can get into this data analytics and generative AI. What are some tips that you can share? What are some important aspects that they must mandatory cover? Right. Uh see uh the types of skills you need in this entire career uh with respect to the tools and languages is not like very diverse, right? Not not extremely diverse. So the basic thing that you need to learn is SQL. Then you need to have good understanding on Python. uh and then you need uh PowerBI or Tableau kind of a thing to kind of do some visualizations right and all the machine learning model development all the predictive modeling is done by Python um so Python is covered SQL is covered and power and tab so the basic skill that is required this much would do right because this covers your entire work from data collection to the data presentation everything there are alternate tools and everything there are certain uh like you can replace Python with maybe R, right? Uh you can have uh you know uh Java guy would say that okay I can build Java based dashboards using JavaScript instead of PowerB and Tableau. But these things are the quick kills. You can build things in PowerB and Tableau uh in one tenth of a time the Java guy can do right these tools have that much of input. Now with respect to your learning skills and everything guys I have a different uh mindset know completely all together. The thing is you will learn something better if you really like that thing. If somebody from you doesn't like finance at all, irrespective of how much effort I can put in, you'll not be able to learn finance properly. Now the liking of a particular subject or particular area comes from a different type of aspect and it is about your familiarity. Right now for me, Python is like gibberish to me because I'm not familiar with it. Uh I saw somebody is a psychologist. Uh from the chat I saw from one of the uh uh I don't know the person she's a psychologist right for a psychologist person uh programming is a completely alienated thing. Why? Because there is no familiarity. How could you get familiar? Only if you take some baby steps. So start with your baby steps. Bring some familiarity. That familiarity will generate interest. You will kind of take away time intentionally. Not just because you are doing a course but because you want to learn and there is a natural inclination and you study well right so I think that is something that those baby steps is something that you have to do and those baby steps start from today's webinar because you get end to end perspective of how things are done and that will generate some interest in you know the the uh the industry trend and you have a very good idea about what things are working what are the skill set that is required most importantly uh pun intended but you know what is the commercial aspect of it right how much I can earn right all these things put together will have some kind of propelling uh uh interest in you and you'll start learning so that that that's the way going forward I'm saying that this is I talk very candidly uh I hope I resonate with all of you >> yes that that was great and uh you did cover the basic skills that are required and uh that also brings me uh to a major part of today's webinar that is the program that we offer in data analytics and generative AI. Uh are you all interested to know more about it? It would be great if you could respond in the chat. Are you all still with us? Yes. Okay. All right. Uh so I won't take much of your time. I will clearly mention everything that you've asked. What are the skills that are required? How can you learn? How can you get an internship? Uh what are the projects? What are the skills that people are looking for when they are hiring? So uh without taking much of your time I will quickly take you through the program that we are offering at simply learn. So uh with uh so this program is in collaboration with ENICT Academy IIT Guhati. So uh the credential itself speaks for itself. So IIT Guahhati has a great reputation in the market. So you will be mastering data analytics and generative applications uh with uh in demand skills and this will be covered by the faculty of IIT Gujarati and also our mentors will be a part of the program. However, you will have mentors from the faculty it directly from the faculty itself. Uh so that's the second point. You will be learning directly from them. You will be covering all the concepts from the very basic to advanced concepts to all the dashboards that we mentioned previously. um and hands-on learning like like Sedhhata mentioned throughout the webinar. It's about how how much of it can you implement, how much change can you bring in and what kind of credentials can you show. Uh so that is something that we will be covering in the program and I will also talk to you about the certifications and campus experience that you will have as a part of the program. Uh so uh this is a very important slide for us because we you can see on screen clearly the kind of tools that are covering. Uh when I asked you all about the kind of experience that you have in data analytics I saw a lot of people mention Excel. So with Excel we will be starting from basics uh to advanced we will be covering all of the aspects and all the formulas that you might need with Excel. uh then like you can see on screen we will cover Python, R uh pandas, Tableau, PowerBI, chat GPT, Gemini, Invido, Wizard, Xavier and many more. Uh so these are some of the tools that are mentioned and the skills that we will be covering as a part of program of this program is firstly generative AI. Then we move on to prompt engineering. So we discussed how AI is changing things, how a prompt can help you create programs, uh analyze reports. So prompt engineering data analytics with Python. Uh so we saw how predict predictive models are uh play in that how Python plays a very crucial role. Then we will also cover data analytics using our data visualization with Tableau and PowerBI. You saw the dashboards. So like I mentioned earlier, you will be going from an Excel sheet to creating that dashboard and these dashboards are really interactive. So if you want to see the data of a certain month, if you want to see the data of a certain year, you can segment it, you can visualize the data in how whatever format that you require. Then we will be covering extract, transform and load. Uh statistical an analysis using Excel again uh SQL uh like Sudharta mentioned initially on call. Uh we will be building data pipelines then different forms of analysis like regression analysis, time series analysis, supervised learning and unsupervised learning. So if you're new to all of these terms, these are this is all this covers almost everything in data analytics uh and generative AI and we will we can assure you that we start from the basics. So you will have a comprehensive understanding and these are the skills that you will be able to mention in your resume. So I can see a lot of freshers here uh who are interested in this program. So you can go from not having much of an experience to actually showing pro projects and work that you have done using these tools. So you will I uh and that brings me to the next slide on how you can you know show your experience. So you will be working on hands-on projects as a part of this program. So if you look at the screen you can see four of the projects that we will be working on. There are multiple there are 25 plus uh projects. These are some capstone projects, four capstone projects. So firstly, you will be building a virtual assistant with generative AI. So imagine being able to create a conversational chatbot. The use cases are multiple. It's like solving a problem for a company. If you are currently working as well in whatever field, if you can uh create a conversational chatbot, add that to your resume, the value that it adds is a lot. Then we will be creating a crime analysis uh dashboard. So this will be primarily using Tableau uh to update polers on crime. So it's just an example. So you will actually be able to showcase a sample of the work that you have done. Uh a dashboard that we had shared earlier as an example. You will be able to make an interactive one, get insights from it and also add storytelling to it. You will know how to create it ground up and this will be a part of your work. U later on you will be working on an e-commerce application. So you will be developing an e-commerce app using Python and integrating features and c uh like categorization payment options. So you will be comping some of the uh uh in you will have an in-depth understanding on this application as well. Then uh this uh the next one is a part of data analysis. So your project will be to create a flight delay analysis report. So analyze and visualize flight delays uh depending on the previous data. This is this is more like predictive analysis as well. So imagine being able to do this in your work. Uh you can analyze uh the sale you can have a sales analysis report leads analysis. There are multiple reports that you can generate multiple analysis you can come up with. Once you actually do this by yourself and uh when you are doing the any of these projects you will also have the support of our team and great feedback from the faculty as well. Um uh so you are not going to do this alone. We have mentors, we have cohort managers and we and you will be a part of a slack community as well. So you will be completely guided. Anytime you have a problem, anytime you face a roadblock, our team will be there to support you. Then I will take you through the learning path that we have at simply learn uh along with uh IIT Goahhati's program. Uh so this is what a lot of people were asking what are the skills that are required to transition into this field. If you can look at the screen these are all the skills that are required. So we have covered it from basic to advanced. So first you will analyze data summarize trends and create dashboards. Post that you will be m mastering querying filtering transforming data sets to making decisions. So this is also covered in the skills that I mentioned. Then you will go ahead and make data pipelines, integration workflows and you will also decide interactive dashboards. That's the one that we spoke about PowerBI and Tableau like Sedharta mentioned are the top trending ones. Uh then you will also brush upon Python fundamentals and explore AI applications. So generative AI is something that we com you know cover as a module. Uh you will be able to apply statistical methods and build predictive models. Again we showed you an example. You currently do not know how it is done but towards the end of the program you will be able to do it completely by yourself. And uh the final module you will learn how to use Gen AI for automation insight generation and business intelligence. So this is everything that is going to be covered and from not knowing the basics or having a very basic understanding this is how much you can learn in 11 months and this is something that is going to be supported by our faculty. So you can uh because the faculty is directly from IIT Goahhati and also our mentors with a lot of industry experience they will be able to assist you in understanding real life applications as well. Uh so Siddhhat I have a question to you. Looking at the curriculum uh and the learning path that we offer at Simply Learn, how effective could this be for someone transitioning and what are some of the modules that they should probably focus on more when compared to others? >> Right. Um I have gone through uh the entire syllabus and uh I have seen like you know the learning path and everything. So I haven't found anything that is missing from here to be honest. uh the path is absolutely perfect. That is how a chronological order for learning these kind of concepts should be. uh the some of the materials are actually explained in depth if you see the syllabus and all uh and the type of tools and uh uh the different concepts and technologies that is mentioned >> uh the previous slide that you showed starting from pandas to you know gemini right so everything is kind of covered every most of the things are like the the top skill sets in the industry right now so it's a all in all it's a good package yeah >> thank you for sh saying that so now you understand uh how much of it that we have covered in this course. This has a lot of people have put in their insights, real world applications to generate uh this entire framework and curriculum for you. And uh you uh there are a lot of questions about the course fees. I will come to that in the coming slides. If you can give me two to three minutes, we will be talking about that as well. Um I will just quickly cover the delivery mode how these sessions are going to happen. So these are instructor-led sessions and these are 100% live virtual sessions with IIT Gojhati faculty and ICT academy experts and industry leaders like I said um so uh it is interactive your sessions are going to be you will be able to ask questions to your uh mentors uh you will be able to interact with your peers that is something that we value here at Simply uh so you will be learning with uh practical applications case studies peer collaboration project based learning This is our focus. We do not want you to be focused on theory. We want you to actually have uh all the projects to show the work that you have done. So when you think about employability after a certification as well this is not just a certification. You have a proof of work and that is what will get you ahead in any kind of job that you might apply. Uh so I'll quickly take you through the learning experience that we offer. Like I mentioned there is peer-to-peer engagement. and also engagement with the menus. You will be added to a slack community. So you will be getting guidance support at all times. Uh flexarn option is also available. So in case you miss a class, we all know that sometimes you can't help it. Things come up and you miss out on classes. So these are recorded and uh you will be able to attend the class uh post the session as well. Uh mentoring sessions are specifically provided because especially when you're new to a field, you need guidance. So expert guidance sessions from mentors for the doubts that you may have project assistance. So you are doing a project but you're facing difficulties are our mentors will be there to support you. Uh the learning support like I mentioned earlier there's a cohort manager. So if you have queries related to your course uh if you have any other roadblocks while doing the course with us you can just directly reach out to the cohort manager and he or she will be able to assist you and resolve your queries at the earliest. uh then talking about career support. So just having a certific after completing a certification the next thing that everybody worries about is the kind of job you would land uh what kind of career growth can I get. So to ensure that you land a very good role uh what we do is that we provide group mentoring and networking sessions. So you will get the opportunity to collaborate with your uh peers and also the industry mentors. So because this is also in collaboration with IIT Gojhati, you will get their alumini status as well. So this will add to your network. That is the kind of network you will be interacting with. You will get into more opportunities. You will know what's trending in the field and you will exactly know how to tailor your resumeuming to the job requirements. Then we have interview prep and assessment. So once you complete all of the modules, work on your projects. Our only focus is that you need to be uh invested in the course, you need to be dedicated and as long as you attend all the live sessions, uh we will help you prepare for the interview. Uh and with that, we will be giving you mock assessment to boost your performance, we will give you feedback on your performance to ensure that you clear your interviews in the very uh your all your interviews as well. Apart from that we provide AI powered profile optimization. So in today's time we all know that a lot of jobs are uh you know through LinkedIn and LinkedIn profile optimization is very crucial. So our team has actually extensively worked on uh creating an AI provide uh powered profile optimization. So by the time you complete this course in 11 months you're going to have multiple certifications from such reputed institutions and also completely optimized uh profile. And the next step that you need to be careful about is your mock interview and mentoring because in case you are new to data and AI like we mentioned earlier, you would not be aware of all the questions that can come up. So we have industry experts who will help you practice real world interview. You will be sitting uh and answering their questions like a real interview. The kind of questions that you might encounter uh and you will also receive feedback on all of this. So you will get the chance to correct anything that is not right and sharpen your skills and come back. Right? So by the time you are done with the course, you will have all of these skills and then you will be ready to transition into that new role. Even if you're a fresher, even if you're transitioning or you currently have a job as well. Uh so I will take you to the eligibility criteria. This is also something that uh that was asked. So the only criteria we have is that you have you must have a bachelor's degree and an average of 50% or higher marks. You don't need prior work experience and we have opened this up for uh freshers as well because this is a great career opportunity. Um, and you do not need any prior coding experience or technology know-how because like I showed you previously, our curriculum covers everything from the very basics to the advanced uh level and we have a great team to support you with that. Um, and uh the kind of certifications that you would be receiving are like you can see on screen. Uh, first you will receive a co-branded program completion certificate from ENICT Academy, IIT Guahhati and also Simply Run. So that's going to be one of your certificates. The second certificate is from um IBM and IBM will also offer you at IBM digital badge. So there are some modules offered by IBM. Once you complete that, you will receive that as well. Uh and like I mentioned earlier, you will also gain executive alumni status from IIT Gujarati for networking opportunities and you also have campus emotion for 3 days. So that's something that is covered. So this is just metrics that you can see on the screen. why professional certifications matter especially when hiring when you're new to a role. So when we we did some statistics and we realized that the average salary increase for a person with a certification is 3.2 times that of someone without a certification. your credentials do matter especially in MNC's they do consider these options right and um we have also seen that there's 68% of certified professionals advance their careers like they move to senior leadership roles within 18 months um and employers 89% of the employers prefer uh candidates with valid certifications because we know that multiple um you know there are so many companies offering you courses but the kind of credential that you add to your portfolio or your profile plays a very important role and like we discussed before we have a lot of job opportunities so it's estimated to grow over 2.1 million data analytics and genai positions uh in the coming years as well and to give you proof of how our experience is going to be at simply learn if you see on screen we have Akash Raymond so he completed this course with us and he got placed as a psy cyber security analyst level and he got a 40% increase in his salary and then there was Sai Naven uh so he landed his very first job after completing this uh certification with us so that also does speak about the kind of experience that we provide you can definitely go ahead check out our reviews check out what people talk about us and you know the impact of the job role as well uh so this is just data analytics in the news the number of jobs that are being um coming up in the market So I'll take you to when the most exciting part. So when does the next batch begin? Actually uh we are very close to the application close date. Uh the application closes on 31st of October for the upcoming cohort and the classes will ideally start on 22nd of November and it will go on till 4th of October 2026. Uh the induction for this session is on 3rd of November. So we are like very short of time. uh we already have closed on a lot of applications and I think there are 11 to 12 slots left for the for this cohort. So um so now I'll take you through the course fees something that everyone was interested in. So the program fee for this is 1 lak 19,900 and you will have the option to pay this in installments. Please keep in mind that this is a 11 month course and we are offering a lot of opportunities with this course and something that a lot of our competitors do not offer. Uh right. Um but how do you enroll if you are interested? Um the steps are very simple. You will have to submit your application and uh how do you do that? I will be sharing a poll in the in sometime. So if you click on a yes, our program advisor will be reaching out to you and like I mentioned because there are very few uh slots left you will also have to we also shortlist candidates because this is in collaboration with IIT Kohhati right. Uh so uh the criteria is the same but shortlisting of the candidates will be done by the um admissions team. So they will reach out to you and uh the selected candidates will be given an update from the team. I think in this case they will be reaching out to you very shortly like by tomorrow itself because the cohort starts very soon. Um and the selected candidates will begin the program within one u I mean uh within one to two weeks right. So I am quickly going to launch a poll for all of you. If you are interested, if you have any questions about the program, uh please go ahead and click on a yes and our team will reach out to you. Uh and they will be ideally reaching out to you tomorrow itself. uh so this will be quite a quick process and if you have any questions regarding the course any doubts that you have they will be there to assist you and I'm also going to share the uh the program link along with the webinar certificate so you can take a uh look at the course curriculum the advisor of the program and everything so just give me a minute I'm also okay so I'm getting a lot of uh responses and if you have any questions in the in the meantime please ask us in the Q&A box we will be more than happy to answer your questions in the meantime is this offered in the US yes you can take up this course even if you are in the US uh if you click on a yes our team will reach out to you regarding that and I see a lot of quest questions about the certificate we will be sharing a form in some time uh we are just going to answer a few questions in this meantime as well. So, Shata, one of the questions that we have is what is the major difference between Tableau and PowerBI? Uh there is say uh Tableau came up with a particular built-in language called as visual quering editor. Visual quering language VQL VQL yes visual quering language. So, that is something that that happened for the first time. uh the PowerBI's the back end code and everything I think mostly it is net right so uh you'll find a little bit of difference with respect to uh the data transfer latency but net they both are equally good uh when it comes to the ease of building I would prefer Tableau uh with respect to the ease of building right and power uh if Tableau with respect to ease of building in a scale develop 0 to 10 is at 9 and power will be at 8 and a half and all right so not much of a difference that way >> okay got it um I hope that answers your question uh the name was not mentioned so um there there's another question um yeah so do you foresee any negative impact of using AI I think you did give an answer of how AI is not going to replace jobs but do you foresee any negative impact Right. >> It is happening right now in the society, isn't it? >> It's happening right now. So if you'd have seen very recently a person I think from Gujarat state in India, he took a selfie of himself sitting on a balcony and he asked a I think some AI editor to kind of put a leopard that is kind of roaming in his lane and he posted it online that create a massive uh issue and he got arrested. Right? It is about how you use AI, >> right? It is not that AI is good or bad. It is how we look at the different things how we use it. So a great sense of responsibility also drawn upon us and uh it is not just about using AI. It is about using AI responsibly. That is the only downside effect of AI. Unless and until with intention people do it to kind of uh see right now uh uh mostly like in in the sector of pornographic and all there is a lot of use of AI right morphed images a lot of cyber crimes are being reported but it is with respect to the usage it is not with respect to learning it is not with respect to learning it is with respect to the usage of AI >> right uh thank you for sharing that uh uh I can also see a few more questions so for anyone who uh some people have also asked the course link. So I have shared that in the chat. I shared that previously as well. So I am ending the poll for your interest. So for everyone who has opted for an for a yes, our team will be reaching out to you. So please share your profile with them. They will review it and get back to you. Uh I will also quickly share a poll for certificates while answering your questions. Uh so um the next question we have is um how do you prepare for these roles as a fresher in data for prepare for jobs in MNC's as a fresher in data analytics >> right so pick up two or three things very quickly SQL will take you a month's time to have some kind of confidence and to be able to answer in interviews Python basics will take you another two months one or two months and I'm saying from the classroom apart from like you know I have to make some of my own studies also some practice uh one or two visualation tool either tableau or power by you don't have to learn both right and uh a little bit of statistics and all you know basic uh statistical concept like um standard deviation or what is a bell curve uh what is the six sigma concept right all these things that's much for a beginner to get started it it it's not rocket science it gets into rocket science when you get to the the research oriented profiles, machine learning research oriented profiles but it is not a rocket science >> right uh thank you for that and I hope that clearly answers uh the question uh so um there's another question and this came up pretty early during the webinar uh what is the future of a data scientist in 2030 what are your thoughts on this >> data scientist position is going to be obsolete nobody will hire a data scientist in 2030 why because the designations are changing Right. >> Right. See early the industry had MIS MIS engineers that became business intelligence engineers because fancy tools came to the market. >> So then we had like statisticians in the years of 2008 to 10 same people started doing and the term got coined as data scientist >> then machine learning engineering so on and so forth. Now the crucial thing here is to know it is not that the data scientist position will be obsolete in a typical sense but it is what is expected from a position of a data scientist those expectations will change the amount of skill set and everything that is required and you know that is actually changing see earlier uh in our days like I'm talking about 2014 or 15 when we used to work the data scientists were the elite people in a team the most elite right and everybody will kind of get the data and you know make the things ready for them then they'll just apply the algorithms they'll give some insight and that's it but nowadays people are expecting the data scientist to know some part of data engineering as well SQL and all these things that's why they are being taught in these courses a typical data scientist in his self-esteem will not do any visualization work I will also not do any visualization work I'll get it done by my team members right uh but right now the demand from a person who is entering into data science and machine learning is increasing it it is not enough that you know algorithms and everything you should know SQL you should be self separation is some of the things right so those expectations will change so in 2030 more expectations will be required from the data scientist why because they will have more bandwidth again why because whatever they are continuing to do in today that takes more time the way industry is changing it will take lesser time with the upcoming tools and technologies so people have more bandwidth and if you have more bandwidth then you will have to have more and more secondary skills right so that's how it is going to change >> thank you for that and I hope that also clarifies the question we had um I saw a few questions about the certificate poll the poll is still live for the certificate so you can go ahead and fill in your full names you will be receiving the certificate within 24 hours along with the recording and the slide deck um and for uh some of the people who asked for the previous poll on interest I'm sharing the uh course link again so you you can just go there and look at the uh what the course offers and there will be an option to enroll. Even if you click on that the team will reach out to you. So you do not if you have missed out on that poll that is completely all right. Uh so I'll give you one more um minute uh for you guys to fill up the certificate poll. In case you missed out on that poll, you can mail us at webinars@simplearn.net. We will still share a certificate with you. um your certificate will be available on your we'll send out emails for all of you. Um so yeah even for users who are not who have not logged into the session they will not be receiving a certificate. So yeah please go ahead and fill in your responses. So with that I think uh with the interest of time uh unfortunately we will not be able to take up any more sessions uh I mean any more questions uh so if you have any other queries we will be uh taking that taking that up in the upcoming sessions um and also I would like to take this time to thank uh Siddata for being such a wonderful speaker for sharing all of his experiences and insights. This was completely uh wonderful having you. I I enjoyed hosting you in this webinar and as we wrap up this qu session I would like to ask you a question. So what is one advice that you would like to share with our audience today? What's one thing that they should always keep in mind? >> Stay childish, stay foolish. I mean that is great advice and I hope yes your the hunger for learning comes from being childish and the learning starts >> uh from accepting that I don't know something everybody misses that right so have a natural uh inclination uh to learn and I would suggest one thing don't just come into this field only because you want a greater salary >> right >> okay because this industry is something that keeps evolving And if you have a genuine interest and you are willing to evolve along with this data science and ML industry, this AI industry, if you're willing to put that much of effort, then come. So it do it for because you love doing that, not because it is gives you get good salary. That's what I would say. >> Yeah. Thank you. That was great advice. Thank you so much. And I hope all of you also keep that in mind. uh always follow the field that you are interested in and follow your passion to a great extent and stay s childish like you mentioned. Uh so um I would also like to thank all of you for joining in. You have been a wonderful audience today. A great uh engagement, great responses. Thank you for staying till the end of the session. If you have any feedbacks, you can mail us at webinars@simplearn.net. If you want us to host more webinars on different subjects, we are more than happy to hear your thoughts. We will try to send you a feedback form as well so you can share your experience with us. So uh have a wonderful day or night ahead everybody. Uh and I wish you all uh best of luck. If you are joining the course consider it. Um I have also shared the course link with all of you. So I hope everyone got an opportunity to take a look at it. Certificates the link is closed but if you missed uh on filling it up you can mail us at webinars@simplearn.net and we will be happy to assist you. Uh so with that we've come to an end uh for the session. It was wonderful having you all. Thank you everybody and I look forward to seeing you all really soon.