 Okay, I'm going live now. Hello everyone, and welcome back to Sanjay Gupta Tech School. I'm Sanjay Gupta, your host. And today, we have a very special guest with us, who is an expert in the field of data science and AI. And she has an incredible background and have worked on some ground-breaking projects in the industry, right? So let's dive into the world of data-driven insights and artificial intelligence. So please join me in welcoming Sonal. So thank you, Sonal, for spending some time and being with us. And like before starting the session and Q&As, I just want you to introduce yourself in front of the audience so that they know your journey and what you are doing actually in the industry. Absolutely, thank you so much, Sanjay. That's such a pleasure to meet you after so long and exchange some good pleasantries and a little bit about myself. So I am Sonal Sekri. I have been working in this field for more than seven years now. For about seven and a half years, I have been with Turtle. That's my current organization as an analytics leader. And my journey started right around my undergraduate and because of my, I would say, understanding and the zeal towards data, you know, that was something that really helped me grow into this field. So right after my undergrad, which was also undergrad, I worked for a couple of years and then I did my master's. And during my master's, I did my specialization in machine learning and that's when I decided that, you know, data science is the field where I wanna step in. Artificial intelligence, I would say it's very recent, right? It wasn't around seven years back. It is very recent, but then I believe both goes hand in hand. Yeah, absolutely. So once again, I welcome you on this platform and like students and experienced consultants are very much interested to know about the future of this data science and AI because they are hearing very much about these in Salesforce as well. So like on this channel, I have created lots of content on Salesforce. So Salesforce enthusiasts also like following me and learning lots of things. And right now, like Salesforce is also promoting data cloud, AI cloud. So I think our discussion will help them as well, learning these technologies and key concepts about them, right? Okay, so starting with the Q and A session now. So I will be asking some questions to you so that you can guide audience like what these things are and how they can shape their career in the field of data science and AI, right? So starting with the first question, like data science and AI are buzzwords we often hear, but they can be quite intimidating, right? Could you provide a simple explanation like what they really mean, what is data science actually and what is AI actually? Okay, so if I have to put in simple words, Sanjay, I would say data science is all about extracting the valuable insights from data. Now we may start from collecting the data, cleaning and analyzing in order to uncover any patterns or trends and these can be insights that can be used to create informed decisions, solve problems or even to improve the process. Now that's data science. So data science, if I have to say again, I would say just two words, extract valuable insights from data. Now if I'm talking about artificial intelligence, now artificial intelligence is a system that we as human are creating and it is to recognize the patterns within the data, make predictions and it may also include sometimes understanding the natural language or learning from experience. So it's nothing to be afraid about as many people get afraid about, oh, this is AI, this is new and it's gonna take away jobs as a very common notion that is, but we have to understand and have a grasp of one thing, that it is a system, it's a software that we ourselves are creating. It is to simplify our lives, it is to simplify the mundane work that we are doing and these are the works that typically require human intelligence or recognizing the patterns, I would say. Right, so after understanding both the things, I just want to know what inspired you to pursue a career in this fascinating field? I would say that overall my interest with data and then as you are working and going forward, you understand that there are profound impacts of delving into the data and becoming a data-driven person. So if I have to give you an example, we all shop from Amazon, right? One thing is you just go and do your shopping and the other thing is you come back, you take your data, understand your data and then there are so many APIs that are available or you can just download a CSV file and analyze your data and see, okay, where am I shopping more, right? Where is my credit card statement going towards? And that's the power of data. That's what it tells you something that is a bad habit for you to spend or something like that you should be spending towards. So doing something versus analyzing something gives you a power of doing things the right way. So I think that's a practical example that I gave you, but the same thing is applicable within me. I'm a very data and I would say a very analytical person though I'm also creative, but then it comes with the nature I believe where I feel like, okay, there are so many good things that are being done and can be done if we continue to analyze data and work more with the data. Absolutely. So I think like lots of folks who are currently in BTEC are like struggling to find some jobs. So they will also get inspired with this, like if they have any analytical skills, so they can also go in this field, right? So as we all know, the tech landscape is constantly evolving. How do you manage to stay up to date with the latest trends and technologies in data science and AI? I try to read as much as possible. So there are a few things that we all need to follow. One thing is continuous learning. So whether you are working, whether you are studying, there should be a portion of your week. If possible, 10% if you are working and if you're not working, you're still studying, then of course most of your time should be going and reviewing the latest blog and something that you read in the blog, going in back again and then checking, okay, what did the blog talk about? I said the blog was about blockchain. So then going and then referring to blockchain and now we have chat GPT and so many other tools that can explain you in a much easier fashion. So I would say continuous learning is one thing, Sanjay, that I would encourage everybody. And then of course, if you have access going to conferences or even networking with people, asking what they are doing, how do they move forward in their life? How do they learn? Also gives a good flavor of understanding and being current. And then other than that, I would say that there are some good research papers and some good podcasts that you can listen. So I preferably whenever I'm going for a walk, I would just start a podcast on my phone or just read or listen something more that is relevant to my technology. And then that gives my brain a thought process going, okay, this is a new use case that somebody is coming up, let's try and implement this at work. Or let's discuss it with other industry experts. What are they doing about it? Or that's something that opens your brain and gives you more ideas that's something that you can implement. So reading and being relevant is something that I would encourage to everybody. And in the world of digitalization, these things are not away from us, like lots of podcasts are available, as you told. And I just like when you explained, like when you are walking somewhere and at that time, like if you are listening, so we are doing some time management as well, right? So if we can make a habit of those things in our daily life, so I think everybody can learn new things day to day. And this is very much important, like to be updated, right? So next question is, can you like give us a real world example so that people can relate like how data science is basically impacting business or industry? Okay, so I can give you a very close example to something that we do at work and I work in a distribution company or an industry. But I'm gonna give you an example of a retail industry. So if you talk about retail, before we started into data science or AI or any of these fields, the data or everything was very manual. You had sheets or you had spreadsheets many times, many a times spreadsheets were also not there in some of the industries that were just printouts that people were working out of it. And now this is, I'm talking about 10 years back, right? Now as we are progressing as the realm of data science has increased in the last 10 years or so, I feel like if we talk about retail industry, then it is a mandate I would say for a retail industry to have a supply chain management, right? And how the supply chain management work, it solely depends on data, right? And how easy or how efficient it has been if you order something online from a retail store and how quickly it comes. It's only because you have, somebody has access to the data and they know how fast something can be delivered, how easy the sales have been or how the customers purchasing patterns have been. So everything is already known is already available and that has made our lives so much easier. Now, if you think about 10 years back, how would you imagine somebody delivering a product to you or just a pack of sugar to you, right? If you order it today, it's gonna take so many days unless you go and tell your local grocery store to deliver it to you, right? So I think, this is just one example I would say, but it's a very similar and a very common example that can come to my mind, you know, because of the data there is so much of the data, you can understand customers purchasing patterns, what are the seasonal trends and how would the demand and forecasting models work? All those type of things give us an idea of how things can be promoted or how the carrying costs can be reduced or how can we minimize the overstocking and so many other things that can be done as part of retail. Yeah, so I think as per your conversation, I could understand, I think in each industry, we require data science experts, right? Yes. Okay, and like, is it only role available or like it is having different roles as well? Under this umbrella, like we have data science, it is our technology, like can you tell me like what all different roles are available there because it will help like beginners to identify like they will be becoming data science expert or something else as well if we opt this technology. Mm-hmm. So many are times in many industries, as you're saying under the umbrella, what does come under the data science. So many industries, they start from a very simple platform which says, okay, start from reporting, right? You start reporting the data. So now reporting the data is not so easy because now you are focusing on the data accuracy. What are you reporting? How are you reporting? Certain aspects of automation goes into that. Next step goes into, okay, create something like a dashboard or a visualization where you are automated to a next step, right? That comes into, you know, how you are stepping into doing some sort of coding, some sort of automation using Python or our understanding the statistical model. So these are all steps that I'm talking about. One is reporting level, which is the basic one. Then it comes to dashboarding, creating the dashboards that is in Power BI, Tableau, Click or any other tool, right? So that comes the next level. Then it comes the analyst or a senior analyst position where somebody can do some coding and then, you know, do some automation on a next level and then have an integration or a pipeline with other tools. For example, with your ERB system or with Salesforce who extract data from one side where it also involves sometimes you're cleaning the data, right? So extracting, cleaning and then prioritizing. Then it comes to a level where you're predicting the data, right? Now this we're talking about on a level of a senior data analyst. When you're talking about predicting levels, now that is the level of senior data analyst. And then comes the data science, right? Where you are actually delving into the models and understanding the statistics and then saying, okay, this has been my pattern previously, now this result is, you know, the model is giving me XYZ, but I don't think this is correct. You know, you use your human brain and common sense to see whether this is going right or wrong or, you know, would you like to explore other models? And then that's the work of data science where you understand whether, you know, this model is better than the other model, right? So that's the path that I would say goes. So these are all the level of positions and the type of jobs which people can pick up when they're stepping into this field. Yeah, absolutely. I think this explanation would help all the beginners because I also heard data science. And for the first time, like, I also knew from your explanation, like, there are different roles available. Data science is having different roles. And step by step, like, people can learn and become, like, they can have a job on particular role, right? So, yeah, this is great. Now, like, let's jump onto the AI. So, like, nowadays, we can see AI has shown massive growth in various sectors, right? And can you just share an instance where AI brought about a transformative change? Any real-life example that you can relate with the industry as well? I can talk about two major fields. I would say one is definitely a transformative, but I would still mention the other ones. The first one would be healthcare. So, you know, AI we are using for diagnosing the diseases, develop, you know, treatment or deliver care. Medical imaging, that is AI-powered, is used to detect cancer cells with more accuracy than what human radiologist were able to do at one point. So that has really changed the arena and early detection of diseases is so much helpful because then it helps them increase the lifespan, right? So I think that is a good example of a transformation that I would say. So healthcare is number one. And then also, you know, finance, where they've been able to automate so many jobs and detect frauds, right? And then able to predict, okay, if something is going in the right direction or not, we have had so many instances in the past few years where, you know, bank itself were failing, right? So now with AI-powered data and then seeing the trends and then of course that led to stringent rules, of course, but then that was only possible through data and its understanding. Yeah, so next is like, let's talk about ethics. So when AI comes, so ethics also comes in the picture. So ethics in AI is a crucial topic, right? Critical topic. So from your perspective, like, what are some of the key ethical considerations that any data science or AI learner should always keep in the mind? For this, I would take a minute, Sunday to explain one thing that you have to understand that AI is a system. It is something that we as humans are building. We are training the data to recognize certain patterns. We are giving the system to train it first, train the model first, and then give the results based on that model, right? So as people who are developing that model, we have to understand that there is always some type of bias in data, whether we recognize it yet or not, right? So when there is a bias in the data that already exists, then it is possible that there can be discriminatory decisions that can be there, right? For example, we may not understand our data, may not show, but it is, let's say, biased towards male community, right? But we might be able to see the same impact in our results, but then we might be thinking that, okay, this is how the model is behaving, so it has to be right. But no, as a person who is developing that model, you have to see and understand, oh, that is the reason why the model is behaving and not favoring males, because my data was biased itself, right? So that is the number one thing. Number second thing I would say that there's a lot of potential to misuse AI. So as you see, a lot of articles are coming up in the field just related to ethics. One of the major piece is, since there is so much potential to misuse AI, just reading your data and then understanding what your patterns of purchasing is, and then just showing you those articles, and there's so many things, I'm just giving you a very simple example there. But there has to be some sort of control towards that. There shouldn't be anything related to manipulating people's behavior or something like that, right? So just a control towards misuse. It's a possibility and the VS developer should be aware about it and make sure that we don't do that. Yeah, so before moving to next question, I just want to request all the audience. Like if you have any question in your mind, so just feel free to post those questions in the chat. So once we complete the conversation, so I will be picking a few of the questions, not all. So I would request like Sonal will be answering those questions as well. Okay, Sonal, next is machine learning and deep learning. So first of all, like machine learning and deep learning both are same or different? So I would say, so machine learning is a subset of AI, of course, right? So machine learning is like training a model to recognize a pattern first of all. And then making predictions based on that. So that's as simple as what machine learning is. It's like you are teaching a computer, how do you recognize, let's say a spam email? So that's an example of machine learning. And deep learning is a specific part of machine learning itself. Okay. You're taking artificial neural networks and then understanding the complex data. So it's like the next level, of course, but then it's still the same stream, I would say. It's still a part of machine learning, though you're going working more on the complex data. For example, now deep learning would be, you know, self-driving cars are an example of deep learning. Yeah, right, yeah. Okay, so next is like many beginners, those who want to make their career in data science. So what skills and resources would you recommend for someone who is just starting their journey in this field? Like how they can start, what they can learn. I know you already explained all different roles, but if someone is a beginner, so how they can start their learning. So just focus, like if they want to learn those things, self, like self-learning if they want to start, so how they can start their journey. So I would say a few things Sanjay, if I have to mention I would say basics. Basics is, for example, I consider SQL and database knowledge as basics. Okay. So that's number one. How to read a database, how to join in a database and all those type of things. So database knowledge SQL, that's one. One, either Python or R, right? One visualization tool, any tool, whether that's Power BI, that's click, that's Tableau or any other tool, somewhere you know how to create a dashboard. So those I would consider basics. Now if you want to go towards the next step, then I would say work on focusing more on grasping knowledge or programming. Of course, I covered Python before, but then if you can delve more into advanced Python, that could be another piece, right? And then as you're moving further on a higher level, then I would say, okay, go more towards how to clean a data, how to extract the data and learning more about the tools that are available that make your life a little bit more easier, right? And then of course, hands-on is the major thing, whether you're doing SQL, whether you're doing your basics or you're going on the advanced level. To step into these are the things that I would say. And then of course, you can increase your realm because now you'll be learning more about the statistical models, which models work and there is no limit. And you can go on learning and learning and learning and it's a continuous learning process. But if you're just stepping into and you wanna do something by your own, start with the basics, practice and do. Not just watching videos, not just reading about it, not just reading the interview questions, but doing them. I think that's the key. Exactly, absolutely, because without doing, if you are learning only the concepts, that doesn't make any sense, right? Yeah. So Arun Pratap Singh is asking, so now I'm going to take some questions from the chat. So Arun is asking, what is the difference between data analytics and data science? Okay, okay, I'll say there are two terms. One is data analyst, which is a job, which is a position of an analyst, a person who analyzes the data. Analytics is a stream, analytics as in, okay, you are analyzing the data, so data, so how you say, okay, data science, data science is a stream or data scientist is a job, right? So that's the difference, analytics is a stream, data analyst is the job that I mentioned about. Okay, yep. And the next question from Arun itself, how is AI going to change the future and it affects on data science branch? How is AI going to affect the future? I think it's already impacting. Yeah. I would say, earlier we used to drive our cars and say, okay, now I have to go, I have to drive, let me look for the directions. Now it's all voice command goes to the car and the self-driving cars are available. They're not as popular right now, still in the beta testing, but I feel that the world is changing already. There are so many things that can be done. I, for job seekers, there are so many new tools that are available that can help you create your resume if you're sitting right in front. You do not have to make sure that your grammar is correct and you know good grammar, right? I would say five years back, this tool wasn't available. You cannot go and have a resume all by yourself, which is in the pristine condition. Now you can. So I would say life is already changing and in the future to come, there are more use cases that are coming up. I would say in the last three years, as we have seen AI develop so much, there have been so many use cases where you are able to see and understand. You know, what are the new things that can be developed? So for example, chat GPT came through and you know, as soon as that chat GPT was live, there were a number of other tools who started to have integration and could have a good support from chat GPT's API. So I believe, you know, there are so many things that are already changing and you know, that's something that I would say. Yeah, and next question from Arun. So he's saying like in recent times, most of the companies require data science, but why? And why has its demand suddenly increased since COVID? Okay, so I would say data science as a demand was there earlier. It used to be called as, you know, data analyst, data analyst position or a, you know, now the scope of the field has increased more because I would say and rightly after COVID because the companies were able to analyze and look at their data and their statistics during that time and see what their results were. But I would say that, you know, prior to this, they were still doing these activities, but maybe COVID made it a little bit more, you know, it emphasized it a little bit more. I'm not sure if I am relating the COVID part to it right, you know, correct Sanjay and Arun, but I feel like, you know, there has been some correlation which you rightly mentioned, but you know, data science is definitely, you know, becoming more popular, but it has been there before also. Yeah. Definitely, I would say in the last 10 years, COVID has just been there for the last three, four years, but then I would say data science has been progressing for the last 10 years. Yeah, absolutely. And yeah, so now one question from my side that I hear from lots of folks like AI is there and people are like having the fear, like will AI replace the jobs? So what is your perception to this question? Like will jobs be replaced or like it will be like going away? So what do you think? I feel like with AI, I wouldn't say the jobs will be going away. I would say the mundane work of doing same thing again and again can be automated, it can be automated even without AI, right? There can be some smart tools and technology, it may not be just AI, but yes, some mundane jobs that can be going away, yes, they can be, but however, we have to understand that one fact that it's not just AI that is making a change. We are building the AI system and if we can build a tool to automate a certain job, then the answer is yes, but it cannot replace a human mind. So if we are keeping ourselves up to date and still understanding that AI is there for me, it's a mindset shift, I would say, that if I am learning and I am keeping up to date, then my job is not going to get away, right? For example, certain invoicing, there are people who are just doing invoicing on the computers and just doing order entry. Now those type of jobs, can they be replaced? Yes, they can be replaced not just by AI, but it can also be replaced by a voice command system, saying that, okay, a phone call was received and the voice command system read and took the order rather than a person took an order. Which happens many a time when we call a banking, just calling a bank and there used to be so many tele operators earlier. Now that has been automated just by a voice call and you press, okay, you want to call it an operator, press nine, if not, then press the other selections. So those type of jobs have already been replaced and certain mundane jobs are and I believe they should be replaced because if it's like something that machine can be done, then we should do that. Yeah. But yeah, the learning here is that we should be able to keep up with the market. Instead of getting intimidated by the fact that, okay, AI is going to take my job, we should be up to the power saying that, okay, I think I should be learning a little bit more what AI does and how it does. Getting informed more and taking informed decision is something that I would encourage in that case. Yeah, perfectly answered. So like we need to upgrade instead of having any fear of losing job. Correct. For sure, there will be some changes in the pattern of the job but as I come from Salesforce ecosystem, so Salesforce is also like enabling their clouds basis on the AI. So in Salesforce ecosystem, also like people are thinking like, what will happen? What will be the future now? So like as things are progressing, so like our suggestion will be, please be up to date, learn new things, try to learn what is happening in the world of AI so that you will like get away from that fear zone, right? Absolutely. So yeah. So like we have done all the discussions, like so I just want to conclude the session. So before wrapping up, if you want to give your final advice to the beginners or like students, like how they can look into data science and AI, these technologies, their career as a future. So what suggestions you want to give to them? I would say work smart, not hard. Don't try to learn everything at once. Start from the basics and see what you can do, do hands on more rather than reading more, right? So I think in terms of data science, people start to think, okay, the syllabus is so wide and I cannot learn everything. Though I agree that many a times organizations require a whole lot of information, but if your basics are strong, you will be able to have a better foundation to answer the complex questions as well. Right. And like in India, there are lots of colleges where data science and AI is a specific branch. So earlier it used to like computer science branch. Now they have a specialized branch which is like data science and AI. So lots of like new things are happening up. So I think whatever insights you have given in this session, it will surely help beginners so that they can shape their career path in the field of data science and AI. So yeah, thank you Sonal for all the insights that you shared with the audience. And one more last thing, like if someone is having more questions and they want to like learn more about data science and AI. So can they connect with you on LinkedIn? Have any barrier and you want to remove that barrier and want to like have your future shaped in the field of data science and AI. So she will be the right person. Okay, so once again, thank you so much for joining us today like Sonal. And your insights have been truly invaluable. So I hope like in future, like we'll be having some boot camp. So I will invite you again for these sessions because you have lots of industry experience and that will benefit our beginners. So we'll reach out to you. Yeah. So thank you so much Sonal once again. Thank you Sanjay. Nice having this conversation with you and our guests. Thank you. Thank you so much viewers as well. Those who are joining live and those who are watching the recording. So thanks to you as well. Thank you. Have a nice day everyone. Bye. Thank you.