Day 9 | Salesforce AI Associate | Data Analytics for AI | AI Bootcamp | Sanjay & Nikita

okay we are live so hello everyone I'm Sanjay Gupta I welcome you on Sanjay Gupta tech school so we are back with one more session on AI associate so uh we already did lots of sessions on AI associate and this boot camp we started last year uh when this AI associate certification voucher was not available free and all those sessions we already delivered so uh Nik who is expert in this area and uh she already delivered all those content and when uh Salesforce announced like AI associate and AI specialist certification vouchers will be available free of cost till next year December so we received Lots of request like can you please deliver few more topics those are pending in this playlist so to do that uh like I just requested Nikita so that we can cover up the topics which which are remaining in this playlist so that if anybody goes through this whole playlist they can appear for the AI associate certification and uh they can learn the concepts as well as they can clear the certification uh in the first attempt because only first attempt is free and uh next attempt you need to pay so with this note I welcome Nikita on this platform once again so thank you sir okay so uh she she will be delivering uh today's topic that is based on data analy analytics for AI so over to you so please introduce yourself once again and then you can start with the session and one more thing you will be having lots of quizzes so maybe those questions will be coming in the exams so uh please pay the attention in the session over to you hi everyone this is Nikita again and today we are here for uh doing one more session on data for AI but the topic will be like morly about data analytics and then we'll go to data quality so all the leftovers that were there that we told you that we would be doing in near future so that's what we're going to cover today um from the data probably the next couple of sessions will be based on ethical considerations for AI initially we thought to these absolutely theoretical um uh you know modules just because we thought that you know there's nothing much about it to be explained in a in a you know a diagrammatic manner or you know there is no um such a buildup way of explanation there's just most mostly it's theoretical only right so for that reason we thought that we going to uh you know maybe skip that part but right now since we are getting those comments where they are uh where the you know readers and the commenters and the people who are actually going through the a associate certification they told that you know they want some um the ethical considerations Plus data for videos so we thought that why don't we give it a take that you know we are explaining all of these things bit by bit now obviously it's not possible to consolidate everything collect everything and propose it in front of you but whatever is important whatever can be briefed and explained in a manner that probably we have uh made U you know thorough presentations of and then we have also kind of gotten some questions that could be helpful for you so that's for that reason we are here today so let's proceed further but before that it's just my um you know experience profile that is in front of you um we have also started a parall uh boot camp for python um U that is um like right now we are just finishing off with python so that we can Implement that through all those machine learning algorithm decision trees and all could be you know explained via those the by the Python programming language so that's what we are doing parall um so now since um this will be completed in this week probably this week we will try to complete all this a associate thing and after this one the next week that you'll we'll be doing everything parallely uh that is the python and AI modules they were left overs so I'm going to take that also right so in the next week we are going to again do the Consolidated profile or Consolidated this discussion on our other things that are in the topic of machine learning so yeah I mean that's it this is what uh we are going to do in our next couple of weeks probably let's move on to the fundamental topic today that we are going to discuss what is data analysis so in the data analysis we have the thing over here which is it is a process of Performing preliminary investigations on the data now since you have got the data you probably need to do something about it right and what would you do like if you if you are a company you probably would certainly app some of your Majestic uh you know properties attributes um and your algorithms into this certain sort of data so that you're able to channelize the information that you have gotten from this data and perform and uh execute all of it so that your company can maximize all the profits right now as we talk about Salesforce it does need uh the user base and what is it going to do of that user base the CRM thing that is going to to be taking all those uh things into consideration and Implement them through data analysis so data analysis is one of the vital thing that we would do in Salesforce in order to implement uh the bestest services that we can give to the client right so it is a process of Performing preliminary investigations on data using statistics and graphical representation to unwrap the patterns and detect the anomalies so unwrap the patterns means whatever is hidden inside that particular set of data uh and whatever connections that we can build up whatever information we can extract from that data all that we are going to take from that data so the first thing that's going to come come up in data analysis is whenever you have data you have to put in lot of efforts in cleansing and organizing data so as you can see this diagram explains you everything that is uh regarding the data so here 60% of your time is going into cleansing and organizing data this is the most uh you know time consuming thing that um has been there and then we have got 19% of U one second I'll just hide this one second yeah it's not visible on the screen no problem okay so um I was explaining you about how much time consumption is going into you know all this process of data so collection of the data sets require requires time that is 19% of the time is going there then mining data for patterns so you know it's all written over here that what all things are to be implemented when you have data which is the new oil that is what you have today so building and training sets would require 3% of this um you know time consumption and then 4% is refining algorithms you it would take so all the other things would take comparatively lesser time than the cleansing and organization of data would take so you can holistically see that first step is to have a clean Consolidated data in our you know data warehouse or data leges then we will move on to the categories so the first category that we're going to study today is descriptive analytics there is u a couple of questions you will see in your quiz that will be based on predictive analysis descriptive analysis prescriptive analysis diagnostic analysis so you know you will have to go through these four things pretty wisely so that you do not miss out on any of the questions that will be there so let's go with the first one the descriptive analytics which actually tells you in the data what went wrong so descriptive analysis involves summarizing historical data to understand what has happened in the past past it focuses on providing insights into past performance and understanding the trends so whatever has happened in the past all of its holistic study is going to be given by the descriptive analysis and the word itself is quite literal in its meaning it's going to explain you itself only that it's going to describe whatever Trends were there that existed in the past and how uh things could have gotten wrong how or what things did went wrong and uh you know all of that description about the data is going to be given with the descriptive analysis so it's a data analysis type used to give you information regarding what all happened okay so you will get a quiz question that in descriptive analysis what exactly is there so they will give you some options where in one of the options will be uh read like you know it is going to give you information regarding what happened so you have to click this option so it is intended to allow you to use data collected by just interrupt are you sharing some quiz or no no I'm on the presentation only I'll take you to the yeah yeah after this one okay so it is intended to allow you to use data collected by a system in order to help you identify what was wrong what could be improved or which matric is not responding as it should which matric means uh so we when we take some values from the data whenever we have some variables whenever we have some columns and rows right so these are all a system so whatever system is not performing very correctly that is all is going to be recognized through the descriptive analysis of that data so as this type of data analysis is widely used to summarize large data sets in order to describe outcomes to the stakeholders so here it is also something that is going to tell you more about the kpis so the most relevant matrics informed by those systems are mostly known as kpis that is the key performing IND indicators of the data so here descriptive analysis if you will read out this simple Chunk from this only you can derive maximum knowledge uh wherein you will be able to answer the max quiz questions if they arrive from this particular topic okay the next one is diagnostic analysis now diagnosis what do we do what do we mean by diagnosis so in the diagnostic analysis it aims to determine uh why something happened by analyzing the historical data so it is going to give you the details about not what happened however why whatever happened that is about the diagnostic analysis so we run the diagnosis to realize exactly what went wrong but also why went wrong so the what of the things is going to give you going to be given by the descriptive analysis but why of the data is going to be given by the diagnostic analysis so it often involves drilling down into Data to identify patterns and relationship that can explain certain outcomes so whenever this explanation is required that means the diagnostic analysis is being done so diagnostic analysis helps by going over the scope of just informing but diagnosing by further investigating and Corel those key performing indicators in order to give you suggestions on what issue could potentially be there which existed in the past or it is still existing the diagnostic analysis can be referred to as a set of actions A system can do to to help the stakeholders understand why something happened the word you need to remember here is why so this is the Highlight the why is the highlight of the diagnostic analysis what was the highlight of the descriptive analysis the next word that you're going to see is the predictive analysis so when I predict something that means I have certain knowledge about my past or anybody's past and hence I'm able to predict anything out of it okay so first I need to have a relevant knowledge about the data whatever I am using or my model is using so Predictive Analytics uses historical data to predict future outcomes because prediction can only happen when you are aware of the history it employs the statistical models and machine learning techniques you see that is why you need to learn about these statistical model and machine learning in order to stay into this uh Arena of uh artificial intelligence to for uh to forecast future Trends or behavior patterns based on patterns uh identified in the past data all of whatever I have explained you just now is written in this in this paragraph now predictive data analysis involves more complexity because as a name suggest it predicts what it what is likely to happen in the future Based on data from the past so make sure that whenever you have such question in the quiz like know what predictive analysis is going to do you've always attempt an answer which says that it is going to be reading the history of the data or it is going to be uh getting this information from the history of all the data and then it is going to predict the outcomes make sure that you notice that history of the data is very important in order to produce the outcomes from that data in a nutshell it kind of tries to predict the future based on the actions from the past the use of neural networks regression and decision trees are very common on diagnostic analysis and we will be covering that all of it into this course that we are running parall in our system that is in our course syllabus that San s will always give you the idea about he's going to publish notes and uh I mean this course details in the in his profile maybe in the next week probably so you'll be getting more idea about it so is uh you talking about the AIML thing right yes yes yeah so we we are already doing that boot camp on YouTube and uh guys soon we'll be launching a course uh that will be for limited people so I will share that uh detail on my website LinkedIn so that you will be awared about that the last type of analytics that you're going to study is the prescriptive analysis now Beyond prediction what happens is the prescrip prescriptive analysis it is basically the addition or cumulation of all the analysis that we have done so prescriptive analytics goes beyond predicting future outcomes by suggesting possible actions to take advantage of those predictions now I have made the prediction but now what like what about the future like what am I going to do of that prediction so if I would do the prescriptive analysis that means what is the action I should take in order to maximize my profits and business expansion maybe something else as well but prescriptive analysis is the uh the end of whatever we have studied till now so it is going to provide recommendation on what actions to take to achieve the desired outcomes now one of the outcomes only I mentioned however there could be hundreds of outcomes that you can implement this prescriptive analysis for so prescriptive analysis can go ahead and suggest the stakeholders what are the most data driven decisions that needs need to be taken based on the past events and the outcomes so you have done the prescriptive analysis uh post the predictive analysis since predictions have been done but you have to be aiming to do something about it right and that comes uh with the prescriptive analysis so whatever you think of doing the action that action comes into the prescriptive analysis so it is going to give you the prescription of what has to be done in order to maximize your outcomes the desired outcomes so prescriptive analysis highly relies on machine learning strategies in order to find patterns and their corresponding remediation by looking and crossing the large data sets so AB uh we have used in fact an AI associate only I took very small data sets obviously I have taken like the you know the the kid section maybe where I was explaining you about like four people five people data I have taken but it's not the case you have to work with thousands of data values right and only then you can be very um useful to a certain organization okay now data analytics applications we are going to see where all it is present in lately or in near future where all it can be implemented implemented so data analytics is widely present in many vertical today such as gaming social media feeds e-commerce online stores websites everywhere guys everywhere data analytics is um taken into um yeah and then um even right now I would suggest ke um in the stores even when you go to the retail stores right now everybody is using the data analytics I don't know about the future because it's exponentially rising and today only Sam Alman made such a drastic um you know um um I should say this um news was coming out that you know if you are building a startup then you just need one person and then you would just need a, of gpus that would be working for you so it's all about data now you can maximize the usages through data what all you can do with the data CU new startups have to be coming coming in in order to have uh you know valuable employees valuable employers uh collected whatever we can see is lot of um you know uh people are not having enough jobs that's because we are not having enough startups so multiple startups have to come in in order to have jobs uh in the market right so if you could be the one you definitely should be you know working on such things so that you can employ maximum people under your organization so analytics helps people develop insights and those insights help them to deal with complex problem solving be it for gaming stock markets real uh State data traffic information fashion computer systems web servers or server security logs so everywhere data analytics is going to be used like you can see that even in the real estate where it is it wasn't it was a thing uh in the past where only you know a couple of Brokers were doing this real estate but now the data analytics is being used in real estate as well so you can see from where to where it has um reached you know the implementation of data analytics the world nowadays is becoming more complex than it was 10 years ago hence computer system play an instrumental role for two main reasons one is scalability and the other one is data driven so if you're not using your uh technology right now you are actually having a setback right uh if you have to have a multitude of business right so you have to use uh all the technology because without technology you can just reach a certain space but with technology you can crack every space make sure that you are the one who is making the making use of Maximum technology possible now we'll go to the major application two that is log analytics that to specifically regarding the security logs now security logs means uh if you are aware of this thing that you know whatever you log in whenever you're in a company and you're using the company Wi-Fi they know wherever you are logging in they know all your server logs they know whatever uh you are you know your mobile is um accessing right even if incognito mode you on uh you switch on your incognito mode then to you are um visible to all the server logs your data is being read uh there in the server logs as well so that's why some of the companies they freeze certain websites so that you are not able to waste your time into those websites you must have seen that I'm pretty sure now it can be the HTTP request mode to the web page user logging inform API that is application programming interface calls or any other type of requests API is basically a Computing interface which defines interactions between the multiple software intermediaries it defines the kinds of calls or requests that can be made so if I make a request to the web server that please let me access this web page I'm actually requesting the HTTP to get me that particular access of the web page right so now it defines the kinds of calls or requests that can be made how to make them the data formats that should be used the conventions to follow Etc and for the data analytics standpoint it is very common to log all these activities somewhere so now here comes the uh usage of data analytics in the server logs that wherever I have logged in we are going to run the diagnosis has to be done in order to check which are the most frequently used websites from our clients right if I if I own a company of 1,000 clients let's say sorry 1,000 uh employees let's say so I need to check which are the most common or most frequently used websites by my um employees correct so for that I will use the diagnostic analysis and I'm going to check that what exactly is the position where exactly are logging in so that is how your server logs are being um noted and then diagnostic has been done and then regarding that the actions are taken so yeah I mean this is how it works so example data analytics uses web server logs to extract the insight about visitors in the website correct so you can see the visitors in the website now if I have to check from which position am I having maximum uh visitors so for that also I run the diagnosis uh if I would want to see that you know what St are there students who are reaching out to me or are there uh corporate people who are reaching out to me middle-aged men reaching out to my content on the on the YouTube so whatever I want to figure out whatever data or whatever information I would want I would run that type of Diagnostic and I would do that type of data analytics so all that all this is done in the YouTube Studio One of the applications if you are making content on YouTube you would be knowing about YouTube Studio they all have such kind of data analytics present in that particular application so YouTube is a big big company and uh they have all sorts of data analytics that are going on these days and like of course they have been going on since years now so if you have one new line added to the access log for every visit in your website you can say that the number of lines in this log is equivalent to the number of requests that was served by the web server so this is about the data analytics of the access logs that you do so yeah before we jump onto the next uh topic data quality so there is a question related to the data analysis so someone is asking uh there are two terminologies one is data analyst and one is Salesforce data analyst so uh in my opinion like Salesforce data analyst will be U specific to sales for CRM only so like uh like if we talk about generic data analyst and Salesforce data analyst so other than um Salesforce as a keyword or the CRM so is there any um other key differences will be there of course in see data analytics come uh I think your video is frozen or something has happened yes yes no problem you can carry on okay yeah so um I was telling you about there are a lot of uh um like spaces where in these data analysis can work right every company would want it and not only s Salesforce you're going to be having a data analyst independent um this profile that is going to be there for every other um company so it's not I mean I just want to say that if you are a data analyst you could work with the Salesforce but there's a possibility there are huge possibilities apart from Salesforce as well okay there's no like it's not a a thumb rule that uh you are using you are with the Salesforce so you will be with that only means you can be an independent data analyst that could work with other companies as well if that's what you're asking I mean there's no key difference obviously right okay y uh I think prakash this answers your question so if you have any other question so you you can just write in the chat yep I think we can continue so now we are going to go to check about the data quality so um I will just brief you about what data quality is so a good data is a well organized data a good data is a consistent data it is like if I want to check something about Nikita I'm going to see the logs and I'm going to see okay this is Nikita username and then I'm going to see the details about it so this is how a major quality uh data would work for you right however if it is something like in my column where the user ID or usern name would be written or should have been written I would write their shoe size that is completely irrelevant right so this irrelevance has to be avoided when you are talking about the quality data analysis so data quality plays a major role in shaping the outcomes and reliability of the AI systems the poor data quality can compromise the system's performance leading to biased outcomes vulnerabilities and even ethical and societal repercussions messy or unrepresented data can lead to misguided business decisions and increased operation cost so I lately was telling some of my um other students when we had this session so there was one of my uh friends um uh colleague maybe so they were um I mean they were into a certain company obviously we I I I can't take any of the names but this is an XY Z thing I'm telling you so this y person who um was there they actually missed out on a lot of things and they put a lot of costings into totally wake things where all the um like a huge chunk of money got wasted just because this cost was not appropriate implemented or not appropriately put in valuable uh you know decision making so all that I want to say is when your data is totally irrelevant and about uh something which is not a latest Trend right when people are not picking up on it so you can lose a lot of money U and have a lot of wastage so probably the best thing would be to use the data which is is going on with the trend and that data should be clean concrete consistent data and not something that is Wake CU it is going to cost you a lot the companies uh do encer losses if they do not uh you know implement or if they do not collect the data that is in alignment with the trend that is on okay so make sure that whenever you have a certain column certain row which is named that way you are putting in efforts to collect data and organizing it uh name within the name uh and uh you know whatever other things that you're taking within the other sets plus it should be in the trend if you are using 19090 Trend right now it is not going to work correct so you should be pretty peculiar about this data that whatever you are collecting and uh feeding your AI model with now high quality data ensures that AI models are accurate unbiased and robust in their predictions it allows these models to better predict real world scenarios and fosters trustworthiness in their applications high quality data can actually maximize the return on investment in AI initiatives if there are some AI initiatives if you have to maximize the return on investments please make sure that you only use qu qualitative uh data analysis on that and you use quality data into U into the um like collection plus implementation in the AI model so adherence to the data quality standards is vital for compliance and involving Global regulations in essence the foundation of any effective AI system lies in the integrity and the quality of data the better the AI model the depends on how better is your data now we go on to the data quality attributes so first is accuracy data should be correctly present represent the real world conditions or scenarios as I told you right now if it is wag it is out of trend it is not going to work okay and then you have completeness completeness means all necessary data should be available and include all required Fields without missing values its importance is that incomplete data can lead to biases and incorrect model training so always you should have necessary data available if it is not available your data would be regarded incomplete and your AI model will not run according to your optimized uh output values then you have consistency data should be consistent across different data sets over and over time the importance for this is that inconsistent data can confuse AI models and lead to erroneous predictions consistency ensures the model can integrate and learn from the data accurately then you have timeliness data should be up to date just what we discussed if it is irrelevant to the current state of environment it represents it is going to be vague the operational cost is going to be total waste then you have relevance data should be pertinent to to task uh to the task or problem the AI model is intended to solve irrelevant data can introduce noise making it harder for the model to identify the correct patterns and relationships then you have uniqueness data should be free from duplicates and redundancies I taught you about the bad data um things earlier in the previous session where I told you that hallucinations occur in the data and then you know that that is there's a lot of problem of uh that data can be pirated or data can be copied something like that so the your data should be unique in order to have the a good AI model running through then you have validity so data should conform to defined for mats and fall within the acceptable ranges invalid data can introduce errors in model trainings and predictions ensuring validity helps maintain the Integrity in the data set lastly we have integrity data should maintain logical relationships and constraints uh data Integrity ensures that related data across various data sets are accurate and reliable supporting cohesive model training okay so these are some bad data quality repercussions lost Revenue missing or inaccurate data Insight wasted time and resources inefficiency slow information retrieval poor poor customer service and reputational damage all these negative things come with the bad data repercussions means the outcomes of the bad data quality and the good data qualities are the Prospect and Target new customers you would always get new leads and customers when you have good data then identify cross sell and upsell opportunities you can maximize your selling opportunities gaining account types increase the efficiency retrieval of the right information that to very faster build trust with customers increase adoption by the Reps plan and alignment of the territories is also better and the score and Route leads the faster so these are all good data qualities that you can see finally how can we ensure data quality so first is data pre-processing then comes data augmentation augmentation I'll explain you bit by bit then there's validation and verification then data C governance and then finally we have feedback loops so first is data pre-processing what comes in that is cleaning and transforming raw data into usable format this includes handling missing values correcting the errors normalizing the data everything comes into that so first thing is to CL cleanse and transform raw data into usable format then comes augmentation wherein enhancing the size and quality of the training data sets with techniques like oversampling under sampling creating synthetic data to ensure diversity and completing so let me tell you about that enhancing the size and quality of the training data sets means whatever I take training data set as a different set for training my uh AI model and then I will take the testing data set that as a different set for uh testing my AI model okay so I need to maximize the training uh data set in order to get the the final optimized output that output that I am looking for so I need to have a perfect range of training data set in order to get a simplified personified output okay so if this is not done probably the AI model would not work as it should so for that reason I need to apply over sampling under sampling and also I might have to create some synthetic data means synthetic data means my it's it's not a uh taken data or observe data it's something that I have created myself in order to uh maintain a holistic U set of this training data set then comes the validation and verification so regular checks and validations to ensure data Integrity accuracy and consistency this is what it requires this includes automated scripts and manual reviews then comes the data governance implementing policies and procedures to manage the data quality so you have to be pretty relevant about all the PO policies and you know procedures to manage the data quality you need to be uh very like open eyed you need to be very in alignment with the policies that are there in order to ensure the data is in rhythm with the certain policies that are government uh that are in the government so now including setting standards monitoring compliance and continuous Improvement processes I'll take you more um in a comprehensive way how ethical considerations is done so for that I will need this data governance needs something more to be explained which is in the ethical considerations of AI that we will discuss later and then we have feedback loops that is using feedback from AI model performance to identify and correct the data quality issues continuously so this iterative process helps refine both the data and model over time so feedback loops obviously you require feedback for whatever model you have created so that is to check the AI model performance that is feedback loop so all these five things you need to ensure for the data quality that is the maintenance of high quality data now I'll take you to the place where is this so here we have got the quiz for you just to interrupt so so all the theoretical concepts are done for this data analytics for right okay so guys uh like now she will be explaining some of the quiz questions and uh like remember all these questions like we collected from the trail head so uh on different trail head modules we have different quizzes so we just prepared a document and uh maybe I will be sharing this documents PDF in the telegram channel so that you can access it so here all the quiz along with their answers are available so she will be discussing their Solutions with you yeah I think we can start so um here you can see that there's a true false question that is data analytics helps leaders make decision based on information yes obviously it does because data analytics means that collection of information and Performing all the analysis on that particular information so yes true that is of course true now what does descriptive analysis tell leaders and stakeholders about the business we just did it that what's going to uh going on based on the data in the system that is what is descriptive analysis is so what is the short term that you can say about the descriptive analysis then you have to go on to the next thing that is how does data analytics software tools help solve complex business scenarios so here they help scale data collection and visualize Trends across a lot of data points so this is what the correct answer is then you go to the true and false for the Predictive Analytics tool now here I T I taught you about the predictive analysis so we'll go on to the Predictive Analytics tools may be used to analyze security logs to anticipate and block bad web page request so I just told you that in this analytics what do we do is we kind of uh predict that what is going to be the best or what is going to be the bad so here we can actually get this information from the predictive analysis that uh should we block or should we not block so anticipation is something that we can uh figure out for the future that whether we should do it or we should not do it so yes true it's it's a true thing that Predictive Analytics tools may be used to analyze security logs to anticipate and block the bad web request so it's not that it is going to block it but yes it it is going to predict that whether the blocking would be having a good uh impact or bad impact that's way now what is the advantage of doing data analytics in the cloud so uh as you know cloud is something that is not a physical entity so your infrastructure cost is almost zero right so it doesn't require knowledge of in network and operating system obviously you don't have to be very Vigilant about this operating system thing you just have to be uh having knowledge about that certain cloud and and nothing else and then you can stop paying for infrastructure resources that are not required now some infrastructure that is the extra offices and all it's not required when you talk about the cloud right so for that reason A and B both options are correct what is not correct is you get to experience the data center operations of course it is not even relevant over here so now we go on to the true and false that is serverless Technologies enable organizations to focus on analytics instead of server configuration serverless Technologies enable organization to focus on analytics instead of server configuration now if if there's no server obviously there's no server configuration right so you will be able to focus more on analytics so that is true now let's see next question you are creating a presentation for a company's executive team outlining the importance of data quality what's one key factor in DET in determining the data quality that you might include so as I told you right now if you have to have a data quality and I told you something about the duplicacy that you should not have duplicate records so here number of duplicate records should be the right answer so your duplicacy should be avoided so this is our number one thing that we should spot if there are duplicate records we should extremely get rid of them at that particular instant of time right or do some queries get rid of all those U duplicate records that whatever we have in our data set then I taught you about the cons of bad data so in your presentation you note some of the pitfalls of the bad data which outcome is consistently linked to poor quality data so we just did it that was reputational damage that was one of the options that I had already shown you from the six to seven bullet points this is one of them that you will obviously have a reputational damage because you can't have increased Revenue increased Revenue happens with the good data increased efficiency also happens with the good data an excellent customer service also happens with the good data but what happens with the bad data is reputational damage next as a Salesforce admin at your company you are asked to develop a certain uh data management plan what's the one data standard that you should include in your plan okay so is that personal names or Revenue goals or record ownership criteria Salesforce login information long long-term company plans so this is again something that you might have to just go through like a couple of paragraphs we might have left or you know uh just a couple of web pages from tail head we might have left so you can just go through that uh and I think then you will be able to get through this sort of question also but I as I said that there will be now very like just count countable number of web pages that we might have left not including into this presentation other than that whatever the basic conceptual things were we have tried to Abate them into our um presentation so you can certainly go through this with the uh you know link that uh he's going to share with you and probably after that you will be acing the quiz definitely and we'll meet now in the AI ethical considerations of AI in the next session I guess yes yep so I think uh this is it for today and uh with this session uh if you go with the day eight that is also related to data 4 Ai and this is Day N which is also related to uh data analytics for AI so I think one module you will be able to prepare if you go through both the sessions and uh uh next we'll be covering ethical considerations so I think uh two sessions will be needed right or in one session we'll be covering two sessions okay then then it will be like complete playlist to uh understand the concepts of AI associate as well as to uh prepare for the certification right yep so I think we are done with the session for today so thank you so much for sharing your knowledge Nik and uh thank you uh audience for joining the session live those who joined and those who are watching the recording uh so thanks to you as well and uh like keep uh following our telegram Channel or LinkedIn so that you will get a notification for upcoming sessions okay and soon we will be resuming our a ml boot camp also so that you can learn um things related to Ai and ml like how things Works practically so few things we'll be explaining on YouTube and then we'll be having our uh course that will be for limited people that will be deriving with the help of Zoom so all those information I will be sharing soon okay thank you guys see you in the next session bye everyone