# Data Analytics With Tableau Project Knowledge Session - 1

https://www.youtube.com/watch?v=T8J_wRj2AC4

[27:56] science very
[28:04] That's what we did.
[38:50] Yes or no?
[38:52] My voice
[38:59] people on the YouTube is my voice.
[39:06] Okay, great. So guys, uh as you all
[39:10] know, we have concluded with the
[39:11] training sessions. Training sessions are
[39:13] over. Now we are here on the uh
[39:17] knowledge sessions. Okay. So starting
[39:20] from today you have four knowledge
[39:22] sessions since 19th, 28th, 22 and 23rd.
[39:27] These four days you'll be having
[39:28] knowledge session. Okay. So please
[39:30] actively participate in the knowledge
[39:32] session to understand which all
[39:33] documents which all templates you need
[39:35] to complete and how to complete it.
[39:37] Okay, you need to understand this thing.
[39:40] Why? Because once that session gets
[39:43] completed, you have to start working on
[39:46] your projects. So you have to fill this
[39:49] documents along uh according to the
[39:52] projects. Okay. And you need to submit
[39:55] it to us for the final evaluations.
[39:58] Okay.
[40:00] to understand one thing it's very
[40:01] important if any of your friend is
[40:03] missing this session ask him to join the
[40:04] session if for some reason you are
[40:06] unable to attend this session please do
[40:09] watch the recordings of this session
[40:11] okay so as I mentioned we have four
[40:13] knowledge sessions starting from today
[40:15] till 23rd which means uh by Tuesday in
[40:19] this knowledge session my uh the
[40:22] structure of knowledge session will be
[40:24] first we today we'll take you through
[40:26] the project orientation session where we
[40:28] will discuss
[40:29] 10 use cases. Okay. Out of this 10 use
[40:32] cases, you will be selecting any one
[40:34] particular use case and you will be
[40:36] working on that. Okay. Now don't ask
[40:38] again and again where will we get the
[40:40] data set. Data set will be provided to
[40:42] you. Okay. You cannot work on any use
[40:45] case of your own. You have to select
[40:47] only one use case out of these 10 use
[40:49] case. Okay.
[40:52] So today you have the project
[40:53] orientation session. After the project
[40:55] orientation session, we also explain you
[40:58] your platform. Okay, means how to access
[41:01] the project, how to which all template,
[41:03] which all uh are the activities, which
[41:06] all are the milestones, how to complete
[41:08] them. Okay, so uh understand one more
[41:11] thing. Uh the projects has to be
[41:15] completed in group. Okay, projects has
[41:18] to be done in group only. Okay, there
[41:20] will be the teamm uh it will begin very
[41:23] shortly. So please wait for the time
[41:26] meanwhile we'll be covering all the
[41:27] sessions also. So as I said in today's
[41:30] session we will give you the uh platform
[41:33] orientation session or platform
[41:35] orientation also. Okay. You can reach
[41:38] out to your college squ for the team
[41:39] formation and project enrollment. Okay.
[41:42] You have to reach out to your college
[41:44] squ for the project enrollment and the
[41:48] team formation. Okay. And one more uh
[41:52] very important thing you have to keep in
[41:54] mind that team once form will not be
[41:56] changed. Okay. The team form and the
[41:59] project assigned will not be changed.
[42:01] Okay. So till now uh I think everything
[42:05] is clear. Okay. Next thing uh I want to
[42:09] make an announcement that uh we had
[42:11] planned qu three for tomorrow but due to
[42:14] some reasons we are uh postponing the
[42:16] quiz on Tuesday.
[42:18] Okay. quiz is postponed to uh Tuesday.
[42:22] Quiz three will be on now on Tuesday.
[42:25] Okay, quiz 3 will be on Tuesday. Uh but
[42:28] the next Saturday which means uh 27th
[42:32] 27th you have a grand assessment. Okayas
[42:38] 4 to 5. Okay. 4 to 5
[42:42] clear. Yes or no till now? Clear.
[42:49] Everyone clear?
[42:56] So next Saturday which means on 27th we
[43:00] have the grand
[43:02] 23rd which is on Tuesday we'll have the
[43:04] third.
[43:07] Yes. College will
[43:11] Is there any issue in my notes
[43:18] where you said you right team will be
[43:20] found by the college
[43:22] okay
[43:26] you can reach out to your squad if the
[43:27] team members are inactive okay
[43:30] I'm telling you you can reach out to
[43:32] your college squad if your team members
[43:34] are inactive your college squawk will
[43:37] form the team and enroll the project for
[43:39] you. Okay,
[43:43] got it.
[43:46] All right. So now I'll just hand over my
[43:49] session uh to my colleague. Okay,
[43:52] Sushita. She'll be taking you through
[43:54] the project orientation session. All
[43:55] right, Sashmita, you can take over.
[43:58] Thank you. So hi guys, I am Sushmita.
[44:02] I'll be guiding you through this uh
[44:04] project development sessions starting
[44:06] from today. So,
[44:09] let's get started.
[44:12] Let me share my screen.
[44:31] Can everyone see my screen? And you can
[44:34] hear my voice, right?
[44:43] Okay. So we'll begin with this this
[44:49] session with the use cases you'll be
[44:51] working on. So there are 10 use cases.
[44:54] So these are the 10 use cases you'll be
[44:57] working on as a project. So we'll be
[45:00] we'll see one by one in detail. So first
[45:04] use case is RDCS analysis. So in this
[45:06] use case you will be given with the RDS
[45:09] data data set. So in this like you will
[45:13] have a various factors uh and like uh
[45:17] you have a various factors like incident
[45:19] rates and all. So in this project you'll
[45:22] be analyzing heart disease and like what
[45:25] are the factors that gets the heart
[45:28] disease. So this is a like for this
[45:31] project you'll be using a powerful
[45:34] visualization tool which is a tableau.
[45:36] You'll be working on this RDS data set
[45:38] with Tableau.
[45:41] So you will see you will explore data
[45:43] first and then you will identify
[45:46] correlations and anomalies and predict
[45:48] factors like they they are the things
[45:52] you have to understand and you have to
[45:54] give a solution and prevention
[45:56] strategies for from the data sets. So
[45:59] next is
[46:02] as I have told you will be using Tableau
[46:04] for analysis
[46:09] and uh through the analysis of the data
[46:12] set you'll be taking a like decisions
[46:16] like that will affect the healthcare
[46:18] industries. So you can choose this use
[46:20] case if you are interested in like heart
[46:24] disease analysis and all.
[46:27] So we'll move on to next slide. So in
[46:29] this slide we'll see what are the social
[46:31] impacts from this use case and business
[46:34] model and existing solutions and
[46:36] recommended technology stack and
[46:39] differences. So before you choose any
[46:41] project or any use case you have to do
[46:44] some research on that so that you will
[46:46] get a better understanding like what to
[46:49] do how to do the project. So then you
[46:52] can perform your project very well. So
[46:54] we will see what is the social impact
[46:56] from this.
[46:59] So the social impact of this project is
[47:02] extremely important because heart
[47:04] disease remains one of the leading
[47:06] causes of death nowadays. So uh
[47:12] so every year we see millions of people
[47:15] lost their lives due to cardio cardiac
[47:20] disease right. So many of which can
[47:24] prevent through early detection and
[47:26] awareness. So prevention is better than
[47:28] cure. So before getting any diseases,
[47:33] heart disease, we have to find the what
[47:35] are the prevention causes. So for that
[47:38] you can analyze from this heart disease
[47:40] data set and you can find the solutions
[47:43] you can prevent not to get heart
[47:45] diseases.
[47:47] So I'll give an example. For example,
[47:52] through data analysis, we may discover
[47:54] that uh like patient have a certain age
[47:58] with high cholesterol and high blood
[48:00] pressure and diabetes are more likely to
[48:04] develop heart related complications. So
[48:07] this information allows doctors to focus
[48:09] on
[48:11] like prevent care and rather than
[48:14] receive treatment for them. So the
[48:18] project can also help like uh the
[48:20] government healthare departments like
[48:24] they can design awareness campaign
[48:26] targeting uh like vulnerable populations
[48:30] and instead of treating every patient
[48:32] the same way they'll help us to provide
[48:36] like personalized treatment plans. So
[48:39] based on risk profiles generated from
[48:42] this status. So
[48:45] overall the social impact may include
[48:47] like early disease detection you can
[48:50] like uh detect disease early so that you
[48:54] can prevent from the getting disease. So
[48:57] it used to reduce mortality rates and
[49:01] also better healthcare accessibility
[49:03] will be there and you can like find the
[49:08] social impact like you can increase
[49:10] public awareness about heart health like
[49:12] and also you can take a datadriven
[49:15] healthare policies like before getting
[49:17] the heart diseases. So this is the
[49:20] social impact which we'll be getting
[49:22] from this data set and from this use
[49:24] case if we implement this. So next we'll
[49:28] see how it affects the business model.
[49:31] So from a business p perspective we have
[49:34] to think here as a business plan. So
[49:36] this social impacts like they can
[49:39] transform into a opportunities of
[49:41] costsaving revenue growth and market
[49:44] differentiation within the healthcare
[49:46] industries. So you will find a existing
[49:50] solutions to prevent this healthcare
[49:52] diseases.
[49:54] You can find your own ex uh solutions
[49:58] also by taking reference of this
[50:00] existing solutions. And for this
[50:03] recommended technologies are like you
[50:05] can use data science, BI tools, Tableau,
[50:07] Python, MySQL etc. In this project in
[50:11] this data visualization you'll be using
[50:13] Tableau. So you can do some research on
[50:16] this and you can find better insights
[50:21] from the uh existing solutions and
[50:24] existing insights. So I prefer you like
[50:28] go to some research papers and do some
[50:30] research on what is what and then you
[50:33] take a project and perform your
[50:36] analysis. So that will help you to build
[50:39] a better and valuable project.
[50:42] So this is about hardis analysis use
[50:45] case and we'll move on to another use
[50:48] case. Second use case is like strategic
[50:50] product placement analysis. So in this
[50:53] we'll be having a product related data
[50:56] set. So here you have to think like a a
[51:00] retail company like uh you have a retail
[51:03] company you want to understand better
[51:06] like how products are positioned and how
[51:09] they are placed in your stores. So how
[51:11] they are marketing and how they affects
[51:13] the sales and customer choices. So they
[51:16] have collected a v variety of like
[51:18] product data, sales data and store data
[51:22] and they also included sales numbers,
[51:25] product placement details and also
[51:27] information about customer demographics.
[51:29] So however they are struggling to clear
[51:32] like see the relationship between
[51:34] product and sales and the placements. So
[51:37] between these factors and they need to
[51:39] help making sense of the data. Through
[51:42] this data they can analyze and they can
[51:44] choose a product placements
[51:47] and sales like how they can increase the
[51:50] number of sales.
[51:53] Are you getting what I'm telling?
[51:57] I hope you understand the first use case
[52:00] and also you got an idea about the
[52:02] second use case which is strategic
[52:04] product placement.
[52:07] So here we'll be using a Tableau
[52:10] analytics tool. So if you choose this
[52:14] project you can convert your raw data
[52:16] into interactive and easy to understand
[52:19] visualizations you can form and this
[52:22] tools helps like uncover a reason behind
[52:25] why buying behavior is changing and
[52:27] highlighting which tackles work best. So
[52:30] which projects like product strategies
[52:33] work work best and which product
[52:35] placement details will work best you can
[52:38] analyze from this use case. So the
[52:41] ultimate goal of this project is like to
[52:44] provide a company a clear and datab
[52:47] recommendations like so that they can
[52:50] improve how they position their products
[52:52] with better insights. They can tailor
[52:55] their marketing and placement strategies
[52:57] like to attract more customers and also
[53:00] they can boost their sales and they can
[53:03] grow grow their business. For example,
[53:06] if you go to a supermarket, you will see
[53:09] bread and milk is placed side by side.
[53:12] Why? because the like they analyze the
[53:15] customer behavior and they get bread and
[53:18] milk side by side because the customer
[53:21] who comes for the milk will also buy the
[53:24] bread. This is a strategy of a business.
[53:27] So this is the solution from the
[53:29] problem. So you can take a one problem
[53:33] statement and you can find a solution
[53:36] for that by using this data set and by
[53:38] analyze. So this is what happens when
[53:40] you choose a use case and build a
[53:42] project like every project should have a
[53:45] problem statement. First you have to
[53:47] work on project problem statement then
[53:50] you have to find the related solution
[53:52] for the problem statement. So this is a
[53:55] use case too.
[53:58] So from this use case you can see social
[54:00] impacts like you can improve customer
[54:03] experience so that the retail uh like
[54:06] marketing
[54:08] like stores can improve the customer
[54:11] experience like placing the products in
[54:14] a particular order and you for a best
[54:18] example is like demats smart bazars and
[54:22] all. So you they can increase to
[54:25] preferred products and also datadriven
[54:28] customer environment will be there is
[54:30] the these are the social impacts you
[54:32] will find from this use case to which is
[54:35] strategic product placement analysis.
[54:39] Here you will be unveiling the sales
[54:41] impact with Tableau visualizations. So
[54:44] next we'll come to business model and
[54:45] impact. So if the higher sales in
[54:49] business you have to think like a
[54:50] business owner. So in that perspective
[54:54] you can see if the sales are higher
[54:57] higher and you can see automatically
[54:59] revenue grows. So you can target the
[55:02] marketing and the reduce wastage of the
[55:05] products. So whichever the customers are
[55:08] most buying products that products only
[55:11] you can sell.
[55:13] So that will reduce the wastage of you
[55:16] bringing the products into the markets.
[55:18] So that is a business model and impact
[55:20] you will find from the this use case. So
[55:24] and faster and smarter decision making
[55:27] will be there. So while you see a lots
[55:31] of data in a one place without structure
[55:35] will you find any like
[55:38] any insights?
[55:41] We won't find any insights. So due to
[55:44] that we will visualize that data and
[55:46] we'll like create a charts and we'll be
[55:49] analyzing and we'll be predicting the
[55:52] patterns and insights from that
[55:54] visualizations. So we will we can work
[55:57] on this in a better way. So there are
[55:59] few existing solutions for this problem.
[56:02] So if you are uh plan software it is a
[56:07] retail uh space planning they will plan
[56:10] the product uh placement strategies. So
[56:13] and also in store behavior analytics
[56:15] tools tools will be there like retail
[56:18] mix and shop track and footfall g.
[56:22] So these are the existing solutions you
[56:25] can find your solution if you want and
[56:27] you can take refer from this solutions
[56:29] and you can build a project efficiently.
[56:33] And you can recommended technologies for
[56:35] this project are data science, PI tools
[56:38] like Tableau, Python, MySQL etc.
[56:42] So you can do some research on this like
[56:46] you you can take reference like
[56:49] publications uh which are already
[56:51] published papers will be there from that
[56:54] can do some research and you can build
[56:55] your project and you can find your own
[56:58] solutions.
[57:01] I hope you understand this.
[57:10] Did you guys understand this two use
[57:12] cases?
[57:22] As you guys are with me, I'll move on to
[57:25] the third use case.
[57:29] We are in the third use case
[57:32] which is India agriculture crop
[57:35] production analysis. So in this what we
[57:38] do is we'll take a India agriculture
[57:41] data set like how the agriculture is
[57:44] going on
[57:46] and uh because India plays a major role
[57:49] in agriculture.
[57:52] So it contributes significantly to GDP
[57:56] and employment and also crop production
[57:59] is a key component of Indian
[58:01] agriculture. So with diverse crops grown
[58:04] across different regions in India, we
[58:06] can analyze crop production data so that
[58:10] we can provide a valuable insights from
[58:13] policy makers, farmers and stakeholders
[58:16] to optimize like agriculture practices.
[58:19] If you want like we can go near farmers
[58:22] and we can ask farmers like what is the
[58:25] secret of uh production and we can
[58:30] produce like good crops by using
[58:32] pesticides and all. So you can take any
[58:36] uh problem statement on this use case
[58:38] and you can work on that which will be
[58:40] helpful.
[58:42] So uh like we can use this uh data set
[58:47] for ensuring food security also and also
[58:51] enhancing productivity by analyzing this
[58:54] Indian agriculture crop production data
[58:56] set. You can provide insights like how
[58:59] we can enhance the productivity and how
[59:01] we can provide produce a good and
[59:05] organic foods. So nowadays everything is
[59:09] chemicals. So how you can produce uh
[59:12] non-chemical foods like organic foods
[59:15] you can analyze that and you can take a
[59:18] problem statement accordingly and you
[59:20] can find the solution from this data set
[59:23] like you will be getting a insights like
[59:27] how is a crop production and all
[59:31] so the Indian agriculture crop
[59:33] production you can analyze valuable
[59:36] insights also and you can recommend a
[59:40] stakeholders involved in Indian
[59:42] agriculture so that they can leverage
[59:44] datadriven analysis and also advanced
[59:46] technologies to increase the production.
[59:50] So this product like this project uh you
[59:54] can contribute by using this project you
[59:56] can contribute to the development of
[59:59] evidence-based policies and also you can
[01:00:01] practice a sustainable and resilient
[01:00:04] crop production in India.
[01:00:08] So we'll move on to what is the social
[01:00:10] impact we'll have if we take this use
[01:00:13] case and we'll build a visuals. So you
[01:00:16] can ensure food security by
[01:00:18] understanding food availability and
[01:00:20] distribution
[01:00:21] by production and also you can uh
[01:00:23] support rural livelihoods and by
[01:00:28] providing insights into income levels
[01:00:30] and also you can provide a employment
[01:00:33] opportunities also and you can
[01:00:37] provide a social economic development
[01:00:40] and also you can use our promotion
[01:00:43] sustainable practices and also
[01:00:45] environmental sustainable by assessing
[01:00:49] environmental impact. So business model
[01:00:52] is like it helps stakeholders to
[01:00:55] anticipate market dynamics and also they
[01:00:58] can manage risks
[01:01:00] caused by using chemicals and all and
[01:01:03] also they can drive and involve a
[01:01:07] technology adaptions in agriculture like
[01:01:10] IoT in agriculture
[01:01:13] like that we can implement few
[01:01:15] technology productions adoptions in
[01:01:18] agriculture to increase
[01:01:20] productivity growth. For that you can
[01:01:22] help them analyze this data set and
[01:01:25] provide the analyzed insights from them
[01:01:28] from this data set so that they can give
[01:01:31] a proper solutions
[01:01:33] and also optimize supply chain
[01:01:35] operations. So this tells like we
[01:01:39] Yes sir. Okay.
[01:01:45] Okay sir.
[01:02:08] Sorry for the inconvenience. So I hope
[01:02:10] you can see it clearly.
[01:02:29] So ultimately by using this uh use case
[01:02:33] you can contribute towards a national
[01:02:35] economic growth also. So there are some
[01:02:38] existing solutions also and your
[01:02:40] recommended technology is teler can use
[01:02:43] data science for this and also BI tools
[01:02:45] like Tableau, Python and also MySQL etc.
[01:02:50] You can do some research on this by
[01:02:51] referring the research papers on Indian
[01:02:54] agriculture crop productivity. You'll
[01:02:56] have so many research papers. You can do
[01:02:58] some research and you can build the like
[01:03:01] best solution like insightful visuals
[01:03:03] from this data set. I hope you
[01:03:06] understood this use case.
[01:03:15] Okay.
[01:03:17] Next we'll move on to
[01:03:21] next use case.
[01:03:24] So this is a fourth use case with
[01:03:27] dollaring behavior and consumer trends.
[01:03:30] Here you can find a structured analysis
[01:03:33] of choose choices of uh choices of food
[01:03:37] and habits. So we know in these days
[01:03:41] everyone orders food. So we like food
[01:03:45] ordering like food delivery platforms
[01:03:48] are more nowadays. So they want to
[01:03:50] analyze like what is the food behavior
[01:03:52] of the customers like what is the
[01:03:54] presence they will choose and
[01:03:58] what is their like how much they can
[01:04:01] spend and all what is the satisfaction
[01:04:04] level of the customers. So to analyze
[01:04:06] all this you can use this use case.
[01:04:10] So
[01:04:12] we have so many apps of food and also we
[01:04:15] have websites food delivery websites and
[01:04:18] food delivery apps. So from those from
[01:04:21] those apps and websites this data is
[01:04:23] collected so that you can analyze so
[01:04:26] much from this data set like purchasing
[01:04:29] habits like what is the customer's
[01:04:30] purchasing habits like how frequently
[01:04:32] they purchase and which food they
[01:04:35] purchase frequently and also their quiz
[01:04:38] preferences. So quins are like south
[01:04:40] Indian, north Indian, Chinese and all
[01:04:44] and they can analyze patterns and what
[01:04:49] is their spending patterns like how much
[01:04:52] cost they are spending on this food
[01:04:54] delivery apps and all and what is their
[01:04:57] satisfaction level of food. So all this
[01:05:01] you can analyze from this use case. So,
[01:05:04] so this project is aiming like analyze
[01:05:07] food ordering data using Tableau. So,
[01:05:11] this uncovers actionable insights into a
[01:05:14] consumer choices and also emerging
[01:05:17] trends in the food tech industries.
[01:05:20] So through this tabular analysis like we
[01:05:23] can uh derive a complex landscape of
[01:05:27] food ordering behaviors and unbuilding
[01:05:29] patterns across platforms, demographics,
[01:05:32] question performances and time of day
[01:05:35] habits and pricing sensitivity and also
[01:05:38] delivery expectations
[01:05:40] like we'll have a delivery time. So that
[01:05:43] also you can analyze like 10 minutes, 20
[01:05:46] minutes, 40 minutes, how far the food is
[01:05:49] coming from. So all these things you can
[01:05:51] analyze and you can choose a like proper
[01:05:54] problem statement and you can provide a
[01:05:56] solutions for the pro problem statement.
[01:06:00] So what happens if you leverage this
[01:06:02] data visualization? So you can give
[01:06:04] restaurants, food aggregations and also
[01:06:07] policy makers like with evidence- based
[01:06:10] insights. They can enhance customer
[01:06:12] experience and you can write a business
[01:06:15] group. It helps to drive a business
[01:06:18] growth.
[01:06:21] So
[01:06:23] for example like I'll tell an example if
[01:06:26] data shows that consumers are increasing
[01:06:29] orders unhealthy fast food items. So
[01:06:32] public health organizations can design
[01:06:34] awareness campaigns like promotion
[01:06:37] promoting
[01:06:39] alternatives and also similarly if the
[01:06:42] local restaurants can understand
[01:06:44] customer senses and also they can modify
[01:06:46] their menu accordingly. So all these
[01:06:48] patterns you can derive from this data
[01:06:50] set. So this project contributes to
[01:06:55] better understanding of consumer food
[01:06:57] habits and also improved customer
[01:07:00] satisfaction and also support for local
[01:07:02] food businesses and promotion of
[01:07:07] healthier eating patterns and also they
[01:07:10] can enhance food accessibility business
[01:07:13] model and also impact
[01:07:16] on business.
[01:07:20] I hope you got this.
[01:07:27] Then move on to next slide.
[01:07:32] So we'll see what are the social
[01:07:34] impacts,
[01:07:37] business model and existing solutions
[01:07:40] and recommended technology stacks for
[01:07:42] this use case and references
[01:07:47] like you can do some research on food
[01:07:49] ordering behavior and consumer trends
[01:07:52] like papers you have like research
[01:07:54] papers you can do some research and you
[01:07:57] can find a solution and also you can
[01:08:00] find a what is the problem lacking
[01:08:02] behind in this data set and you can find
[01:08:05] the solution for that problem statement.
[01:08:08] So in social impact like you can use
[01:08:11] tables for food ordering like behavior
[01:08:14] analysis that can lead to a positive
[01:08:17] social outcomes and you can improve a
[01:08:20] affordable and promotional healthier
[01:08:23] food choices for the people and also you
[01:08:27] can empower local restaurants and also
[01:08:29] you can enable datadriven public health
[01:08:32] interventions like around
[01:08:36] dietary patterns. So in business model
[01:08:40] like in business perspective what
[01:08:42] happens is food ordering analytics uh
[01:08:45] translates into opportunities for
[01:08:47] dynamic pricing options. You can change
[01:08:50] pricing options accordingly and also you
[01:08:53] can target
[01:08:55] uh like targeted customers by using
[01:08:58] promotions and all. And also
[01:09:02] you can use our platform partnership
[01:09:04] decisions and menu engineering and also
[01:09:07] improved deliveries SLAS and also
[01:09:11] directly driving revenue growth. This
[01:09:14] all will directly increase your revenue
[01:09:16] growth and also customer retention for
[01:09:19] food aggregations and restaurants. This
[01:09:21] will help the
[01:09:23] restaurants and partners partners who
[01:09:27] are partnered with this food businesses
[01:09:29] and all. So that is a business model and
[01:09:33] there are some existing solutions also
[01:09:35] you can through it in
[01:09:38] Google you will find according to that
[01:09:40] you can choose and you you will be
[01:09:44] recommended with technology stacks like
[01:09:46] data science BI tools like Tableau and
[01:09:49] also you can Python MySQL pandas C1 etc
[01:09:54] and there are some reference papers also
[01:09:56] you can refer them
[01:09:59] so we'll move on to next slide Okay.
[01:10:03] So this is a simp use case where we'll
[01:10:07] be working on visualization tool for
[01:10:10] electric vehicle charge and range
[01:10:13] analysis.
[01:10:15] So nowadays EVs are most popular.
[01:10:20] So that is powered by an electric motor.
[01:10:23] So you can drive a uh electric motor
[01:10:28] using electricity battery
[01:10:31] and it is capable of being charged from
[01:10:34] an external sources right. So and it has
[01:10:38] an electric motor instead of an internal
[01:10:41] combustion engine. So this EVs is not
[01:10:44] new but it has been received
[01:10:47] significantly more attention in recent
[01:10:49] years only. So it has implemented
[01:10:52] uh like before only but it is popular
[01:10:55] nowadays. So advances in both EV
[01:10:58] analytics and battery technologies will
[01:11:01] lead us to increase the automative
[01:11:03] market share. So the new EVs are
[01:11:07] combined with an electrical storage and
[01:11:10] operation systems also with electronic
[01:11:14] sensors. You will be installed with
[01:11:16] electronic sensors, controllers and
[01:11:18] actuators. uh you will be integrated
[01:11:21] closely in software
[01:11:24] and secure data transformation will be
[01:11:26] there and also you will be using a
[01:11:29] comprehensive transformation solutions
[01:11:32] for this. Based upon this you can take a
[01:11:34] problem statement and you can find a
[01:11:37] solution for this.
[01:11:39] So in this the major issue is what
[01:11:41] happening is charging time and
[01:11:45] range of battery. So you can find
[01:11:48] solution further by analyzing this data
[01:11:50] set. So why the charging charging time
[01:11:53] is less and why the battery range is
[01:11:55] less. So you can find a solutions to
[01:11:58] increase the charging time and range of
[01:12:02] battery. So if we can get or provide
[01:12:05] insights of this particular car, we'll
[01:12:08] be using that analysis to do a better
[01:12:11] next time. we can implement a better EVs
[01:12:16] which are then EVs which are present
[01:12:19] nowadays.
[01:12:20] For that you can use this data data set
[01:12:23] or this use case.
[01:12:27] So we move on to next
[01:12:30] they'll see what is the social impact
[01:12:32] and business model and existing
[01:12:35] solutions. So social impact is like by
[01:12:39] solving this problem you'll be helping
[01:12:42] like biggest issue in to solve biggest
[01:12:45] issue in EV markets like which is
[01:12:47] battery and charging issues. So more
[01:12:51] people will understand about EVs instead
[01:12:54] of IC's and they like shift from shift
[01:12:59] from IC to EVs so that pollution will
[01:13:02] decrease.
[01:13:04] So everyone will equipped to the EV
[01:13:08] vehicles
[01:13:10] which will which will lead to the
[01:13:12] decrease of diesel and petrol. So we
[01:13:15] have a business model here. So in this
[01:13:19] like uh you can provide insights for a
[01:13:22] car or battery manufacturers to uh give
[01:13:26] a better uh battery life and so that
[01:13:30] based upon that insights they will
[01:13:32] better their battery more and they'll
[01:13:34] manufacture accordingly.
[01:13:37] So you can provide insights like uh
[01:13:40] people who are using the EV thinking to
[01:13:43] enter in EV market will uh impress
[01:13:46] through your insights and they will
[01:13:48] adopt this EVs. There are some existing
[01:13:52] solutions also you can
[01:13:54] and recommendation technology stack is
[01:13:58] all this data science diodes Tableau
[01:14:00] Python MySQL and etc. We will be using
[01:14:05] Tableau here. There are more like
[01:14:08] reference papers on this. You can refer
[01:14:12] this will be best who are interested in
[01:14:15] EV market and all EVs and all you can
[01:14:19] choose this.
[01:14:25] We'll move on to sixth use case which is
[01:14:29] heritage treasures and in-depth analysis
[01:14:32] of UNISCO world heritage sites in table.
[01:14:38] So UNISCO world heritage sites are vital
[01:14:42] cultural and historical and they are
[01:14:44] natural landmarks like that preserve
[01:14:47] global heritage and promote tourisms. So
[01:14:51] what you have to understand is how they
[01:14:53] are distributed this heritages and how
[01:14:56] they are
[01:14:58] preserved the statues and tourism impact
[01:15:02] how tourism impacts the requires and
[01:15:05] comprehensive analysis what you can do
[01:15:08] on this. So you can figure it out you
[01:15:11] can find a problem statement
[01:15:12] accordingly. So the challenges here are
[01:15:15] like uh integrating data from multiple
[01:15:18] sources is the most challenging task
[01:15:22] such as site information, visitor states
[01:15:25] and a preservation efforts of the
[01:15:28] preserve the heritages and all. So to
[01:15:32] identify meaningful trends you have to
[01:15:34] use Tableau visualization
[01:15:36] analytics tool. So it will help make
[01:15:39] sense of all complex data you have. you
[01:15:43] can visualize easily.
[01:15:47] So
[01:15:48] the key issues include like uh assessing
[01:15:52] like tourisms impact on heritage sites
[01:15:56] and also tracking conservation effects
[01:15:59] and identifying regional with the
[01:16:02] greatest need for preservation. So TAB
[01:16:04] can provide real time insights to
[01:16:07] support better decision making. you can
[01:16:09] analyze this data set and you can give a
[01:16:11] better insights that can derive a better
[01:16:16] decision making.
[01:16:20] So by using Tableau for data analysis
[01:16:23] for this data set. So stakeholders can
[01:16:26] better manage and protect UNESCO world
[01:16:30] heritage sites and they can help in
[01:16:34] driving sustainable tourism and also
[01:16:36] ensure the preservation of cultural
[01:16:39] heritage for future generations. We are
[01:16:42] seeing the heritages. We want our
[01:16:44] childrens also to see them. So how we
[01:16:47] can preserve them? You can identify from
[01:16:49] this use case
[01:16:51] you can find a problem statement
[01:16:53] accordingly and you can find the
[01:16:55] solutions using this data set.
[01:17:00] So we'll move on to next slide which is
[01:17:02] how it impacts the social.
[01:17:05] So it promotes awareness like how to
[01:17:09] preserve UNISCO world heritage sites and
[01:17:12] also it supports the sustainable tourism
[01:17:15] and
[01:17:16] conservation efforts.
[01:17:19] So how business models impact business.
[01:17:22] So
[01:17:24] uh we'll provide actionable insights for
[01:17:26] tourism and conservation sectors. So
[01:17:29] from that insights they will find a
[01:17:32] business solution for that. So they can
[01:17:35] enhance this uh insights that we derived
[01:17:39] from this data set can uh can help make
[01:17:45] decisions better to site management and
[01:17:49] promotions.
[01:17:53] So recommended technology stacks are
[01:17:56] data science, PI tool, Tableau, Python,
[01:17:59] MySQL
[01:18:01] on this also there are some existing
[01:18:02] solutions
[01:18:04] who are interested in heritage turing
[01:18:07] and all you can choose this use case and
[01:18:11] find a better problem statement and
[01:18:14] solution for that problem statement. So
[01:18:17] we'll move on to next slide. We'll move
[01:18:19] on to use case seven.
[01:18:24] So which is visualizing housing market
[01:18:27] trends and analyzing like of sales price
[01:18:31] prices and features using
[01:18:34] table. So this data set helps us like
[01:18:38] how housing market is cons constantly
[01:18:41] influencing by a variety of factors like
[01:18:45] factors like consumer preferences and
[01:18:47] how the governments are preferring and
[01:18:49] economic conditions how the economic
[01:18:51] conditions based upon the economic
[01:18:52] conditions also like how prices are
[01:18:56] increasing or decreasing and also
[01:18:57] seasonal trends.
[01:19:00] Uh through this we can analyze insects.
[01:19:04] code
[01:19:06] and also real estate professionals
[01:19:09] and investors must analyze housing data
[01:19:12] such as sale pricing and also features
[01:19:16] and what is the market trends in real
[01:19:18] time. So they can perform their best
[01:19:22] according to this analysis. So the
[01:19:24] challenges in this is like to integrate
[01:19:27] data from multiple sources. We have to
[01:19:29] take data from the multiple sources to
[01:19:31] analyze this visualizing housing market
[01:19:34] trends such as we'll take from property
[01:19:38] listings and sales prices and market
[01:19:42] trends and consumer preferences. We'll
[01:19:45] collect all this data to uncover
[01:19:47] meaningful insights and patterns
[01:19:51] so that we'll uh analyze
[01:19:54] using Tableau. We'll do advanced data
[01:19:57] visualization and
[01:20:00] we'll find the insights. We'll make
[01:20:03] sense of this complex data using data
[01:20:06] vization tools. We'll use advanced
[01:20:08] visualization tools. So
[01:20:11] key insights like uh sorry key issues
[01:20:15] like include like how we can understand
[01:20:17] price fluctuations. So it is increased
[01:20:20] so much. So from the uh old days to
[01:20:24] nowadays the house prices were increased
[01:20:26] so much so drastically it was increased.
[01:20:29] Why that happened? How that happened?
[01:20:32] You can predict that and you can predict
[01:20:35] market intense like what is the market
[01:20:38] pricing in 2008? What is the market
[01:20:41] pricing in 2026?
[01:20:44] You can predict that using this data set
[01:20:49] and how to assess like different
[01:20:52] property features how they impact sales
[01:20:55] prices
[01:20:57] and also you can find a robust solution
[01:21:00] with it.
[01:21:01] So it can provide a real time insights
[01:21:04] when you take a real time data which
[01:21:06] supports better decision making. So by
[01:21:09] leveraging Tableau for in-depth analysis
[01:21:13] like you can help our real estate
[01:21:15] professionals they can optimize
[01:21:19] property pricing according to the market
[01:21:22] and define marketing strategies and also
[01:21:24] they can engage better with potential
[01:21:26] buyers
[01:21:28] and also ultimately you can drive sales
[01:21:32] and enhance market positioning. So this
[01:21:36] is what about visualizing
[01:21:38] housing market trends. Uh this helps us
[01:21:41] to analyze sales prices like features
[01:21:45] using
[01:21:47] tab. So we'll move on to next.
[01:21:54] So we'll see what is the social impact.
[01:21:57] So by providing this insights into house
[01:21:59] marketing trends and consumer
[01:22:01] preferences, you will help a businesses
[01:22:04] and homeowners and also real estates
[01:22:07] professionals to make a informed
[01:22:09] decision based on the market data and
[01:22:12] also business model is like they can
[01:22:15] offer a valuable insights for real
[01:22:18] estate professionals and developers and
[01:22:20] also investors to adjust pricing
[01:22:22] strategies according to the market
[01:22:24] pricing strategies. So they can help
[01:22:26] refine marketing efforts and also
[01:22:28] develop targeting
[01:22:30] uh like strategies based on the market
[01:22:33] trends and property features also.
[01:22:36] So there are few existing solutions like
[01:22:40] uh rela.com you can go there and check
[01:22:43] and zillow
[01:22:46] and recommended technology stack is data
[01:22:48] science viol
[01:22:50] python ISQL and social model analytics.
[01:22:58] So you have a reference uh like papers
[01:23:01] also you can refer them for this.
[01:23:06] So I'll give a simple example why you
[01:23:09] should choose this. So
[01:23:14] this data may reveal that uh homes near
[01:23:17] schools and transportation hubs command
[01:23:20] higher prices. Why? Because schools and
[01:23:22] transportations are near
[01:23:26] they are travel friendly. So business uh
[01:23:29] benefits they may include is like they
[01:23:32] may get like they can
[01:23:35] uh improve investment decisions and also
[01:23:38] better pricing strategies they can keep
[01:23:40] and also they can enhance marketing
[01:23:42] effectiveness and also they can increase
[01:23:45] a profitability and also existing
[01:23:48] solutions. They can put a more price for
[01:23:51] the houses
[01:23:53] the houses which are near schools and
[01:23:54] offices. So we we saw this real time in
[01:23:59] our situ like in our daily situations.
[01:24:02] We'll see this.
[01:24:06] So we'll move on to next slide.
[01:24:10] So this is use case eight. So where
[01:24:14] we'll be
[01:24:16] exploring the electricity consumption
[01:24:19] patterns using electricity data. So
[01:24:24] every day in our homes we'll use
[01:24:25] electricity. So we want to know what is
[01:24:28] the patterns we can find from the
[01:24:30] electricity. So electricity consumption
[01:24:33] refers to the amount of electricity
[01:24:36] energy that is consumed by particular
[01:24:39] entity like such as householder a
[01:24:42] business or an entire country like we
[01:24:46] will be starting from the householders
[01:24:48] business and also entire country. How
[01:24:50] much of the electricity is consumed
[01:24:52] daily hourly that all that we can
[01:24:56] analyze using this data set
[01:25:01] and also electricity kilow are we can
[01:25:05] measure using this data sets. So the
[01:25:08] amount of electricity consumed will be
[01:25:10] dependent on the various factors right.
[01:25:12] So it can it will be including like
[01:25:14] population how much population we have
[01:25:16] in our home or city or like in our
[01:25:19] country.
[01:25:21] So electricity consumption will be
[01:25:23] dependent on that factors and also
[01:25:25] economic activities we do like using
[01:25:28] fans and all. So like
[01:25:32] in house uh we have fans, ACS and all
[01:25:36] based upon that also you can analyze the
[01:25:40] electricity cost. So the availability of
[01:25:44] electricity appliances we have and also
[01:25:47] availability of alternative energy
[01:25:49] sources we have in our homes like EV
[01:25:53] vehicles and all. So an electricity
[01:25:57] consumption analysis can be performed at
[01:26:00] different levels like individual homes,
[01:26:02] businesses, cities or countries. So this
[01:26:05] analysis may involve the collection of
[01:26:07] data on electricity usage over time. How
[01:26:10] much usage of electricity is uh we are
[01:26:14] consuming over years over months or as
[01:26:19] the daily monthly or yearly electricity
[01:26:21] consumption we can analyze.
[01:26:24] So this data can analyze and identify
[01:26:27] trends and patterns in electricity usage
[01:26:30] such as peak usage, peak usage times
[01:26:34] like when we are using a highest
[01:26:37] electricity like morning, evening,
[01:26:39] afternoon or what is the peak usage
[01:26:41] time? We can analyze that and also how
[01:26:44] the impact of weather conditions. If we
[01:26:48] see few sometimes if we get rains the
[01:26:51] electricity will be off due to rains
[01:26:55] that time we don't consume any
[01:26:56] electricity so that time the electricity
[01:27:00] consumption will be less so in summers
[01:27:02] we use more electricity because it will
[01:27:05] be hot in rainy seasons we'll use less
[01:27:07] electricity because we don't use AC and
[01:27:10] all in rainy and winter season most of
[01:27:14] mostly we'll use AC coolers summer
[01:27:17] season. So you can analyze according to
[01:27:21] the times and seasons
[01:27:23] and also this supports energy efficiency
[01:27:28] and conservation efforts as well as a
[01:27:31] development of renewable energy
[01:27:33] resources
[01:27:36] and also it results in analyszis like
[01:27:39] how inform policy decisions how they can
[01:27:43] implement a policy decisions based on
[01:27:45] the electricity consumption. This can
[01:27:47] guide to design a energy efficient
[01:27:50] buildings and appliances. Uh like
[01:27:54] and also we can like perform energy
[01:27:58] saving practices in our homes. When we
[01:28:02] are inside our home we can on our fan or
[01:28:05] light when we are not present we can
[01:28:06] offer
[01:28:08] like we can't waste the electricity so
[01:28:11] that we can give to our future child
[01:28:13] also.
[01:28:15] So all this we have to analyze and this
[01:28:18] helps our businesses and also
[01:28:20] householders.
[01:28:22] We'll be analyzing business business
[01:28:24] perspective and householders
[01:28:25] perspectives overall and electricity
[01:28:28] consumption analysis is an important
[01:28:30] tool to like we are the responsible to
[01:28:34] save our renewable energy resources. So
[01:28:36] for that we have to analyze this data
[01:28:39] and we have to give a
[01:28:42] perfect uh decisions to consume like how
[01:28:46] we can consume less energy electricity
[01:28:50] energy.
[01:28:52] So that can reduce a greenhouse gas and
[01:28:56] also create more sustainable energy
[01:29:00] further.
[01:29:02] If we use more electricity now, we can't
[01:29:05] give to our future child because
[01:29:09] electricity is a renewable energy
[01:29:11] resource.
[01:29:15] We'll move on to next use case. So
[01:29:17] before moving to next next use case,
[01:29:20] we'll see what is the social impact and
[01:29:22] business model impact and existing
[01:29:24] solutions for this electricity
[01:29:26] conduction.
[01:29:28] So the social impact may be like you can
[01:29:32] provide organizations with the
[01:29:34] information they need to make more
[01:29:37] sustainable and socially responsible
[01:29:39] decisions which can ultimately benefit
[01:29:42] the society as a whole that will benefit
[01:29:46] the society.
[01:29:47] So business model is like uh like
[01:29:50] generate revenue by providing services
[01:29:53] to the electricity departments and also
[01:29:56] they get tie up with governments and can
[01:29:59] sell your product to private companies
[01:30:01] also. This will be impacting the
[01:30:05] business models. There are existing
[01:30:08] solutions and recommended technology
[01:30:10] stack is data science pad taboo python
[01:30:13] myql etc. There are uh IA uh different
[01:30:20] papers you can explore them and based
[01:30:24] upon that you can find a problem
[01:30:25] statement and you can find solution for
[01:30:27] that problem statement using data
[01:30:29] visualization using a powerful data
[01:30:32] visualization tool tablet.
[01:30:36] We'll see next use case which is the
[01:30:38] ninth use case.
[01:30:42] This is uh cosmetic insights and
[01:30:45] navigation
[01:30:47] uh sorry navigating cosmetic trends and
[01:30:49] consumer insights with tablet. So
[01:30:53] like we see most of the cosmetic
[01:30:55] products are produced nowadays.
[01:30:58] There are brands like purple and all
[01:31:01] which produces cosmetic products like
[01:31:04] based upon the skin type dusky and all.
[01:31:09] So you have all the factors in this data
[01:31:12] set. You'll be analyzing like product
[01:31:15] performance of the product like dating
[01:31:18] and also pricing like if you want to use
[01:31:23] a branded products it will cost high. So
[01:31:28] b b b b b b b b b b b b b b b b b b b b
[01:31:29] b b b b b b b b b b b b b b b b b b b b
[01:31:29] based upon that you can choose premium
[01:31:31] product or low budget product or you can
[01:31:34] analyze all this insights by using this
[01:31:38] use case and also what is a product
[01:31:41] which is suitable for your skin
[01:31:44] you can find by analyzing this product
[01:31:47] sorry this data set
[01:31:51] they support
[01:31:52] datadriven decisions.
[01:31:55] So
[01:31:57] like it will be using for the
[01:32:00] competators of beauty industry
[01:32:05] to better their businesses than the
[01:32:08] other competitors
[01:32:10] and also they can understand the brand
[01:32:12] rankings and also product demand skin
[01:32:15] type suitability.
[01:32:17] This has become essential for improving
[01:32:20] customer satisfaction and also marketing
[01:32:23] positions.
[01:32:25] So this pro uh like this project
[01:32:28] leverages Tableau like you can transform
[01:32:31] cosmetic data set into a interactive
[01:32:33] visualizations and also you can build a
[01:32:35] dashboard and stories. Uh they can
[01:32:38] provide a meaningful insights into
[01:32:40] trends and consumer behaviors like how
[01:32:42] the consumers will think about the
[01:32:45] cosmetic products. So this visual
[01:32:47] analytics helps a stakeholders
[01:32:51] so that they can quickly identify
[01:32:53] patterns. So they can compare products
[01:32:56] and also they can monitor performances
[01:32:58] across multiple brands and they can
[01:33:00] choose a brand.
[01:33:04] So I hope you are getting my points.
[01:33:10] So I'll be continuing. So by analyzing
[01:33:14] this data set you'll get insights like
[01:33:17] cosmetic companies can identify gaps and
[01:33:21] they can enhance product formulations
[01:33:24] and also they can optimize the pricing
[01:33:27] strategies according to their
[01:33:29] competitors they have and also they can
[01:33:31] make better decisions for future
[01:33:34] production development. based upon this
[01:33:37] which product is selling the most
[01:33:41] they can uh like manufacture those
[01:33:44] products the most using this.
[01:33:47] So for example uh if products designed
[01:33:50] for sensitive skin like consistently
[01:33:53] receiving higher rating so companies may
[01:33:56] increase investment in that category
[01:33:59] only. So they can get a business
[01:34:02] benefits like improved product
[01:34:05] development and better pricing
[01:34:07] strategies they can put and they can
[01:34:09] enhance uh their brand positioning and
[01:34:12] also they can increase sales revenue
[01:34:15] and they can uh like work on existing
[01:34:19] solutions and find a better solutions.
[01:34:21] So industry research organizations
[01:34:24] include in this are like uh statista and
[01:34:29] mental and purple all brands. So these
[01:34:32] companies they provide market
[01:34:34] intelligence and consumer trend
[01:34:36] analysis. So
[01:34:40] we'll move on to next slide.
[01:34:44] So this may impact a social socially
[01:34:50] like how they impact social investments
[01:34:53] like they can provide clear insights
[01:34:55] into like cosmetic product performances
[01:34:58] and skin suitabilities as I have
[01:35:00] mentioned before. So this helps like
[01:35:03] consumers make safer and also more
[01:35:05] informed purchasing decisions like
[01:35:08] whichever products it is suitable for
[01:35:11] this in type they can like like buy
[01:35:14] those products only. So they can
[01:35:17] increase transparency in cosmetic data
[01:35:20] like consumer trust. They can increase
[01:35:22] consumer trust and promotes awareness
[01:35:25] about like ingredients they are using in
[01:35:28] the products like you'll be analyzing
[01:35:32] and also pricing. What is the pricing of
[01:35:35] those products based upon the brands and
[01:35:38] the product time
[01:35:40] and the product efficiency can
[01:35:45] this all this can impact.
[01:35:48] Uh next we'll move on to business model
[01:35:50] impact. So they can provide actionable
[01:35:54] insights for the cosmetic brands and
[01:35:56] manufacturers to produce cosmetic brands
[01:35:59] and manufacture cosmetic products based
[01:36:02] upon the insights.
[01:36:05] So that can improve product formulations
[01:36:09] like what is the formula they are using
[01:36:11] to produce one cosmetic product
[01:36:14] whether it is suitable for like skin
[01:36:17] types or not based upon that they can
[01:36:19] change their formula and also they can
[01:36:23] put a better pricing strategies also and
[01:36:26] they can give a brand positioning
[01:36:30] and can assist uh like customers and
[01:36:34] beauty consultants.
[01:36:35] So they so that they can select their
[01:36:38] suitable products based on their skin
[01:36:41] type and brand ranking and consumer
[01:36:43] trends.
[01:36:45] We have existing solutions also for this
[01:36:48] and recommended technologies that are
[01:36:51] data science tools like Tableau, Python,
[01:36:54] MySQL etc. You have research papers on
[01:36:59] this also you can do research.
[01:37:02] We'll move on to next
[01:37:06] uh use case which is 10th use case. In
[01:37:11] this like we will be analyzing the
[01:37:15] uh like college food choices
[01:37:20] like how the college people college
[01:37:22] students have their lifestyle and their
[01:37:25] food choices and habits using data
[01:37:28] analytics.
[01:37:29] So it studies how diet and daily doains
[01:37:34] impact overall health and wellbeing of
[01:37:36] the students. So the goal is like uh to
[01:37:39] promote a healthier decision making
[01:37:42] among students.
[01:37:43] So you can use advanced analytics tools
[01:37:46] like Tableau to transform this raw data
[01:37:50] into a insights like interactive
[01:37:52] visualizations.
[01:37:54] This helps a trends and patterns easier.
[01:37:58] So this makes a complex dietary data
[01:38:02] into a simple to understand what is the
[01:38:05] college students food choices and there
[01:38:08] how that impacts on their lifestyle
[01:38:11] habits.
[01:38:13] So this is a structured data and are
[01:38:16] integrated with interactive dashboards.
[01:38:19] You can build a interactive dashboards
[01:38:21] from this data set and also stories. So
[01:38:24] you can explore data dynamically. you
[01:38:27] can use filters rules and this creates a
[01:38:32] comprehensive and userfriendly
[01:38:33] analytical system.
[01:38:36] So uh
[01:38:40] what happens is uh I'll give an example
[01:38:42] for this. So if this analytics reveals
[01:38:46] like low consumption of healthy meals
[01:38:49] and cafeterias can redesign venues and
[01:38:53] awareness programs.
[01:38:55] So
[01:38:56] This is a uh example
[01:39:01] like you can increase your fitness by
[01:39:04] taking a healthy food
[01:39:07] and you will get a nutrition from the
[01:39:10] healthier food. So key factors are like
[01:39:14] you can intake like nutrition intake
[01:39:16] will be there and you can uh like
[01:39:19] analyze the exercise like how is the
[01:39:23] student behavior if they are performing
[01:39:26] exercises and if they are not performing
[01:39:28] exercises and what are their eating
[01:39:30] habits uh are and how they affect their
[01:39:35] health. So the analysis this analysis
[01:39:38] highlights that unhealthy patterns and
[01:39:42] uh also areas for improvement. So it
[01:39:45] supports like students with a better
[01:39:48] health awareness and lifestyle choices.
[01:39:53] So we'll move on to social impact. So
[01:39:56] this promotes healthier eating habits
[01:39:58] like college students among college
[01:40:01] students this helps to intake like
[01:40:05] nutrition intake for the college
[01:40:07] students from this data.
[01:40:11] So you can increase awareness about
[01:40:13] nutrition balance. Nutrition balance is
[01:40:15] the most important thing. So most of the
[01:40:19] people uh like suffering from many
[01:40:22] disases because of low nutrition and
[01:40:25] also calorie intake if you take more
[01:40:27] calories you it you have a chance of
[01:40:30] getting diseases and also food quality
[01:40:33] you have to take a quality food all this
[01:40:35] you can analyze from this data set and
[01:40:38] how they impact the people you can find
[01:40:42] here from this data set and also it also
[01:40:46] supports improving ing physical health
[01:40:49] and mental being. If you are proper
[01:40:51] physically then that leads to mental
[01:40:54] well-being
[01:40:56] and it increases the academic
[01:40:58] performance. You can perform well in
[01:40:59] your academics. So business models are
[01:41:02] like uh optimized cafeteria operations.
[01:41:06] You can optimize cafeteria operations
[01:41:08] and also improve student satisfaction
[01:41:11] like and brand and reputation
[01:41:13] enhancement.
[01:41:15] There are existing solutions like manual
[01:41:18] food surveys and feedback forms can be
[01:41:21] given to our students and from the data
[01:41:26] you can analyze and you can track on
[01:41:28] nutritions. We'll be having a mobile
[01:41:31] applications like my fitness park and
[01:41:34] health life me you can track your
[01:41:36] nutrition intake in there. So recommend
[01:41:40] net technologies like BI tools, Tableau,
[01:41:43] Python, MySQL. We'll be having reference
[01:41:47] publications also on this. You can refer
[01:41:50] them to balance your healthy diet and to
[01:41:53] perform like to take a problem statement
[01:41:55] for this use case and to provide a
[01:41:58] effective solution for this use cases.
[01:42:00] So we come to an end. So this is the end
[01:42:03] of this
[01:42:05] use cases.
[01:42:09] I hope you all understood.
[01:42:14] Do you have any doubts
[01:42:17] on this use cases?
[01:42:21] You can text in chat box.
[01:42:28] Is everyone clear with this use cases?
[01:42:35] So
[01:42:37] if you are okay
[01:42:42] yeah I'll explain you lita how to do
[01:42:45] this project and next is that only
[01:42:50] so now we'll come to skill valid
[01:42:54] you'll be uh like choosing your project
[01:42:57] here in skill valid so this is your
[01:43:00] skill valid interface you'll be giving
[01:43:02] your credentials and login into the
[01:43:04] skill valid. After you log in, you have
[01:43:07] to go to skill valid. I hope my my
[01:43:11] screen is visible.
[01:43:14] Okay, I'll move on. So, you'll have a
[01:43:17] data analytics tab to your platform
[01:43:20] here. So, your domain, you can click on
[01:43:23] access resource here.
[01:43:26] As soon as you click on access resource,
[01:43:28] you'll be guided with the instructions
[01:43:29] to do this project.
[01:43:32] So these are the instructions.
[01:43:35] You can go through this instructions
[01:43:38] here. You can read this. So follow these
[01:43:41] guidelines to complete your program
[01:43:42] successfully. You have to read it very
[01:43:45] carefully and follow all the steps.
[01:43:49] Next, this refers to the like learning
[01:43:53] journey tab. You have to go to learning
[01:43:55] journey tab to learn how to build your
[01:43:57] project.
[01:43:59] So next step two is you have to access
[01:44:01] the courses. Here you have a courses.
[01:44:04] You have to uh like uh finish those
[01:44:07] courses. I will show first I'll explain
[01:44:11] the steps and access the project
[01:44:13] workspace.
[01:44:15] So this is a project workspace. So where
[01:44:18] you have your guided projects on which
[01:44:20] you'll be working on
[01:44:25] and you have to update uh give you
[01:44:28] updates on your uh project
[01:44:32] progress
[01:44:34] and also make sure you will submit your
[01:44:37] project documents and submission of
[01:44:40] project deliverables is most important
[01:44:42] thing. If you don't submit any one
[01:44:44] document also you'll be deducted with
[01:44:47] marks for that. So make sure you will be
[01:44:50] submitting everything and you have to
[01:44:53] attend the grand assessment
[01:44:55] and then al then it will your project
[01:44:58] will go to evaluation evaluator after
[01:45:01] evaluation finishes you will be getting
[01:45:03] your certification. So these are the
[01:45:05] steps you read it carefully and go
[01:45:08] through go through the process. So I'll
[01:45:10] move on to next step. Next step, second
[01:45:13] step which is learning journey. Here in
[01:45:16] this learning journey, you'll be having
[01:45:17] all the sessions
[01:45:19] from day one to day 15.
[01:45:24] You can access your session recordings
[01:45:26] here by clicking on this.
[01:45:29] Can click this and you can access all
[01:45:31] the session recordings
[01:45:34] here.
[01:45:40] So you will see a duration in minutes
[01:45:43] here time duration
[01:45:47] and next you'll be going to courses. You
[01:45:51] have to finish these two courses
[01:45:53] compulsory.
[01:45:56] Without finishing this you won't you'll
[01:45:59] be not going to the guided projects. So
[01:46:03] after finishing this courses, this is a
[01:46:06] beginner level uh like course which is
[01:46:09] introduction to genai learning path and
[01:46:12] this is a 100k agent blazer champions
[01:46:15] course. You have to click on access and
[01:46:18] you have to finish everything.
[01:46:21] So next click on next. You have to
[01:46:23] complete this course to go to next.
[01:46:28] So finish this course also. Click on
[01:46:31] access resource and finish all this
[01:46:35] and come to your courses
[01:46:38] and then go to group projects. All the
[01:46:41] use cases which I have explained till
[01:46:43] now which are your project titles and
[01:46:46] projects. Then you see in this group
[01:46:49] projects here all the 10 projects you'll
[01:46:52] be seeing here can see RTS analysis
[01:46:55] strategic product placement analysis
[01:46:58] India's agriculture crop production
[01:47:00] analysis and all
[01:47:03] these are the like if suppose I want to
[01:47:07] enroll to this RDS analysis I click this
[01:47:11] access resource you'll be having all
[01:47:13] your team details here in this interface
[01:47:16] I Hope my screen is visible.
[01:47:20] Okay.
[01:47:23] So here you will see the steps. First
[01:47:25] step is you have to download the data
[01:47:27] set. This is the most important and
[01:47:30] beginning step and then you have to sort
[01:47:33] the data set
[01:47:36] and you have to connect your data set
[01:47:38] with Tableau. Then you have to do a data
[01:47:41] preparation for visualization and you
[01:47:44] have to build a u like almost 8 to nine
[01:47:49] unique visualizations
[01:47:51] based upon your problem statement and
[01:47:53] the solution you are finding for that
[01:47:55] problem statement. You have to build a
[01:47:57] visualizations
[01:47:59] and also you have to design a dashboard
[01:48:02] from that visualizations you have built
[01:48:05] and story. In this story, you'll be
[01:48:08] having scenes
[01:48:11] and you you need to give a information
[01:48:13] like amount of data you have rendered to
[01:48:15] your tableau like your data set data
[01:48:18] source image and also if you have used
[01:48:22] your filters like in your dashboard or
[01:48:26] in your visualizations you have to put
[01:48:29] here like utilization of data filters
[01:48:31] you have to give your information here
[01:48:33] and if you have used your calcul
[01:48:36] calculation fields
[01:48:37] You have to give your here number of
[01:48:39] calculation fields you have used. If you
[01:48:41] are using one calculation field, you
[01:48:43] have to give one and the formula for
[01:48:46] that. And you have to give a titles of
[01:48:50] your visualizations and graphs that you
[01:48:52] have used and the number of
[01:48:54] visualizations you have used. Then it
[01:48:56] comes to a publishing part. You will be
[01:48:58] publishing your dashboard and story
[01:49:02] to a tab public
[01:49:05] and you will be doing a MD. So all this
[01:49:09] we'll be guiding through this project
[01:49:12] development session. So this is the flow
[01:49:16] for your project. Next we'll go to
[01:49:18] overview.
[01:49:20] If you have any doubts in building your
[01:49:22] project steps and all you can come here
[01:49:25] and you can refer this convince you
[01:49:28] overview.
[01:49:29] So most important thing is you have to
[01:49:32] put your demo link here and GitHub link
[01:49:36] here.
[01:49:37] So without demo and GitHub link you'll
[01:49:40] be not getting bugs.
[01:49:42] And in demo link like you have to uh
[01:49:46] take a short video of 3 to four minutes
[01:49:49] explaining your project. uh you have to
[01:49:52] uh like explain each and everyone who
[01:49:54] who are present in team in the demo link
[01:50:00] and put your demo link here explaining
[01:50:03] the project from the step one to end of
[01:50:06] the step which is publishing and
[01:50:11] so and your GitHub link here I'll be
[01:50:14] guiding you to how to like what is the
[01:50:16] flow of GitHub and all so this is the
[01:50:20] overview
[01:50:30] go through this epics
[01:50:34] like this is a process. First you have
[01:50:36] to define problem. You have to
[01:50:38] understand that problem and you have to
[01:50:41] collect the data according to the
[01:50:43] problem statement and you have to do a
[01:50:45] data preparation and visualizations
[01:50:48] dashboard storing performance testing
[01:50:51] web integration and you have to do a pro
[01:50:54] project like demonstration and
[01:50:57] documentation. Demonstration is which is
[01:50:58] your demo video. You'll be speaking
[01:51:01] about your project uh about 3 to four
[01:51:04] minutes.
[01:51:06] This is the overview.
[01:51:09] So you will click here. You'll be
[01:51:11] getting all the details of this text
[01:51:13] here
[01:51:16] and go to workspace.
[01:51:21] So in this workspace you have to finish
[01:51:24] all the steps. I did finish. So it is
[01:51:27] showing 0%. If you finish it will show a
[01:51:31] percentage here.
[01:51:36] So this is a steps
[01:51:42] next. Next next go to this shanun and
[01:51:46] you have to drag your like steps to in
[01:51:49] progress. If you are working on that
[01:51:51] step
[01:51:53] you'll be getting you have totally 15
[01:51:56] tasks. If you drag one task into in
[01:51:59] progress you will get to see here how
[01:52:02] many tasks are in progress. I have
[01:52:04] dragged one task. So it is showing me my
[01:52:06] task in progress. If I drag another it
[01:52:09] will show you uh two tasks. If you
[01:52:12] finish this task like if you finish and
[01:52:16] uh remember if you give your demo link
[01:52:20] and GitHub link then only you can able
[01:52:23] to drag this fields to this to be review
[01:52:27] session. You'll be get if you want uh
[01:52:29] try to drag this to be reviewed you'll
[01:52:31] be getting an error. can't change status
[01:52:34] to review without a GitHub or demo link.
[01:52:36] You have to make sure you will be giving
[01:52:38] a demo link and GitHub link in workspace
[01:52:42] before dragging the things into the to
[01:52:44] be reviewed section. After reviewing it
[01:52:48] will go to a complete section.
[01:52:51] So evaluator
[01:52:54] will will give a completed tasks here.
[01:52:58] So based upon that you'll get a marks.
[01:53:03] So you have to drag all this in progress
[01:53:08] session if you are working on that and
[01:53:10] it is in in progress.
[01:53:17] So after you are dragging this fields to
[01:53:20] in progress
[01:53:22] you can notice here in workspace this
[01:53:25] bar is increasing with the percentages.
[01:53:28] So it has to fill four.
[01:53:32] So till it fills you have to do all
[01:53:36] these steps.
[01:53:39] You have to finish all these steps. Then
[01:53:41] you have to add your GitHub and demo
[01:53:44] link and then it will fill this part.
[01:53:48] Then it will go to radio and then then
[01:53:50] it will go to session.
[01:53:53] So this is all about skill valid
[01:53:55] platform. So I hope all uh like you all
[01:54:00] understood this.
[01:54:02] Uh I would like to add something. Just
[01:54:04] give me a second. I share my screen.
[01:54:06] Just stop sharing the screen.
[01:54:22] Guys, I hope my voice is audible and my
[01:54:24] screen is visible. Yes, no, please
[01:54:27] confirm.
[01:54:29] All right, great. Uh, so guys, uh, as my
[01:54:33] colleague have explained you the
[01:54:35] interface, I would like just like to
[01:54:36] add, uh, once again, once again, uh,
[01:54:40] just just give me a minute. Let me log
[01:54:42] into my platform and
[01:54:44] okay
[01:54:48] just allow me one minute. Uh let me see
[01:54:51] the credentials and
[01:54:54] let me log in.
[01:55:53] Okay, I'm just sharing my screen. Uh,
[01:55:56] please confirm if my screen is visual.
[01:55:58] Yes or no? Please confirm.
[01:56:05] Is my screen visible?
[01:56:07] Yes sir.
[01:56:09] Okay, great. So guys uh this is your uh
[01:56:12] platform. Okay. I hope everybody has got
[01:56:16] the login credentials so far. If you
[01:56:18] have not received please check in your
[01:56:19] registered email id search with login
[01:56:21] credential you'll get the details. Okay.
[01:56:23] So this is a platform once you log into
[01:56:25] the uh with your login credentials. So
[01:56:27] here you need to go to the skill valid
[01:56:29] section. You can website you can see
[01:56:31] there are multiple options dashboard
[01:56:32] skill bank skill valid right you need to
[01:56:35] select skill valid. Okay. In this
[01:56:37] particular course uh you'll be setting
[01:56:40] the track for which you have the
[01:56:41] enrolled. Okay. You'll get the access to
[01:56:43] the track for which you have enrolled.
[01:56:44] For example, you have enrolled into data
[01:56:46] analys. Okay. Just click on access
[01:56:49] resources. Okay. Once you click on
[01:56:52] access resources, uh finish it. Uh
[01:56:54] there's no not screen today. Okay.
[01:56:56] Please don't ask this question once
[01:56:57] again and again. So now uh once you
[01:57:00] click on that access resources, you'll
[01:57:02] be redirected to this particular page
[01:57:03] where you have multiple sections. Okay.
[01:57:05] instruction, learning journey, courses,
[01:57:07] group, project, certificate. Okay. Now,
[01:57:10] uh I recommend you to go through this uh
[01:57:12] general instructions which is given over
[01:57:14] here. Okay. This is the learning
[01:57:16] journey. In the learning journey, you
[01:57:18] can get access to all the recordings of
[01:57:20] the previous sessions. Okay. You can get
[01:57:23] access to recordings. You just have to
[01:57:24] click on the view and you'll be able to
[01:57:26] see the recording. Okay. I hope till
[01:57:29] this point it's clear to everyone.
[01:57:31] Right.
[01:57:32] Arjun Satya it's already covered. I'm
[01:57:34] just recapping what she has done. Okay.
[01:57:37] So if you are asking queries in between
[01:57:40] still it will become uh additional for
[01:57:42] me to explain everything right.
[01:57:45] So now uh this is a courses section we
[01:57:48] have any number of queries you people
[01:57:50] are asking which courses to complete
[01:57:52] which courses to complete even though
[01:57:54] reminding in every session which all
[01:57:56] courses you have to complete then also
[01:57:58] you're asking the same thing again and
[01:57:59] again. I told you that you have to go to
[01:58:01] the courses section and whichever course
[01:58:03] is mentioned there you need to complete
[01:58:04] that. So there are two courses mentioned
[01:58:06] here. One is beginner introduction to
[01:58:08] gen lab and another is 100k agent based
[01:58:11] champions. Okay these two courses are
[01:58:14] mandatory to complete. Okay these two
[01:58:16] are self coursees which are mandatory to
[01:58:19] complete. Okay you need to complete this
[01:58:22] course.
[01:58:24] Clear? Yes or no? Is that clear? Now
[01:58:28] will there be any other questions
[01:58:29] related to self-paced course?
[01:58:35] These are the two self-paced course that
[01:58:37] you have to complete apart from FSP
[01:58:39] registration and service and account
[01:58:40] creation. Okay. Those two are mandatory
[01:58:43] things that you have to complete along
[01:58:44] with this. All right. So these are the
[01:58:46] self-paced courses which you have to
[01:58:48] complete. So you can see the duration
[01:58:49] approxation it will take you to complete
[01:58:51] it. Okay. So those who have not enrolled
[01:58:53] into this particular uh lab, please
[01:58:56] enroll it. We have also shared the
[01:58:57] Google forms uh related to the tokens.
[01:59:00] Please fill it. It will take some time
[01:59:02] and you will receive the credits. Okay.
[01:59:04] You'll receive the credits.
[01:59:07] No Arita you will get the recording.
[01:59:09] Okay. Group projects. So what uh
[01:59:13] whatever projects we have uh explained
[01:59:15] today all the projects are listed here.
[01:59:18] Okay. So what you need to do? You need
[01:59:20] to uh sit with your team. Okay. Once the
[01:59:23] team is formed and you need to decide
[01:59:24] which project you have to work on. For
[01:59:28] example, I have enrolled into this
[01:59:29] particular project her disase analysis.
[01:59:31] Okay. So I have to click on access
[01:59:33] resources. I have to click on access
[01:59:35] resources. And you can see this is the
[01:59:38] page where you can uh see the team
[01:59:40] members. Okay. You can see the team
[01:59:41] members and in the same page you can
[01:59:43] assign the task to your team. Okay. Like
[01:59:46] downloading the data set, uh storing the
[01:59:48] data set. Okay. See for your case you do
[01:59:51] not have to use MySQL. Okay. You can
[01:59:54] directly connect the database to
[01:59:55] Tableau. You can directly connect the
[01:59:57] data to Tableau. There is no need to
[01:59:59] connect to MySQL as it's not in your
[02:00:01] curriculum. So we are not expecting this
[02:00:04] from you. Okay. You can skip this step.
[02:00:06] You can directly connect the data set to
[02:00:08] Tableau. Okay. And whomever you want to
[02:00:10] assign this task, you can give the name
[02:00:12] to this particular person. Okay. Once
[02:00:14] the team is formed, you will get all the
[02:00:16] team members name here. Clear.
[02:00:22] clear. Okay. Next, you can see here uh
[02:00:26] we have overview section. In the
[02:00:27] overview section, you'll get a detailed
[02:00:29] uh instruction about the project. Okay.
[02:00:32] You'll get a detail instruction or
[02:00:35] description about the project like what
[02:00:36] is the project problem statement? Then
[02:00:39] what is the architecture and how to what
[02:00:42] are the steps to complete this project?
[02:00:44] Okay. What are the steps to complete
[02:00:45] this project? Then in the left hand uh
[02:00:47] sorry right hand side you can see the
[02:00:48] detailed milestones. Okay these are the
[02:00:51] milestones. Okay in your project we have
[02:00:54] discussed the data and life cycle right
[02:00:56] the first step was problem understanding
[02:00:58] right this is the problem you have to
[02:00:59] understand that then after that you have
[02:01:01] data collection then data preparation
[02:01:03] then data visualization then building a
[02:01:05] dashboard building a story and
[02:01:07] performance testing. You can skip web
[02:01:09] integration you have to in the web
[02:01:11] integration you just have to publish it.
[02:01:13] Okay. And later you have to create the
[02:01:15] documentation and demonstration.
[02:01:16] Documentation talks about all the
[02:01:18] remaining templates. Clear?
[02:01:21] Is this clear?
[02:01:24] Now if you want to see what in detail
[02:01:26] exactly what you have to do, you can go
[02:01:27] to this workspace. You can see here all
[02:01:31] the milestones are given in detail. For
[02:01:33] example, data collection and extraction
[02:01:34] from database. The first step is to
[02:01:36] download the data set. If you click
[02:01:37] here, you can see the access to the data
[02:01:39] set. You can see the data set link here
[02:01:41] given. got it data set link is given
[02:01:44] here. Okay, you can skip these two steps
[02:01:48] in your project. Why? Because this is
[02:01:50] related to connecting the database with
[02:01:52] uh data set with database. You do not
[02:01:55] have to connect the data set to
[02:01:56] database. You can directly load the data
[02:01:58] to Tableau. Okay, you can directly load
[02:02:00] the data to Tableau. After this, you
[02:02:02] have data preparation. In the data
[02:02:04] preparation, these are some of the
[02:02:06] instructions which are given that you
[02:02:08] can follow to clean your data. Okay,
[02:02:10] that you can follow to clean your data.
[02:02:12] The next step is data visualization. In
[02:02:14] data visualizations, you have to create
[02:02:16] eight unique visualizations. Okay, you
[02:02:18] have to create the unique
[02:02:20] visualizations.
[02:02:22] Okay, there should be minimum eight
[02:02:23] unique visualizations. Then you have
[02:02:26] dashboards. In dashboard, you'll be
[02:02:28] developing some kind of dashboard like
[02:02:29] this. Okay, this is a sample dashboard.
[02:02:31] Here you have given a demonstration link
[02:02:33] also like how we can create this
[02:02:34] dashboard.
[02:02:36] Clear?
[02:02:38] Yes or no?
[02:02:44] All right. After that you have story. In
[02:02:47] story you have this kind of story you
[02:02:49] can create. We have also given a
[02:02:50] reference link for this. Okay.
[02:02:56] Got it.
[02:02:59] Uh for this particular project
[02:03:00] visualizations are not visible. I'll try
[02:03:02] to check it and I will rectify this.
[02:03:04] Okay. I'll try to rectify this. Why the
[02:03:08] visualizations are not visible?
[02:03:11] Okay, you'll also see the list of
[02:03:13] visualizations which you can create.
[02:03:15] Okay, for this project it's not uh
[02:03:16] reflecting. I'll take it from my end and
[02:03:19] try to resolve it. Okay, next is
[02:03:21] performance testing. In performance
[02:03:23] testing, this is for your understanding
[02:03:25] purpose like how uh how the data is
[02:03:27] rendered then what filters are used,
[02:03:30] what uh calculated field, how you can
[02:03:32] create the calculated field, the number
[02:03:34] of visualizations. So in this particular
[02:03:36] project we have created this many
[02:03:37] visualizations. Okay, we have created
[02:03:40] this many visualizations.
[02:03:42] All right, this is just for your
[02:03:44] understanding purpose. In web
[02:03:46] integration, you'll see publishing. You
[02:03:48] will see the publishing like how you can
[02:03:50] publish the project. Okay, you need to
[02:03:51] publish the dashboard and story, right?
[02:03:53] In yesterday's session, we have seen how
[02:03:54] to publish it. Correct? Same you have to
[02:03:57] do here also. Okay, same you have to do
[02:03:59] here also. Can I get the attendance
[02:04:00] link?
[02:04:11] Okay.
[02:04:13] Yeah.
[02:04:18] In publishing you need to do. You do not
[02:04:20] have to integrate the web page with
[02:04:21] using flask and all. You just have to do
[02:04:23] publishing. Okay. Then finally this is
[02:04:25] the canon board. In canon board
[02:04:28] whichever task you have completed you
[02:04:30] have to move it to in progress. Okay.
[02:04:32] Then once it is done means once you have
[02:04:34] uploaded your entire project file in the
[02:04:37] demonstration link you'll be able to
[02:04:39] move this cards from in progress to
[02:04:41] review section. Okay in progress to
[02:04:43] review section. Got it? Yes or no?
[02:04:48] Yes or no?
[02:04:52] Okay. See uh in the overview section you
[02:04:56] have these two options demo link and
[02:04:58] GitHub link. Okay. In GitHub link,
[02:05:00] you'll be submitting your entire project
[02:05:01] files. Okay? And sharing the public link
[02:05:05] here. If you click here, you get an
[02:05:06] option to add the GitHub link. You have
[02:05:09] to load it. You have to add it here.
[02:05:10] Okay? Same demo link. Basically, what
[02:05:12] you need to do once your project is
[02:05:14] complete, you need to record a small
[02:05:15] video of 5 to 10 minutes. Okay? 5 to 8
[02:05:19] minutes and upload that video over here.
[02:05:23] Okay? You need to upload that video over
[02:05:25] here. Make sure whatever video you're
[02:05:27] uploading has a public access. Okay? If
[02:05:30] you send a restricted file who which
[02:05:32] file evaluator cannot see, you'll get
[02:05:34] zero marks for it. I'm telling you up
[02:05:36] front, please make sure it's public.
[02:05:38] Okay? Make sure your file is public
[02:05:40] before sharing and uploading here.
[02:05:45] here
[02:05:49] . So once you upload your files here
[02:05:52] here. So once you upload your files here
[02:05:52] means your demo link and the GitHub link
[02:05:54] you'll be able to move the Canman boards
[02:05:56] to the review section. Okay, it says we
[02:05:59] cannot change the status without the
[02:06:00] GitHub or demo link. You need to move it
[02:06:02] to the review section. Okay, once you
[02:06:05] move all these things to the review
[02:06:06] section once you move all these things
[02:06:08] to the review section the progress
[02:06:10] percentage will go till 90% 90%.
[02:06:14] Okay, it will go only till 90%.
[02:06:17] Okay, once your evaluator evaluates your
[02:06:20] project, he will move all these cards to
[02:06:23] completed section. Then this overview
[02:06:25] section will become 100%.
[02:06:27] Is that clear?
[02:06:30] Is that clear?
[02:06:41] Yes, Lanka. It should be like the way
[02:06:44] I'm sharing my screen and speaking. In
[02:06:45] the same way, you have to do it.
[02:06:52] Mohamad, you have to work together. You
[02:06:54] have to work together in a team. Okay?
[02:06:57] You can form a group Ge and you can work
[02:06:59] together on a project.
[02:07:04] Okay? You can distribute the task among
[02:07:06] your team's member.
[02:07:14] Is that clear
[02:07:31] guys I would like to share uh no Arjun
[02:07:34] six members will not there in team okay
[02:07:36] I'm telling you four to five members
[02:07:40] No Jasmine. Uh we do not have the access
[02:07:42] to faculty details so I cannot share
[02:07:43] you.
[02:07:49] Okay. Look into this image very
[02:07:51] carefully guys. I have uh this image. So
[02:07:54] you can see here uh I am telling you
[02:07:56] that uh you have to complete your
[02:07:57] project in particular documents right in
[02:08:00] particular phases. See once you complete
[02:08:02] your project you also have to submit
[02:08:03] some documentation to us. Okay. So these
[02:08:06] are the format these are the folder
[02:08:09] structure. Okay. This is the folder
[02:08:11] structure. So basically your project is
[02:08:13] divided into different phases. Okay. So
[02:08:16] you can see here you have ideation phase
[02:08:18] then requirement analysis phase. The
[02:08:20] third phase is project design phase.
[02:08:21] Fourth is project planning phase. Fifth
[02:08:23] is project development phase. Sixth is
[02:08:26] performance testing and seventh is dock
[02:08:27] and demo. Okay. So these many folders we
[02:08:30] expect you to create in your git
[02:08:33] repository. Okay. What the files they
[02:08:36] will be containing? For example,
[02:08:38] ideation phase will contain these three
[02:08:40] documents. Problem statement, empathy
[02:08:42] map, brainstorming. Requirement analysis
[02:08:45] will contain customer journey map,
[02:08:46] solution requirement, data flow diagram,
[02:08:48] technology stack. Project design phase
[02:08:51] will contain problem solution fit,
[02:08:52] proposed solution, solution
[02:08:53] architecture.
[02:08:55] Project planning phase will contain your
[02:08:57] project planning template.
[02:08:59] In your project development phase, you
[02:09:00] need to submit two PDFs. One PDF will be
[02:09:03] the steps of data prep-processing that
[02:09:05] you have done on your data set along
[02:09:07] with the business questions and
[02:09:08] visualizations. You should create at
[02:09:11] least eight business questions and eight
[02:09:13] visualizations. Okay. That to unique
[02:09:16] and second PDF will contain the
[02:09:18] screenshot of dashboard story and it
[02:09:20] will also contain your dashboard and
[02:09:22] story public links. Okay. And the third
[02:09:24] file will be the data set that you're
[02:09:26] working on.
[02:09:28] In the sixth phase, performance testing
[02:09:30] phase you have a one document that is
[02:09:32] performance testing and final
[02:09:34] documentation uh folder you have final
[02:09:36] report and demonstration. Don't worry
[02:09:39] guys what all templates these are we
[02:09:40] will be discussing it you in tomorrow
[02:09:42] session onwards. Okay this is just an
[02:09:44] overview like these are the documents
[02:09:46] which has to be submitted. We'll explain
[02:09:48] you each and every template like how to
[02:09:50] create it, how to work on it and you
[02:09:52] need to maintain the same folder
[02:09:54] structure. If in case you are
[02:09:56] maintaining any different folder
[02:09:57] structure or any particular order. If
[02:10:00] the evaluator miss your file then it is
[02:10:02] not a guarantee. Okay. So ensure that
[02:10:05] you're prop following a proper folder
[02:10:06] structure. Folder structure allows the
[02:10:09] evaluator to access the files easily.
[02:10:11] Okay. If you're following some random
[02:10:13] folder structure and if you misses your
[02:10:16] file to evaluate it then please don't
[02:10:20] blame us. and telling you upfrontly
[02:10:22] please follow the folder structure given
[02:10:24] here. Okay, because of this it easy for
[02:10:27] us to evaluate all your files. Okay, if
[02:10:31] you're not following the folder
[02:10:32] structure then there is a high chances
[02:10:34] the evaluator might miss any of your
[02:10:37] file. Okay, for which you might get zero
[02:10:39] marks. Each of these document which you
[02:10:41] are seeing over here carries some or
[02:10:43] other marks. Five marks, 10 marks, 15
[02:10:45] marks and this these files are carrying
[02:10:47] some marks. Okay. So please don't miss
[02:10:51] any particular template. Okay.
[02:10:55] Here
[02:10:57] wi Nanuba please stop posting your
[02:10:59] concern. I understood what you're
[02:11:01] saying. It is self enrollment is not me
[02:11:05] uh restricted for you. It's restricted
[02:11:07] for you because this enrollment will
[02:11:08] happen from your college side. That is
[02:11:10] what we are telling you since the
[02:11:12] beginning of the session. If you join
[02:11:14] session late, you'll not able to
[02:11:16] understand the things.
[02:11:18] Okay, understood.
[02:11:22] Project enrollment teammation will be
[02:11:24] done from the college side. If in case
[02:11:26] some colleges, we will try to allot it
[02:11:29] for you. Okay, we'll try to assign it
[02:11:30] for you. The teams as well as the
[02:11:32] projects. The projects will be the team
[02:11:35] formulation will be from the same
[02:11:37] college, same track, same branch. Okay
[02:11:41] guys, is my English not clear to
[02:11:42] everyone? I'm seeing the same concern
[02:11:44] every time.
[02:11:47] Gopal ready it is in process we'll
[02:11:50] update you you'll get a proper email
[02:11:51] okay all your team members such kind of
[02:11:54] things you'll get it
[02:11:56] clear
[02:12:14] now we are open for your Q&A If you have
[02:12:17] any queries, you can tell me.
[02:12:20] Jasmine, please complete it as soon as
[02:12:21] possible.
[02:12:25] First guys, try to add your name while
[02:12:28] posting your queries. Then only we'll
[02:12:29] try to answer you. Okay.
[02:12:34] Doesn't want to view the project
[02:12:36] details.
[02:12:40] You can check this recording. You'll get
[02:12:41] the recordings. Okay.
[02:12:45] Uh sushita just add all the project use
[02:12:47] cases uh titles in our uh note sheet
[02:12:51] please add it.
[02:12:52] Okay
[02:13:00] guys you can ask your queries if you
[02:13:01] have any. I I'll answer only those
[02:13:04] queries which has proper name.
[02:13:09] Yes, Mohamad Anish, you can do in that
[02:13:11] case.
[02:13:16] Yes, Mohamad Ari, that is what we are
[02:13:18] trying to say from the beginning of the
[02:13:19] session.
[02:13:22] Uh, no Manu, I don't have that details.
[02:13:26] I'm sure from your college you will be
[02:13:28] easily finding out who is the person.
[02:13:33] Manu Puri Rajesh there is no notes we
[02:13:35] are maintaining. There's only one notes
[02:13:36] that we have created and it is already
[02:13:38] shared with all the students.
[02:13:41] People in the YouTube, do you have any
[02:13:43] concerns?
[02:13:48] People in the YouTube.
[02:13:59] Hey guys, if in case your college is not
[02:14:00] forming the team, it is getting too
[02:14:02] delayed, you can just reach us out. We
[02:14:04] will try to resolve it. Okay. What is
[02:14:07] Yogesh? What is your query?
[02:14:12] Yogesh, please post your concern here.
[02:14:18] I suggest you guys please attend all
[02:14:21] these knowledge sessions.
[02:14:24] Okay, next three knowledge session. It's
[02:14:26] very important. Then only you'll
[02:14:28] understand what you have to do in the
[02:14:29] templates. Okay,
[02:14:32] please don't miss it.
[02:14:43] Mohammad, please wait for a couple of
[02:14:45] days. If you don't get any response,
[02:14:47] we'll try to do it for you. Yogesh
[02:14:49] Shahadav, as of now, we have not shared
[02:14:52] any attendance with the college. If it
[02:14:54] if required, we'll share it. Okay. uh 7
[02:14:58] Kumar probably 8 will be the last date
[02:15:01] for submission.
[02:15:03] Okay. 8th of July probably
[02:15:08] it's not confirmed but
[02:15:11] probably it will be 8th of July as per
[02:15:14] your calendar.
[02:15:26] money as I told you I do not have those
[02:15:28] details so I cannot share you
[02:15:33] nam manasa if there is any concern we'll
[02:15:35] try to directly reach out to your spark
[02:15:38] okay
[02:15:40] like to reach out to your spark
[02:15:45] nita you can first reach out your
[02:15:46] college spoke
[02:15:49] you can first reach out your college sp
[02:15:52] Okay
[02:15:58] guys, all the 10 use cases are has been
[02:16:00] added to the notes. Please check it.
[02:16:04] Spock is the contact person of your
[02:16:06] college who is uh coordinating for this
[02:16:08] program with us.
[02:16:13] Okay. Mohamad Arit there will be one
[02:16:16] person who will be coordinating our
[02:16:18] appship program with us. So you can
[02:16:20] reach out to that person. Please connect
[02:16:22] with the other students in your college.
[02:16:26] Guys, please check the notes. All the 10
[02:16:28] use cases has been added.
[02:16:43] You can just go to the last page. You'll
[02:16:45] find all the 10 use cases.
[02:16:52] If there is no one in your college,
[02:16:53] you'll do the project alone.
[02:16:59] Uh Zara, I we need to check for that.
[02:17:02] I'm not sure.
[02:17:06] Yes, Shik Sahad, you are eligible. You
[02:17:09] just have to complete all the self-pace
[02:17:11] courses and attempt all the assessment.
[02:17:16] Okay.
[02:17:28] that in that case uh I have to check
[02:17:30] with my team like how to crack the as of
[02:17:32] now we have not shared any attendance
[02:17:33] with any stock only one list we have
[02:17:36] shared okay everyday attendance we don't
[02:17:39] share
[02:18:10] Guys, if you have any issues with
[02:18:12] attendance, please don't worry. If there
[02:18:14] is anything, we'll directly reach out to
[02:18:15] your college faculty. Okay?
[02:18:28] So tomorrow is your first knowledge
[02:18:30] session. Uh we'll try to complete first
[02:18:32] and second phase. So tomorrow is second
[02:18:33] knowledge session. Today was the first
[02:18:35] one where we have covered the project
[02:18:36] orientation session. Okay.
[02:18:42] Rakkesh Mumawad
[02:18:44] uh maybe just wait for two to three days
[02:18:47] as it is in progress. So you will
[02:18:51] receive one email related to the team
[02:18:54] information and project enrollment.
[02:18:55] Okay.
[02:19:01] If you know the college F you can
[02:19:02] directly reach out to them.
[02:19:10] Last date is uh 8th of July probably
[02:19:12] guys it's not confirmed uh I just have
[02:19:15] got one date so I'm telling you
[02:19:30] platform is clear to everyone yes or no
[02:19:33] platform is clear to everyone yes or no
[02:19:35] please tell
[02:19:41] Guys, in your platform, whatever project
[02:19:43] you enroll, there is a step-by-step
[02:19:45] instruction given how to complete the
[02:19:47] project. Okay? So, don't worry.
[02:19:49] Everything is given there. Step-by-step
[02:19:51] instruction. Some videos are also there.
[02:19:53] You just have to follow it. Okay?
[02:19:56] And you can complete your project.
[02:20:04] Uh yes Kiran we'll try to schedule one
[02:20:06] session on GitHub probably on the last
[02:20:09] day we'll try to uh explain how to
[02:20:13] uh create the folders and how to upload
[02:20:14] the files in the folder structure. Okay.
[02:20:22] So tomorrow you do not have any quiz.
[02:20:23] Quiz will be on Tuesday. Grand
[02:20:25] assessment is on Saturday.
[02:20:37] Gopal ready are you not attending the
[02:20:40] session?
[02:20:41] We have told this date multiple times.
[02:20:43] 8th of July. Where is your attention?
[02:20:54] The 8th of July is not confirmed. It's a
[02:20:57] probable date. Okay.
[02:21:04] Grand assessment is a kind of a mega
[02:21:06] quiz where you have to answer 30
[02:21:07] questions. Okay, 30 multiple choice
[02:21:10] questions like the way you are giving
[02:21:12] quiz in quiz you just have 10 questions.
[02:21:14] In grand assessment you will have 30
[02:21:16] questions. Mohamad
[02:21:26] Arjun Satya tomorrow is not any exam. I
[02:21:29] told you you have quiz number three on
[02:21:33] Tuesday.
[02:21:39] It will be 1 hour manu.
[02:21:47] Okay guys, attend the quizzes, this
[02:21:50] meetings, these sessions through your
[02:21:52] registered email only. Okay, if you are
[02:21:54] registering or attending from any other
[02:21:56] email, then you'll not get the
[02:21:58] attendance, you'll not be uh your score
[02:22:00] will not be captured. Okay.
[02:22:08] Uh subby no issues
[02:22:10] no issues but ensure you are doing it.
[02:22:13] We'll see how many attendance uh the
[02:22:16] student is maintaining. Okay.
[02:22:26] All right. Any other questions?
[02:22:31] So tomorrow phase one phase two
[02:22:33] templates we'll try to explain. Please
[02:22:35] attend it. Don't miss it. Okay.
[02:22:51] Diva, we have not shared any attendance
[02:22:53] so far. So there's no way to track the
[02:22:55] attendance. If you want to track, you
[02:22:56] can track the old attendance that we
[02:22:57] have shared. Everyday attendance we are
[02:22:59] not sharing with your college.
[02:23:20] Telegram group is not working. It will
[02:23:21] not work till 22nd June.
[02:23:25] So if you have any queries, you have to
[02:23:26] attend this session. You can ask here.
[02:23:34] Good.
[02:24:16] Uh si uh first try to attempt the grand
[02:24:19] assessment. Okay, we'll check this out.
[02:24:27] Don't miss the upcoming quizzes and the
[02:24:29] grand assessment. We'll try to resolve
[02:24:30] it. Okay.
[02:25:04] I hear the queries
[02:25:08] second course is not showing.
[02:25:11] How come second course is not showing?
[02:25:13] Please check it properly.
[02:25:18] Uh what is your name student? Whatever
[02:25:20] you have given please first give your
[02:25:22] name. What is your name?
[02:25:33] Lakshmi. Laxshmi. So understand one
[02:25:35] thing. You'll be completing the project,
[02:25:37] right? You'll be completing certain
[02:25:40] documents. Now, how you're going to uh
[02:25:42] submit the documents to us? GitHub is a
[02:25:45] platform where you'll be uploading all
[02:25:47] your project files. Okay? And that link
[02:25:50] that link that URL you have to share it
[02:25:53] with us that is your GitHub link. Okay
[02:25:57] we have one session we'll keep it on the
[02:25:59] last day so to make you more
[02:26:02] comfortable. Okay.
[02:26:09] Yes sash it's restricted.
[02:26:16] Uh no Rohit
[02:26:19] it's fine Rohit there there will not be
[02:26:21] any kind of issues. Okay
[02:26:26] participants on the YouTube any queries
[02:26:53] Uh Jasmine that is what I said probably
[02:26:56] by 8th of July.
[02:27:00] Okay.
[02:27:06] Don't wait for the last date guys. Try
[02:27:08] to complete your project as soon as
[02:27:09] possible. Once everything is done, start
[02:27:12] working on the project. Okay.
[02:27:19] If it is not showing attempted, maybe
[02:27:21] your email id was not proper. Maybe you
[02:27:23] have given some other email id.
[02:27:25] Sometimes uh we make typo mistakes,
[02:27:29] right? It can happen.
[02:27:33] It might happen.
[02:27:38] form just takes your email id if you
[02:27:40] have given whatever email id it doesn't
[02:27:42] verify whether that email exist or not
[02:27:44] exist okay it does it just takes that
[02:27:46] email id so you need to fill the form
[02:27:48] properly then only the email will get
[02:27:51] captured accordingly okay
[02:27:59] Jasmine mean please check once your
[02:28:01] login credentials in your email with the
[02:28:04] login credential search okay just check
[02:28:07] it with
[02:28:13] What is your email ID and what is your
[02:28:15] college name?
[02:28:44] Is this your email id? Is this your
[02:28:46] registered email id?
[02:28:57] Don't worry. First complete the project.
[02:29:00] complete the project. Don't worry about
[02:29:02] attendance.
[02:29:04] Okay, we have clearly mentioned how to
[02:29:06] start the project.
[02:29:16] All right guys, so that's all for today
[02:29:19] from my side.
[02:29:30] Thank you so much for joining.
[02:29:36] Uh Rohini, it can be it is your choice
[02:29:39] whether you want to assign it to one
[02:29:40] person or you want every person to be
[02:29:42] part of team.
[02:29:46] Okay, it's up to you.
[02:30:08] Uh, please stop the recording and stop
[02:30:11] the streaming. I'm facing some technical
[02:30:13] glitch.