How to use Codex as your AI data analyst

Today, we're going to turn you into a data analyst. That might sound boring,

data analyst. That might sound boring, but it's not. AI makes analyzing and understanding data possible in ways that's never before been achievable. And

why you have to watch this show is cuz I have an ex Google data scientist who has decades of data analysis and data science experience. She is going to give

science experience. She is going to give you the cheat sheet. She's going to give you all the tips, the tricks, and the guidance to actually do this for your own company. This is going to be a

own company. This is going to be a gamecher. It's going to unlock your

gamecher. It's going to unlock your career and unlock your growth. Let's get

into today's show.

What we're going to talk about today is Agentic Analytics and how you actually use these AI tools to do really powerful and meaningful data analytics. And today

we're going to go over codecs. And so

maybe before we actually get into the demo, like give us a little bit of like why codecs, what's your take on codec for data analysis? And give us the unvarnished opinion. So Codeex and Chad

unvarnished opinion. So Codeex and Chad GPT have come a long way. Like for those of you who have been following Chad GPT launches a couple of years ago, Chad GPT launched something called Chad GPT

operator. They literally called a data

operator. They literally called a data analyst and I tried it out and it was horrible. It was not good at all. It was doing a very poor job. So

all. It was doing a very poor job. So

compared to that, I would say like OpenAI has come a long way with respect to data analysis. For example, the demo that I'm going to show today is I basically have a CSV file which I take

and put it into codeex and I give it a problem that I want trying to solve a root cause analysis and you're going to see that it does a fundamental job. Now,

does that mean I'm going to take that at face value? Definitely no. I'm going to

face value? Definitely no. I'm going to like do some validations on it. I'm

going to make sure like whatever is spitting out is actually correct.

Before we go forward though, codecs can seem like the scary thing to people.

Yeah I I think we're obligated to say that if you have a chat GPT account, then all you have to do is go download Codeex and it's a very quick setup on your computer and so it's not this big scary thing.

It's like a couple of minutes to get up and running. Right.

and running. Right.

Exactly. So like just like Google or like watch some YouTube videos. There

like awesome content online that takes you like from very beginner to like somewhat intermediate where you're comfortable to like just get started and open codeex and like put your analysis

file and like run the analysis.

I did a great show with Matt Wolf where he built like a an AI second brain in codeex and we did something codec set up there. So you can go check that out. So

there. So you can go check that out. So

this video is going to assume that you have that part done, right? That that

you got codeex and you have that set up.

And then if that's the case, then we can kind of go to the data analysis use case. And so I know that you were

case. And so I know that you were talking about that you're going to walk us through a case where you have data in a CSV. And so take us from there.

a CSV. And so take us from there.

So this is the ver file that we're going to be working with. This is customer retention data. Basically, uh we have

retention data. Basically, uh we have visit ID. We have customer ID, their

visit ID. We have customer ID, their sign up date, and when they're visiting.

And we also have like their visit date like what week they visited what were their uh total number of visits are they new customers their returning customer.

Basically it has like all customer retention data that you would expect in a customer retention data set.

Retention is like this complex topic right because it's not just like binary like I was a customer was not a customer. It's

customer. It's well I'm still a customer but I pay you less. I'm still a customer but I pay you

less. I'm still a customer but I pay you more. I'm still a customer, but I bought

more. I'm still a customer, but I bought this other product or I'm not a customer of this one product, but I still retained this other product. It's a very like multifaceted complex topic, which I think it makes it really good for the

kind of demo and example you're walking through.

Exactly. Like your best customer is your current customer.

Exactly.

They have already sold them once. They

believe in your vision. So like it's easy to give them another shot and like see what else they would purchase.

Anyways, so we're starting with this customer retention data, but the whole framework that we're going to use today, it's going to remain the same regardless of what data set you're using. And for

just a little bit more basics, I have created a customer retention folder under my documents folder. So when we say work locally, we're working with

basically files on our desktop. So what

I did is in my documents, I created a folder and I called it user retention codecs. And this file that I just showed

codecs. And this file that I just showed you is actually saved in that folder. So

now what we're going to do is we're going to go to codeex. So codeex is basically think of like claude cowwork but open version of claude cowwork and

claude code all in one. I have both chips standalone apps as well as codeex.

So in codeex what I'm going to do is I have already created a project. If you

don't have a project created, you can just easily create project using this dropdown. So for example, right now you

dropdown. So for example, right now you can say add new project and then you basically it will tell you like which folder and then you create the folder and then you will work within that

folder. So in this case I have already

folder. So in this case I have already connected it to this user retention folder that I just showed you in my documents folder. And now here what I'm

documents folder. And now here what I'm going to do is we're going to imagine a scenario. The scenario is your

scenario. The scenario is your leadership is coming to you and it's basically 12:00 p.m. on a Friday and they want to understand why user

retention has dropped in the last week and what exactly is the root cause and they want an answer by 2 p.m. So like

you literally have two hours to like figure out which before would have been impossible.

Right.

Exactly. And of course if you're going to present it to leadership it has to be a deck. like you can't just go with like

a deck. like you can't just go with like some two word like summary like yes that could work but like if you really want to impress like you want to put a deck together.

I know we covered a lot today show but we've got some free resources that are going to help you take it to the next level you can scan that QR code. You can

click the link in the description below and get everything you need. So what

we're going to do with codeex today, I'm going to give it a prompt and I'm going to say can you find what drove the retention drop basically last month and I want you to build a cohort analysis

and I want you to turn the top insights into leadership tech. So it's a very simple prompt. Now we can obviously like

simple prompt. Now we can obviously like refine it once this like starts developing but this is like the basic prompt I'm starting with and the folder that I'm working with is user retention

which has the CSV file that I showed you earlier is saved. Now in terms of the model you have option to like pick uh whatever model you want to pick from chat GPT currently I have put it on

intelligence at medium and we're using GPT 5.5 and then all we're going to do is hit enter and it's going to start working and what's interesting it's like this seems very simple but it's doing a

lot of hard things you know you told it to make a cohort analysis it needs to be a deck which means it needs to graph that data in a way that's accurate and doesn't misrepresent that data I think You're probably going to talk us too

about how you make sure the data is correct, right? Because I think one of

correct, right? Because I think one of the big challenges with any data analysis is like am I sure my data is right?

Exactly. So I think people have fear that the AI tools are now going to replace data analyst or data scientist.

I think that's that's not true. As

somebody who has worked professionally in the domain for the last 12 years, data analytics and data science is a lot more than just like coding or doing analysis in Excel. It's a lot more about like stakeholder management, applying

the critical thinking and applying your analytical thinking because it's possible that AI is going to give you something wrong. And if you don't have

something wrong. And if you don't have the analytical thinking and the domain knowledge, you're going to give wrong answers and that's going to hurt your career more than it's going to help.

And as somebody who's like a real expert in data analysis and data science, like what are the couple of tips, tricks, principles that if somebody's doesn't have your level of experience that they should just keep in their head when

they're doing a project like this? First

of all, you need to make sure like the data that you are uploading and the tool that you're using um do you have the appropriate permissions from your company to be able to like upload that

data into an AI tool. So the rule that I follow yes there are so many AI tools out there but if you're using it for the data doing data analysis for your job for example at HubSpot if you are doing

like um data analysis like some marketing funnel analysis what does HubSpot has subscription to at enterprise level is it claude is it openai if that's correct then use the

internal tools that have like the enterprise security measures in there to use it for like data analysis so like that's number one that's like bare bare basics like don't take your company's

data and upload it somewhere else.

That's a big no. So that's definitely measurement number one. The second is when you're doing analysis with um AI, have a clear problem that you're trying

to basically solve with it. So unless

you understand what you need to ask, what type of questions you need to ask, you're not going to be able to like get a lot of value out of it. So like fully understand what you're trying to get from this data analysis and what the

output you want to look like. For

example, for my analysis, I want it to be a cohort level analysis and then I want it to be a leadership deck with explanation why exactly this retention basically dropped. Once you get the

basically dropped. Once you get the analysis, once you get the output, you need to like spend a little bit of time looking at the numbers. Start with like very simple like does the math even make sense cuz sometimes AI tends to like

make mistakes on a great tip.

Yeah, simple math. So if the revenue dropped by 12%, but the customer retention dropped by 30%, like that doesn't actually make sense and you can

only make that judgment if you have like looked at the retention analysis in the past. So your experience, your

past. So your experience, your background knowledge is going to help you quite a bit when you're working with AI to make uh judgment decisions and validate what AI is spitting it out for

you. Right? So those are I would say the

you. Right? So those are I would say the three fundamentals that you have to like keep in mind. Um obviously like validate, validate, validate especially if you are just starting to use it. Uh

eventually you'll build your trust with AI but it will take time and if this is something that I'm going to put my name on it. I have to make sure like this is

on it. I have to make sure like this is this is actually the right correct. You just gave everybody 10

correct. You just gave everybody 10 years worth of data analysis skills in in in like 70 seconds. It was it was pretty impressive. Those are like

pretty impressive. Those are like everything you need to know to to basically go from nothing to being competent at something like this, which is awesome.

Okay, so we kicked it off and it's been working for the last 5 minutes. And what

it's starting doing is it's first thing it did is looked at the data. It walks

me step by step what exactly it's doing.

And right now what it's doing is basically creating u project files in the folder that we have given it. And

then it's looking for like appropriate libraries that it's going to use for analysis. It's looking for like

analysis. It's looking for like permissions. So you may have to like

permissions. So you may have to like babysit it a little bit to give it the right permissions. On cloud, you will

right permissions. On cloud, you will get like do you give it access and you have to like manually click allow access, allow allow allow allow allow, right?

Like the the two biggest challenges with whether it's codecs or cloud code or whatever. There's lots of them, but a

whatever. There's lots of them, but a few of them are allow allow allow and permissioning as well as just like managing everything locally and versus like the cloud and sometimes you need stuff in the cloud to share it. So you

spend a lot of time manipulating files around different places, right?

Yeah, exactly. So in the last six minutes, it has primarily still looking at the right permissions and tools and libraries that it need. that we can jump to like a pre-built analysis that I've

done same analysis and I can walk you through in more detail.

This is the magic of like the cooking shows on TV, right? It's like you're showing us what it's doing, but now you're going to show us the kind of the end result that it was pre-run. But

that's an important thing for everybody to understand is what you're showing is not like a 60-second project.

This is I I assume probably takes 30 minutes to like fully run.

Yeah. So like we can see like the last one that I ran it took about 9 minutes.

So it went through everything asked me a bunch of permissions and then it looked at the data in more detail. For example,

it says I'm going to inspect the schema and data coverage next and why last month happened. And then it found that

month happened. And then it found that the data set basically it's still giving me description of the data. So it's

saying uh data runs from March 2nd through May 3rd, 2026 with the latest week starting April 27th flagged as the dip period. So it's basically telling me

dip period. So it's basically telling me more specifics in terms of like the problem that I gave it. So and then it says that retention did fall by 66% in

the prior week to 41% in the latest week. So basically uh it did identify

week. So basically uh it did identify that the retention did drop quite a bit.

Now, if you want to do like validation, this is where you would like quickly look at the data and like do some pivot tables and validate this number and see like if this seems correct. Okay, if

this seems correct, let's keep moving forward. And then it said like it's

forward. And then it said like it's looking at couple of things and based on what it's seeing, it seems like mobile crash exposure jumps to 45%. While email

campaign exposure collapses to 1.5%. So,

it's kind of like hinting toward mobile, but like it's going to keep going. And

then eventually after this finishes the analysis, it actually does find that the pattern is now pretty clear. Customer

weekly level retention drops from 72% to 46% which is a drastic drop and it's related to new mobile version launch. So

that's our root cause. So now the question that leadership was asking why did last week drop? It's possible that the product and nge team launched a new version of the mobile app and there was

a bug and that led to a lot of crashs and customers basically not retaining even though they were coming back on the site they were not able to like continue on the site because their session ended.

Now it's not done yet. There is more. So

because we asked it for a cohort analysis and we asked it for a PowerPoint, you have a robust data set here and that's really important and it's possible if you're watching this, you might be doing something similar and one

of the things that it could come back with is like I need more data, right? Because you you fortunately have

right? Because you you fortunately have a lot so it can actually get a statistically significant conclusion from here. But it's very possible that

from here. But it's very possible that if you're out there doing this, it's going to ask you for more data. So I

think you want to start I think with a pretty good size of data more data than you think you need. Is that like a good thing that everybody should consider or how do you think about how much data to start a project like this with?

Yeah, I would say this is again like where your judgment would come in because AI is going to just assume that it has limited space to work with like this is the only data set it needs to work with. So it's going to make

work with. So it's going to make conclusions based on whatever data set you give it. You give it like the missing data is going to make conclusion based off that too. So that's where like you need to figure out what exact data

set you need to be able to do the analysis. This is like one of the

analysis. This is like one of the mistakes that AI makes. It just assumes that it doesn't have the option to get additional data set from you. Uh at

least today one of the things like data analysts, data scientists or anybody working with data struggles with is figuring out where the data lives. And a

month ago when I was working at Google, I was actually able to use AI just to describe this is the data set that I'm looking for. this is the SQL script that

looking for. this is the SQL script that I want to write and pull the data. It

would automatically figure out which tables the data lives in. It will

automatically write the SQL and finding the data set is one of the hardest challenge when you work at a bigger company where there's so much data and it's like spread out everywhere. But

even there's like a AI layer that is like all the rag which is like your company's internal database information embedded and you can quickly find where the data lives. So to your question, as

an analyst, you have to figure out what data is the right data for this.

Well, and I think what's fascinating about the show today is that it's like the ultimate example of like, hey, AI is really good and it's really helpful, but it is light years away from replacing

humans. like everything you've talked

humans. like everything you've talked about is you've talked about a step of like human intelligence, experience, and discretion and judgment needed at like

literally every step of the process, right? And I do think there are some

right? And I do think there are some people who do kind of set it and forget it AI and the AI is just going to figure it out and I'm not going to worry too much about it. And I think that's fundamentally a bad thing to do, but especially bad thing to do when it comes

to data analysis because there's so many gotchas. There can be hallucinations.

gotchas. There can be hallucinations.

There can be misinterpretations and if it doesn't make sense to you as a human, it's probably wrong.

Exactly. Yeah. I think uh Forbes wrote an article couple of years ago when TAGPT operator came out and they basically made a very bold statement saying like data AI is going to replace

data analyst with like TAGPT operator and I think that scared a lot of people and I would say if you are a practitioner who has done data analysis regardless of what your role is you know

that data analysis is way more than just crunching numbers. The way I treat it is

crunching numbers. The way I treat it is like let's say if I have an intern who is working for me and I know what problem I need to solve, I'll just give the crunching part and the coding part

to my intern and yes intern will come back with something but I need to like sit down with the intern and figure out what exactly makes sense, what doesn't make sense, what we need to revise and what we can just present as is.

Yeah, I I have this whole theory that no matter what your job is, there's like a bad creative component that the people who are great at that role have. Like I

think the great creatives, designers, editors, like they know when to call something done versus like to keep tweaking, right? They know this magic

tweaking, right? They know this magic moment of when the art is done. I think

for data analysis, for example, there's just like the best people at data analysis I've ever met, they're like the most creative question askers, right?

They don't ask the obvious questions.

They ask these counterintuitive unique questions of the same information and get remarkably different outcomes than anybody else who maybe just be a novice

at that thing. Right? Do you see that?

Oh, a lot. I don't know if it's like an internal inside joke or something like that. A lot of the times when like data

that. A lot of the times when like data scientists or data analysts have like stakeholders come to them, they will ask a question, but when you like dig in a little bit deeper, it turns out like the problem that they're trying to solve is

completely different than the one that they came to us with. So you definitely have to like figure out what the right problem to solve is clarity of problem and really creative questions around that problem.

Exactly. Exactly. You nailed it.

Okay, that that's awesome. I think

what's fun is that we're building this analysis and kind of going through the skills and human aspects necessary to do that. And so you've obviously run this,

that. And so you've obviously run this, you found, you know, in this sample data set kind of the core issues. But one of the things you talked about is like if if people remember a few minutes ago, it's like, oh, we need to present this

to the executive team. We got to get a deck. We got to visualize that. So show

deck. We got to visualize that. So show

me that component of all of this.

Yeah. Okay. So the the fun part which I honestly cuz personally I I can write documents all day. Tell me to write a six-page document I will write it for you. But if you as soon as you tell me

you. But if you as soon as you tell me to create a deck like I don't know like I am so lost. So one thing I personally love that I can actually now create decks like take it from a CSV file to

like actually create decks with AI. Now

I will like put a disclaimer. Codeex

doesn't create the prettiest decks. I

would say like Claude and Gemini do better at decks than Codeex.

I completely agree.

But hey, it's a deck so we can work with it. So after I did the analysis, it

it. So after I did the analysis, it basically like found like the main drivers are the mobile app crashes and then it basically gave me two deliverables. One is the cohort analysis

deliverables. One is the cohort analysis that I asked it to give me and then the second is the leadership deck and you can actually see on the right side of the pane this is the cohort analysis

that it did and we'll look at it in a little bit more detail and then this is the deck that it created for me that I can present to leadership and then it also like gives you like coding files that it has created but

let's actually go back to the folder where it has saved all the files. So

when we go to our documents folder uh now we can see like that it has created like bunch of folders and bunch of like basically here we'll see like it has

created bunch of CSV files for the cohort analysis and then for like deck it has also created like bunch of photos and uh screenshots and in the retention

deliverables is the two files that I was showing you one is the Excel file with the retention drop cohort analysis and the second one is the leadership deck.

So let's actually look at the cohort analysis file and see what it did for us. Okay, so this is our source data

us. Okay, so this is our source data that we gave it and it gave me a bunch of tabs that you can see here. So first

tab is executive summary. So it gave me top leadership takeaways that you can see here. The number one takeaway is

see here. The number one takeaway is that the drop is concentrated on April 27th week. Customer retention fell from

27th week. Customer retention fell from 72 to 46. And then the second takeaway is like it's related to the mobile version launch and third is something related to email campaign. Now it has

given me like the main metrics that the core metrics that it analyzed and what it found but it also went deeper into like the analysis where it gave me like

weekly retention trend. So here customer week retention is the primary KPI and this is the week that they started and how the active customer number has

changed. So if you look at the weekly

changed. So if you look at the weekly data so like active customer in March 2nd week is 831 and then we keep going down you can clearly see that the active

customer number is 588 and then the retained customer drops significantly and we also have our crash customer. So you can see like there's a

customer. So you can see like there's a 52.6% crash and so on. And then it also spit out a plot for us where it tells us

like retention drops as we have increase in exposure. So CDs 2 is our crash

in exposure. So CDs 2 is our crash percentage and see the series one which is this blue line. It's our customer retention. So you can clearly see that

retention. So you can clearly see that they both are related event and one calls the other. Anyway, so here again like you will do the validation and you will like go through the numbers try to

see like if it makes sense because otherwise like you can't just present it as is. you have to like do some math on

as is. you have to like do some math on your own to figure out like this actually makes sense or not. This is my favorite analysis that it created. So it

basically shows it's the cohort analysis where this column column A is the signup month. It

tells me what month customers signed up in and like how their retention has been.

Yeah. Or weeks.

And then you can see like April 27th is definitely retention is off for pretty much regardless of who signed up when.

The interesting thing about this is you're kind of walking through the the actual Excel docs that's created. So,

one, Codeex has built Excel docs for you and you can now go and view them, which is very valuable. And two, I think you're giving people real good insight into what data analysis is really about,

which is like you're trying to find outliers and and you know, in each of these different Excel tabs that you're looking at, it's like very clear. It's

like, oh, something happened here. and

you're trying to pinpoint when here is and then what happened around that period of time to actually understand that you need to do something about it.

And and normally when you have a big performance change in a whether it be a marketing team or your business, it's because you did something or because something happened to you that somebody else did and you're trying

to pinpoint what that is so that you can remedy that problem. And these are the types of Excel sheets that you need to look at. And if you're earlier in your

look at. And if you're earlier in your career, you may not know what things like cohort analysis means or you're not super into data. One, ask the LLM and LLM will explain it to you. And if you

want to work on your understanding, you can also just ask the LLM to create a dummy set of data and say, "Hey, I'm trying to learn how to better analyze these types of problems. Can you create

dummy data and walk me through and help me figure that out?" And you can learn that in real time and you can actually close a lot of the gap to to expert knowledge like somebody like you has.

But I think this is visualizing them into spreadsheets a very helpful way versus just seeing like the couple line summary in codecs.

Yeah, exactly. And then um maybe you can like look at the spreadsheet and come up with like new hypothesis. For example,

what exactly happened in April 20th week? Why is our retention so high? So

week? Why is our retention so high? So

like I would say like that's another analysis opportunity that you can like take back to your team and see like can we investigate what we did in April 20th and can we repeat it more often. So it's

possible that this may be related to another launch or something like that that drove customers back to the site.

So the second deliverable that we had is our deck which is basically what we want to present to leadership. So as you can see like it put together how many seven

slides and in slides basically is what we will take to the leadership and tell it like yes retention fell because mobile 4.3 version and these are the

next steps we're taking to basically solve this problem but for your 2PM remember that earlier I said like we're working on a scenario where your VP or your director is like can you explain why retention dropped now you're going

to be able to like take this and not only share what you found but also have a presentation if they want to like get on a call with you and here you can explain that this is why customer

retention dropped and it is related to the mobile launch that we did in April 27th week and then these are like the three things that we have done like this to resolve things back to normal always

go with what you're going to do about it don't just go with like the problem and the root cause always tell the leadership what exactly you're doing about it because that's going to be their next question and you want to be ahead of it this is just my pro tip

Um that is a good pro tip.

Anticipate the questions and then have the answers ready or prepare them in your deck. So if we look at this deck

your deck. So if we look at this deck like as I said earlier like codeex doesn't do a great job with decks like they don't look the prettiest but for somebody like me who doesn't enjoy

decks. I will take this and maybe I'll

decks. I will take this and maybe I'll like modify some things like look through like the data points that is uh spitting out see if I want to add something or doesn't want to add something then I will like modify it.

And here you can see like there are some mistakes that it has made here. So I'll

definitely like go through it and fix it. But as like a baseline working deck

it. But as like a baseline working deck that I can like take over and change. I

think for that it's already did a great job basically giving me something to start with.

Yeah, it's a great starting point. It's

really easy to make slides look better, especially with some of the other models. Claude and Gemini are great. And

models. Claude and Gemini are great. And

I like your point of like getting ahead of the objections and you know even that's like a prompt modification of like in the deck make sure you're including slides that would address any

possible questions or objections you know a leader at the company would have would it would be a great way of doing this right okay as we're about to close out you have given us a master class in how

to think about data how to analyze data how to solve problems with data is there anything else that will people really need to know to make sure that they get this right because I want anybody watching this to say I can go do this

and feel really confident about it.

Yeah. I say like one thing that we briefly touched on but we didn't fully discuss is when you have your data like there is no such thing. Yes, we talked about like some data is missing and we

only have subset of data but we didn't talk about like the data that you have what is like how dirty or in cleanup it needs to be. So if you look at the codeex like it started it just assumed

the data is already clean and there's like nothing needs to be done. So here

if I were to like redo it all over again I will definitely ask it to like look at what are like missing values what data needs to be cleaned and obviously like you're going to look at the data

yourself too but definitely follow that step because if your data has missing values or if it's incorrect then you're the answers that you're going to get is incorrect. Anybody who's like creating

incorrect. Anybody who's like creating data analysis with AI just working under the assumption that the data is perfect which in real world scenario it's never really perfect. So like that's up to you

really perfect. So like that's up to you to figure out how to get that clean data set from where and if there is missing data like use AI to kind of like clean that up and normalize it before you do

the analysis. Now I would say that what

the analysis. Now I would say that what I showed today it's not prominent today at all organizations. There are certain organization enterp companies that have adopted AI. For example, when I was at

adopted AI. For example, when I was at Google, like we were heavily into using Gemini for data analysis. I know Meta is specifically pushing the PMs and

basically everybody to become take over data analysis as one of the skill set and use Agentic analytics tool that they have adopted to like ask good questions and get an answer. I would say like 5 to

10 years from now, we're going to see more and more of this where this is going to become like a core skill set for anyone who wants to do data analysis and or whoever touches data. So if

you're learning this today and like practicing this, you could not only be like doing your work in a more productive way, but you could also teach other people in your team and in your

organization how they can stay ahead of this and be the leader. So right now everybody's looking for people who like use AI to like do your day-to-day job.

So like if you are that person, you could be that person at your company who is teaching other people how to do it.

Yeah. kind of your closing call to action is that this agentic data analysis is going to be a huge growth trajectory of career over the next few

years. And if you're looking to kind of

years. And if you're looking to kind of build your career or build the quality of company that you're trying out there build, you need this as a skill set. And

it's going to differentiate you out in the market because gone are the days of very traditional data analysis. You

still need those fundamental skills, but the work needs to get done in this new and modern way.

Yes. And for all my data analysts and data scientists and data engineers watching, your jobs are safe. They're

not going away yet change how we do things. But

things. But correct, you're not getting replaced.

You might be doing more and doing that in different ways and and there there's an evolution of those jobs, but I think if one of my big takeaways is the human element of data analysis is more

important than ever, not less important.

Exactly. And that is a huge takeaway for everybody. You've given us a master

everybody. You've given us a master class on how to actually get started and learn and basically get tough questions answered in a new and better way. Thank

you so much, Sundus, for joining us. We

really appreciate you. We'll see

everybody next time on Marketing Against the Green.

This data is wrong every freaking time.

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Whoa. I can see the client's whole history. Calls, support tickets, emails,

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