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AI For Data Analysis In 21 Minutes

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AI For Data Analysis In 21 Minutes

Tina Huang 26.10.2025 98 666 просмотров 3 465 лайков обн. 18.02.2026

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Start generating your own beautiful AI videos with LTX-2 👉 http://bit.ly/4oGIeAB 🤖 Want to get ahead in your career using AI? Join the waitlist for my AI Agent Bootcamp: https://www.lonelyoctopus.com/ai-agent-bootcamp Want to supercharge your data analysis workflow with AI? In this video, Tina breaks down how to use AI tools like Perplexity, Claude, and ChatGPT to analyze data faster, smarter, and with fewer errors — from market research to financial analysis and visual storytelling. You’ll learn: 💡 How to use Perplexity’s Deep Research for market insights and competitive analysis 📊 How to access real-time financial data with Perplexity Finance — stock tracking, peer benchmarking, and economic indicators 🧠 How to apply AI-assisted research methodology to validate your findings 📈 How to structure your workflow using the DIG Framework (Describe → Introspect → Goal-set) 📉 Plus: Tips for building dashboards, automating EDA, and avoiding AI hallucinations Whether you’re in finance, marketing, or research, this video will show you how to combine reasoning-based and programmatic AI tools to transform raw data into actionable insights. 🤝 Business Inquiries: https://tally.so/r/mRDV99 🔗 Tools & Resources Mentioned Perplexity AI Finance: https://www.perplexity.ai/finance Claude AI: https://claude.ai/login?returnTo=%2F%3F ChatGPT Advanced Data Analysis (Code Interpreter): https://chat.openai.com Building 5 AI Apps in 30 Minutes (ChatGPT + Lovable Tutorial): https://youtu.be/dLxz6zDGzUQ?si=sHRMPdDU1uq9OKoN 101 Apps You Can Vibe Code: https://youtu.be/vMyk77NNJeU?si=c73zh1x1owg4WaYO ======================== 🔗Affiliates ======================== My SQL for data science interviews course (10 full interviews): https://365datascience.com/learn-sql-for-data-science-interviews/ 365 Data Science: https://365datascience.pxf.io/WD0za3 (link for 57% discount for their complete data science training) Check out StrataScratch for data science interview prep: https://stratascratch.com/?via=tina 🎥 My filming setup ======================== 📷 camera: https://amzn.to/3LHbi7N 🎤 mic: https://amzn.to/3LqoFJb 🔭 tripod: https://amzn.to/3DkjGHe 💡 lights: https://amzn.to/3LmOhqk ⏰Timestamps ======================== 00:00 Intro & What You’ll Learn 00:24 Agenda 01:05 When To Use AI (ACHIEVE) 01:35 Human Coordination 02:09 Remove Tedious Work 03:19 Safety Net Checks 04:16 Creativity Boost 05:12 Scale Great Ideas 06:24 Quiz 1 06:28 Sponsor: LTX2 07:31 The DIG Framework 07:57 Step 1: Describe 10:12 Step 2: Introspect 12:21 Step 3: Goals 13:28 Beyond Spreadsheets 15:01 Traceability & Replication 15:42 Quiz 2 15:48 Examples Overview 15:52 CSVs → Trends & Forecasts 16:47 Visuals & Dashboards 17:05 Multimedia Workflow 17:48 Zipfile Automation 18:19 Auto-Organize Files 18:46 Convert to Utilities 19:49 Quiz 3 19:59 Beyond Analysis 20:29 No-Code Apps 20:49 AI Investment Agent 21:26 Wrap-Up 21:37 Final Quiz 📲Socials ======================== instagram: https://www.instagram.com/hellotinah/ linkedin: https://www.linkedin.com/in/tinaw-h/ tiktok: https://www.tiktok.com/@hellotinahuang discord: https://discord.gg/5mMAtprshX 🎥Other videos you might be interested in ======================== How I consistently study with a full time job: https://www.youtube.com/watch?v=INymz5VwLmk How I would learn to code (if I could start over): https://www.youtube.com/watch?v=MHPGeQD8TvI&t=84s 🐈‍⬛🐈‍⬛About me ======================== Hi, my name is Tina and I'm an ex-Meta data scientist turned internet person! 📧Contact ======================== youtube: youtube comments are by far the best way to get a response from me! linkedin: https://www.linkedin.com/in/tinaw-h/ email for business inquiries only: tina@smoothmedia.co ======================== Some links are affiliate links and I may receive a small portion of sales price at no cost to you. I really appreciate your support in helping improve this channel! :)

Оглавление (30 сегментов)

0:00 Intro & What You’ll Learn 101 сл.
0:24 Agenda 177 сл.
1:05 When To Use AI (ACHIEVE) 81 сл.
1:35 Human Coordination 134 сл.
2:09 Remove Tedious Work 273 сл.
3:19 Safety Net Checks 215 сл.
4:16 Creativity Boost 200 сл.
5:12 Scale Great Ideas 275 сл.
6:24 Quiz 1 14 сл.
6:28 Sponsor: LTX2 193 сл.
7:31 The DIG Framework 81 сл.
7:57 Step 1: Describe 548 сл.
10:12 Step 2: Introspect 534 сл.
12:21 Step 3: Goals 298 сл.
13:28 Beyond Spreadsheets 355 сл.
15:01 Traceability & Replication 152 сл.
15:42 Quiz 2 20 сл.
15:48 Examples Overview 14 сл.
15:52 CSVs → Trends & Forecasts 229 сл.
16:47 Visuals & Dashboards 75 сл.
17:05 Multimedia Workflow 157 сл.
17:48 Zipfile Automation 119 сл.
18:19 Auto-Organize Files 126 сл.
18:46 Convert to Utilities 210 сл.
19:49 Quiz 3 43 сл.
19:59 Beyond Analysis 106 сл.
20:29 No-Code Apps 66 сл.
20:49 AI Investment Agent 144 сл.
21:26 Wrap-Up 37 сл.
21:37 Final Quiz 42 сл.

Intro & What You’ll Learn

I learned how to do data analysis with AI for you. I guess we can call it vibe analyzing. But really though, I took 11 courses on this topic. What can I say? I used to be a data scientist at Meta. I love data. I use data every single day. I'm going to save you the time and money that I spent buying these courses and give you the cliffos version of what I learned. As per usual, there'll be little quizzes throughout this video. So, pay attention. All right, let's go. A portion of this video is sponsored by

Agenda

LTX2. The outline of today's video is first I'm going to cover when is it useful to use AI for data analysis. Then we'll talk about the dig framework for how to approach analysis. But of course to make it all concrete we need some examples. So I'll then be showing you lots of examples. And finally I'll explain how to take this even further and take your data analysis and build it out into dashboards or even AI applications. I do want to make a note that a lot of the courses and examples are focused on using chatbt as the tool for data analysis. But that is not the case that you have to use chatbt. In fact, you can switch it off at Gemini and Claude would work the same way. In fact, sometimes they would actually work better. So, don't feel like you need to be married to a single tool. And I'll actually call out if there is a tool that I think would work even better. Okay, let's start off with

When To Use AI (ACHIEVE)

when we should be considering using AI for data analysis. Well, from the course Chachi PT advanced data analysis, Dr. Jules White, the instructor from Vanderbilt University has an acronym for this called achieve. He explains that there are five different areas that is useful for using AI in data analysis. Aiding human coordination, cutting out tedious task, help provide a safety net for humans, inspire better productivity and problem solving, and enable great ideas to scale faster, achieve. Aiding human

Human Coordination

coordination refers to helping people work better with each other because people actually tend to be quite messy, you know, and there's a lot of miscommunications. like back and forth between people. So, there's a lot of room for improvement here that AI can help with. Say, for example, you're in a meeting with a bunch of people and you have this meeting transcript. You can actually put it into AI and say, "Actal assistant. Read the following meeting transcript and provide me a summary of the key points of discussion. " This is just an example. I'm sure you can think of a lot of other scenarios where there is a bunch of data that can be analyzed such that you can provide more clarity for humans. The second part of the framework

Remove Tedious Work

is to cut out tedious task. Just as that suggests, it's best to let AI to be able to do things that are very repetitive and boring for people. For example, say you're hosting a workshop and you ask people to sign up for the workshop and provide different types of information like what their name is, what their occupation is, which department that they're in, what their interests are. Instead of having to go through this and manually analyze it, you can tell the AI that this is the list of people that registered for my workshop on prompt engineering and chatbt. Describe the data in this file. By the way, asking AI to describe data is best practices which we'll cover a little bit later. But yes, so the AI will be like, okay, like you know, this file contains this type of information. It's a CSV file and it contains like timestamp, name, the email, the department, um this is a university workshop, how they're using chatbt and their tools already, the role that they hold, etc. You might notice that people are filling their department names in a lot of different variations. So you can actually ask the AI, there seems to be a lot of overlap between departments with alternate spellings. Can you list out all the departments and then do some intelligent grouping of them? And then you can ask it to create a bar chart showing the total number of registrations per department. This is the kind of data cleaning and visualization that is pretty mundane and AI is able to do this much more quickly.

Safety Net Checks

Third part of the framework is to help provide a safety net for humans. You see people often say like oh AI has a lot of hallucinations and that is true. AI does hallucinate but people hallucinate too. People make a lot of mistakes, like some really dumb mistakes. I make dumb mistakes like literally constantly. Wrote my name wrong on a form yesterday for example. So that is why having AI as a backup is actually a really great idea. Say for example, you're on a business trip and you need to ask for a reimbursement. So you need to like generate some invoice thingy and then make sure you have all the fields covered. If you're anything like me, I am not very detail- oriented. I probably will make a really dumb mistake. So, as a safety net, you can actually upload this invoice into the AI along with the business expense policy and ask read each page of the attached business expense policy and see if the attached receipt complies with it. So many other examples of this. Every time you need to submit like an insurance claim, you need to like read some sort of document uh looking at like travel policy whatever like yeah so many examples of this. The

Creativity Boost

next part of the framework for when to use AI for data analysis is the IEV which is inspire better problem solving and creativity. This is another thing. People always feel like AI is going to make people less creative and is going to take over creative things, but that is not the case. It's all about asking the right questions. Say, for example, you have a PowerPoint presentation that is really, really important. You can actually upload those slides into AI. Ask them to quickly summarize it and then ask it to act as a skeptic of everything I say in this presentation and find flaws, my assumptions, assertions, and other key points and then generate 10 hard questions for me. This is a way for you to actually force yourself to think about the questions that you could potentially be asked and come up with better ways and better solutions for answering them. People are actually very rigid creatures. We tend to think in a very specific way and it's very hard for us to actually expand past that. So using AI as a tool to help us expand our creativity is actually really helpful. And finally the

Scale Great Ideas

last part of the framework for when you should be using AI for data analysis uh is the E, which is enable great ideas to scale faster. Let's go back to that workshop example. You're doing a workshop on prompt engineering and you have people coming from all types of different backgrounds um who are interested in all types of different things and all types of different levels. After you give the AI the signup form and analyze all the data about your participants, you want to create a cheat sheet for each of them that is most relevant to them. What you can actually do is ask the AI to map each attendee with their specific domain of interest and then generate a column called ideas that includes the corresponding idea/prompt um to put for their cheat sheet. This way now your CSV file for each attendee also contains a very specific prompt idea that is specific for that attendee. And then after the workshop, you can actually send them an email with their specific little cheat sheet. I'm sure you can see prior to AI, this would have been so hard to do if you have more than just like 10 attendees to be able to come up with like a custom cheat sheet for each person. So, whenever you're thinking about if you should use AI to do a certain analysis, you can think back to this acronym. Of course, I haven't yet covered exactly how it is that you should be approached to these analysis, which is what I'm going to be covering next. But first, let's do a little quiz.

Quiz 1

Please put your answers in the comments. This portion of the video is sponsored

Sponsor: LTX2

by LTX2, the new AI video engine for creative workflows. And this one honestly blew me away. What stood out to me isn't just the quality, it's how LTX2 can finally tell a story. And if you can't guess by my career choice, storytelling is what I live for. Most AI video models just give you short looping clips. A few seconds that look great but don't really say much. LTX2 though can generate up to 15 seconds of continuous video with synchronized audio which means full monologues. — Super villains also have feelings. — Dialogues or even short scenes with music and natural sounds. It's smooth, coherent, and feels cinematic. The kind of storytelling range that's missing from AI video tools. As someone who's been creating content for over 5 years now, this one really impresses me. You can build short narratives, generate B-roll and snippets, or even cinematic transitions, all with a single prompt. You can now try out LTX2 to create your own AI videos. The link is in the description. Thank you so much LTX2 for sponsoring this portion of the video. Now, back to the video. From the

The DIG Framework

course, ChatBT Plus Excel, master data, make decisions, and tell stories. There is a nice little acronym for how it is that you should be approaching data analysis using AI called DIG, which stands for description, introspection, and goal setting. By the way, if you do have a little bit of background in data, like data science or data analysis, um it's basically the same as EDA, exploratory data analysis, but specifically for using AI. The first step of dig, which

Step 1: Describe

is describe, it's a way for you and the AI to explore the data together. This step is very important because it helps both you and AI gain a familiarity for the data and to also notice if there's any issues with that data. This is going to help a lot with hallucinations and issues down the road. So very similar to normal EDA processes, after you upload like a spreadsheet or whatever data it is that you give to the AI, like let's just say like a spreadsheet in this case, you would ask the AI to list out the columns in the attached spreadsheet and show me a sample of the data in each column. For example, if your spreadsheet contains data about different roles um and different salaries, the AI would be able to output and say, "Here are the columns for your spreadsheet along with a sample of the data for each column. " So the column name you could have like salary ID, job ID, max salary, med salary which is median salary, min salary, pay period, currency and compensation type. You can already notice that under max salary and min salary you have nan which is not available. This is important to note because hallucinations tend to happen when you have things like missing data or incorrectly formatted data. So if you see something like this, the first thing you actually want to do is to confirm that is it just that DAI is not parsing your data correctly. So you actually want to go in and see like is it actually not available or is there like a parsing problem? And if there actually is a parsing problem, you want to then tell the AI, hey, you are parsing this incorrectly. This is actually how you should be parsing it. Or maybe the data is just not available. Then you just want to make a note of this for the future. We will get back to that. But first, you actually want to do a few more random samples. Just ask your AI like take a couple more random samples of the data for each column. Make sure you understand the format and type of information in each column. What we're basically doing here is verifying that the data is being parsed correctly and the AI has correct understanding of each of these columns. You can even ask it what do you think each of these columns represent and it might tell you that salary ID appears to be a unique identifier for each entry. Job ID is likely unique identifier for jobs or position max salary, min salary and med salary not all entries have complete salary data etc etc. So this is a way for you to validate that the AI understands what's happening also that you understand what's happening. The best way to think about this is that your AI is a very competent but still very junior developer or data scientist or data analyst. So you need to make sure it understands what is the data that it's actually receiving. Otherwise any analysis that you do on top of this could potentially be wrong. So after you do this, you want to move on to the next

Step 2: Introspect

step of the dig framework which is introspection. This is when you want the AI to start looking at the data that it finished describing and think about the patterns and relationships that exist in the data. This is also another great way to catch any misconceptions that the AI may have. Notices were just being like very skeptical all the time. Very important. You can just ask tell me some interesting questions that could be answered with this data set and why they would be interesting. And it might come with some questions like is there a relationship between compensation type for example base salary and variability in salary ranges. It's saying why it's interesting is that understanding whether certain compensation types like bonuses or equity are more likely associated with higher salary ranges can help employees and employers make more informed decisions about how they structure pay packages. Here's a question that came up with that could be a red flag. The question is, are there any noticeable patterns in salary data for different currencies if additional currencies exist? So, when you see this, you want to think to yourself, oh, like, is there actually other currencies that exist? And you might want to double check yourself to see if there's actually data that is not USD. In this case, there actually are no other currencies. So, that's when you want to tell the AI all currencies are actually listed in USD. So, it knows that information moving forward. If you catch your AI asking questions like these or you're catch your AI like asking questions that you know cannot be answered by the data set and ask you to generate some more questions that can be answered from the data set. In this way you're really helping the AI and yourself make sure that you really understand what's happening in the data set. It's also actually quite a good exercise because sometimes AI will come up with different things and different analyses that you might have not have thought of doing. I know this might seem a little tedious and you just want to skip to like making graphs and charts and doing analysis, right? But do not skip these steps, okay? Trust me. Because like that's the thing with data. It's one of those things where if you mess up, it will just propagate throughout your entire analysis. So it is really worth the effort to actually make sure that everything is understood correctly. And this is actually the same with humans too. It's not like an AI problem. Even when I was a data scientist, I spent a significant amount of time doing exploratory data analysis, making sure that I understood exactly what was happening in data and clarifying all that information as well because I knew if I just like rush into things, I'll probably end up making a mistake and be very embarrassed when someone catches my mistake or worst case scenario get fired because I made a really dumb mistake and then, you know, lost the company a lot of money or something like that. Anyways, so after you do this, the third step of the

Step 3: Goals

framework is goal setting. It's very important that your AI understands what it is that you're trying to achieve. Like if you just went like analyze this data, your AI is going to be like what the heck like you know what does that even mean? What am I supposed to analyze? What's the result? So it's the same. You got to be like very clear about what the goal actually is. So you could tell the AI, my goal is to answer a couple of these questions, the questions that you know it generated previously and turn them into a really exciting interesting report to post on LinkedIn. This really helps provide context because then your AI is able to do this analysis and then things like give you LinkedIn ideas how it is that you can put it together in the form of LinkedIn. This is going to be very different if you were actually analyzing this data in order to, you know, like do something very serious like generate like a report for your boss. This is also just part of good prompt engineering practices. So, if you do feel like you want to brush up your prompt engineering a little bit to be able to be more clear about what it is that you want, I do recommend that you check out a video that I have over here which covers the foundations of how to do good prompt engineering. So, check it out over here. Anyways, whenever you have a data set that you want AI to help you analyze, it would be really helpful for you to go through this dig framework. It is a really great foundation and you can build on top of this as well. Now all the stuff

Beyond Spreadsheets

that we talked about earlier uh is pretty standard for if you're doing any type of data analysis using like Excel, Python, SQL or whatever. It's just like maybe more convenient doing in a conversational fashion with AI. But there are certain things that you can do with AI that would be extremely difficult for you to do just using these traditional tools. Like for example, if you're job hunting right now and you have access to this data set, you could be thinking like, oh, like I'm looking for a job that is between like 50 to $80,000 based on the East Coast and specifically works with wood. I don't know, something like that, right? So in this data, there is no specific section that's like works with wood/notwork with wood, you know, or like materials that you're working with. And it also doesn't specify like is it east coast or west coast. It's just like the location like Chicago, right? But because of Genai's capabilities, you're able to like filter through this data um in a way that's far more intelligent to be able to find the rules that you could be potentially interested in. This would be so hard to do if you didn't have Genai. And later on in the video, I have a lot more examples which I'll show you uh like Genai specific really cool data analysis things that you can do. There's also one more thing that I thought was really cool in this module of the course, which is the idea of traceability and replication. I thought this was like super clever because a major issue that people face when doing traditional data analysis is that they would like come up with some sort of thing and then it would be stuck in like a Jupyter notebook or like whatever and it would actually be very difficult for other people to reproduce that analysis. But with AI, you can actually ask AI to come up with a traceability document that allows other people to be able to perform the same analysis to validate the results. You can

Traceability & Replication

ask let's create a traceability document to make sure that others can one know what data was used two how the analysis was performed and three threats to validity. We want a guide for someone else to be able to replicate and know the limitations of the analysis. You can save this traceability information as like a readme. md. Uh don't worry if you don't know what that is. It's just very common for software engineers to be able to store it this way but you can kind of store it like a word document whatever doesn't actually matter. And then for each analysis and visualization, you can actually ask it to write a single Python script that performs the full analysis to produce the visualization and the results. I think it's a really clever idea and really smart thing to do if you're doing any type of data analysis using AI.

Quiz 2

All right, time for our next little quiz. Please answer the questions on screen in the comments. Yay. Okay, we

Examples Overview

can move on to some examples. I'm really excited for this section. First example

CSVs → Trends & Forecasts

is super simple but is actually really helpful is it's pretty much like any type of small document you can just directly upload do dig on it and then have it proceed to analyze that information and transform it in whatever way like if you have structure data like say you have a CSV form that has like all the different types of inventory throughout the past few months you can ask it to filter it for different types of inventories you can ask it what are the trends in the inventories over time are there certain items that are becoming more popular less popular maybe you want to like remove those if there's too much of that in the inventory. You can even ask to come up with a predictive model to see what are the inventories that you should be stocking for the next few months so that you're able to optimize the amount of inventory so you don't have too much and you also don't have too little. You can also do visualizations both static visualizations. You might want to make a bar chart that's ranking all the different types of inventory that you have. You might want to make a time series graph over time. What are the changes inventory? And you can also make dashboards as well, interactive dashboards. for this specific one. Cloud

Visuals & Dashboards

as of the filming of this video um is a lot better than the other models to create interactive dashboards. It also usually is the best at writing the code as well in order to do the analysis and it tends to hallucinate less. Again, this is at the time of this filming, so I don't know if this is going to change in the future, but just FYI, AI data analysis with different media

Multimedia Workflow

forms is also a really cool application. For example, you can have like a video and you can ask it to extract 10 frames from this video evenly spaced out from 1 second apart. Then you can ask it to take these images, resize the images, make it like 300 pixels wide and say convert it to grayscale and increase contrast by 30%. You can ask it to do things like combining the images to animated GIFs that flip the to the next image at 1second intervals. You can ask you to turn the images into PowerPoint presentations and then catalog all of the images into a CSV file with the name of the image and movie file that the image was extracted from and the operations applied to it. So really cool that you can manipulate uh multimedia using AI. Prior to AI, this would have been so difficult to do. Another example of data analysis

Zipfile Automation

using AI is by automating things using zip files. Zip files are very, very convenient and they're amazing because not only are you able to put a lot of different files into a single zip file, you're also able to maintain folder hierarchy in the zip files themselves, which means that you can actually zip together a bunch of different files together and ask the AI to mass analyze them all together. So you can have multiple Excel files that you're telling it to combine and search and do whatever with it. Then afterwards, you can build all back together and then send it back to you. Also, Grave, if you need help organizing different files, you can ask

Auto-Organize Files

the AI, one, I want you to help figure out what is in them by opening and reading each one to create a summary. Two, I want you to propose a folder structure that would better organize the files. Three, I want you to propose better naming for each file using just A to Z and 0 to9, keeping the extensions. And four, when you have all of this done, show me your proposed folder structure and name. Then it's going to do that. And then finally, once you're happy with it, you can ask her to zip everything up again and then send it back to you and voila, everything is well organized and beautiful. And the final example I'm going to show from the

Convert to Utilities

course is a little bit more advanced, but so cool. This is when you can actually turn conversations into software programs. Let me explain. So, say for example, you have like a sequence of analyses that you did, right? Like for example, maybe you have like some sort of movie and then you ask it to get like 10 frames from this movie spaced 1 second apart. uh maximized it, I don't know, like did some photo manipulations on it um and then combine them together and then generate some descriptions for it and then put them all together into a CSV file. You can then ask the AI, turn this process into a Python program that I can download and run on my computer and provide the path to the documents as command line arguments. Zip up the program for me to download and then it can literally go and actually like write a script that performs all of these different steps and then put them all together in an executable program. That is so cool. You can literally do this for any sequence of analyses that you do. You can just like automate it like that. At least for me, that blows my mind. All right. I can

Quiz 3

literally go on forever, but I'm gonna stop for this section for now and I'm going to put the next little quiz onto the screen. Please answer these questions and put them in the comments. Okay. So, I wanted to include this final

Beyond Analysis

section because I wanted to make sure that you understand that just doing the analysis using AI, you don't need to stop there. There's actually so much more that you can do on top of that. From the examples that we already seen, we can take this data analysis and then use it to generate emails, use it to make like social posts, use it to generate reports, PowerPoint slides, even build software programs and dashboards. But that's not all. You can even build full-on applications based upon these analyses. And no, you don't actually need to know how to code. You can just use pipe

No-Code Apps

coding. Say you've analyzed a lot of traffic data. You can actually make this into an application that analyzes real-time traffic data and then like I don't know gives alerts to people. uh where generates reports based upon traffic incidents. You can have application that's able to take videos and blur out people's faces or like different identifications within the video. Here's an example of an

AI Investment Agent

investment research AI agent uh that people who join our AI agents boot camp will build that has an entire database with information about investments and it has this interface where the user is able to ask it specific questions and it will analyze that data to generate certain types of responses, conversations and reports. Yeah, there is so much that you can do. Data truly is power and being able to analyze and harness that power by using AI just opens up so many possibilities. All right, I'm going to stop here. If you do want to dive into how to actually build out these like applications, agents, and things like that, I'll link a few videos in the description that you can check out that goes into a lot more detail about how to do this. But for this video, I'm going

Wrap-Up

to end it for now. I really hope that this was a very helpful video for you and you have lots of ideas for how to analyze your data now using AI. Vibe data analysis. As promised

Final Quiz

here is the final little assessment. Please answer the questions on screen and put them into the comments. Thank you so much for watching until the end of this video and I will see you in the next video or live stream.

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