Explore Data Science and Resources for DataX

0:00Hello and welcome to the first skill in the DataX certification course, where you're going to get the chance to apply learning strategies for the DataX certification. My name is Jonathan Barrios and I'm excited that you're here. Before we get started, let's check out some of the domains that we're going to be working with in this course. So here we have the five domains. The first one here is Mathematics and Statistics, and you can see that it's 17 %. So it's not a lot of math and statistics, but it is mostly focused around machine learning.

0:33And then we have Modeling, Analysis, and Outcomes in Machine Learning, which also includes deep learning. We'll talk more about that. Operations and Processes, and then finally just a sliver for specialized applications of data science. Now that we've gotten that out of the way, let's talk about everything that we 're going to cover in this skill. Number one, we reviewed the domains, the five domains. And the reason that we did that is because I have some existing adept courses, and that's going to be number two, and these adept courses cover a whole range of domains.

1:08Not everything, not so much the math, but we're going to get into this and I'm going to show you each the course because they are deep dives resources, or I can say courses. And why am I mentioning that here? Well, think about it. When we're talking about the five domains, these are quite in depth. That's the thing about data science, right? It encompasses a lot. Not only we're talking about machine learning and deep learning, but there's math, and then there's analysis, data wrangling. I mean, we can go on and on, so I think you get the idea here.

1:40But we're not going to be able to go into deep dives in all of those domains because this course would be super long. So I'm trying to leverage the existing courses that I have where I cover all of this stuff in a deep dive format. I thought it would be really appropriate to list all of those out and show you how to navigate adept to find my courses in case you need to deep dive. Chances are you're going to understand most of this, or if you don't, then you 're going to have some additional resources. Whatever the range of your skill set is right now, I hope that this pipeline of data science will be an invaluable resource to you.

2:18And it's so cool that I just finished building that resource. So I call it the zero to hero data science pipeline, and it just finished it. So that means that now that we're diving into data X, you have that as a resource. And number three, we're going to talk about the Feynman technique, and this is going to also work with this deep dive, and also an assessment skill for skill gaps. And this is coming next. And let me explain a little bit about this Feynman technique.

2:49So Richard Feynman is a physicist and educator, very well known in physics, but a wonderful educator. And if you don't know Richard Feynman, definitely look him up on YouTube, he explains quantum physics like nobody does with a lot of excitement. But this technique is really awesome. And what it does is it's a learning method where you deeply understand a concept by pretending to teach it to somebody else. Ideally, a child, maybe even a five year old, right? And by doing this, you're going to be explaining it in simple terms, which forces you to identify and address any gaps that you might have.

3:26And we're also going to identify gaps with the assessment skill. And I'm going to give you answers and explanations at the very end of that skill, so you're not going to be guessing if, you know, I'm not just going to leave you hanging with a bunch of quiz questions and then no resolution. We're going to cover those, and I'll even go as far as to explain each one of the answers. And so again, why are we doing the Feynman technique? Well, not only is it going to help you identify the gaps in your understanding, but it's going to give you an invaluable skill, which is using teaching to learn.

3:59And that's one of the learning strategies I'm going to share with you. And it really has to do with the five domains. You can actually use the Feynman technique for that. And you can also, once you identify those gaps, either using the Feynman technique or the assessment skill coming up next, then you can go to the adept courses and do the deep dive right here. So all of this works together, and you might be asking yourself, great, but how does the Feynman technique work? Let's cover that right now. So number one, you're going to choose a concept.

4:31And what you're going to do is just whatever topic, or it could be one of the domains, or one of the domain objectives, which I'm going to share with you in the next skill. And then step two, you write an explanation. And this is where you pretend that you're explaining this concept to someone with no prior knowledge, like a child . And then you write down everything you know about it. And one good example here, and you've probably seen this before, when you have a five-year-old or any child, really, that's asking, why is the sky blue? And then the parent says, because, and then the child says, why? And it's like, well, because it's, and then maybe they'll attempt some kind of an explanation.

5:10And then after that surely comes, why? And that's the process here by asking why you'll get to the point where you don't know something, right? When you're talking about the natural world, well, of course you're going to get into physics and ultimately quantum physics and go, it's a rabbit hole. So you can't know everything about everything, but this technique is really simple, and I use it all the time. And then number three, you identify gaps. And that's the most important part. You review your explanations, and then you identify areas where your understanding is unclear.

5:44Or where you use complex terminology, because we don't want to use any jargon. When you start to do that, that's a red flag. Step number four, you want to research and refine. So go back to your source material and fill in any knowledge gaps and rephrase your explanations to make it simple and accessible. You want this to be something that a child can even understand. And why is that? When you can explain it in simple terms without using jargon, then you're going to have it stored in long-term memory, and it means that you have a pretty good understanding of that, which is perfect for taking the data out.

6:21And you're taking the data X certification. That works well with the concepts because they want to make sure that you understand them. And if you understand the concepts, then you have a much better chance of getting each of the questions right in a situation where there's added pressure . Right? So that's the whole idea here. And this is a useful tool. And this is actually how the physicist Richard Feynman taught himself as he was studying physics. So it's a proven technique, not just with myself. You use it all the time, and a lot of people use it, but Richard Feynman used this technique to great avail.

6:55And to recap, you're going to gain a deep understanding because you're using active learning, and you're going to understand all of the topics rather than memorizing facts, which is terrible, especially for certification like data X. And you're going to identify knowledge gaps. And this is going to help you pinpoint areas where you need to further study and also save time if you already know something. And the goal here is improved retention because the act of explaining a concept is going to reinforce your understanding and improve memory retention, which is crucial.

7:29All right. So that's it for this video because now we know almost everything that we're going to do. And in the next video, we're going to dive into my adept courses, which I call the zero to hero data science pipeline. And these are going to offer deep dive resources for you. And there's more than what I have listed here. here. See you there!

0:00Welcome back. All right, so now let's talk about each one of these courses. So the best way to do this, if you're new to CBT nuggets, then all of this will just seem like, well, this is adept. Let me make this a little bit bigger. But if you're used to the old CBT nuggets website, then this is going to be something that is new to you. And the reason I mentioned this is because this is something new and it's changing. So let me show you how to find these courses, because if you just type in this search area, it's a little tricky. The easiest way is to go to author and then go show more and then find my name, Jonathan Barrios, click on that. And then you'll see all of these courses. So my most popular one right now is Introduction to Machine Learning.

0:42But this is also a new one that just came out and its data science fundamentals. Now going back to this list, we're starting from the very beginning here. And this is introductory Python for data analysts. So if you don't have a programming background, this is a great place to start. And real quick, in the next video, we're going to talk about the origins of data science, because a data scientist can be someone that has a math background, or somebody that has a business analysis or business intelligence background, or somebody who's a programmer, and they're working with computer vision and deep learning and things like this. We're going to talk about where that term came from, because the reality of data science is that it's a lot of different paths. And the reason I'm showing you this is because depending on your path and your background, you may not have a programming background, or you may not have the math background, you may not be aware of what a t test is, or how to form a hypothesis for a hypothesis testing. And if you don't have that programming background, I have a lot of these resources here to share with you. So we're going to go over that in the next video.

1:48But that's why I'm showing you all of these different resources right now, because you could leverage these. And then we'll talk about the origins of data science , and then get into what is data science by talking about the domains. All right, so going back to this, this starts at the very beginning. So let's say you have a math background, but you don't have a programming background, this is a great place to start. If you don't have experience working with databases, SQL for data practitioners is a great place to get started. And then here we get into pandas and Python, which is really data manipulation and data wrangling. And the iSACA data science fundamentals, this is really for conceptual information. So a lot about the data science life cycle and things like that a little bit about machine learning, but really machine learning is going to be here. So the introduction to machine learning is a pretty in depth introduction, but it's definitely in an introduction. So it's not going to be alienating, it 's going to be quite accessible. And if you wanted to go into deep learning, I would suggest having a little bit of machine learning under your belt before diving into neural networks, which is deep learning.

2:57And I really think about deep learning as the newer version of machine learning . So let's take a look at each one of these. So let's go to, oh, and I didn't mention the CompT IA data plus. I would describe this as data analysis. So data analysis is also a wide field, not as much as data science. And I think the difference here is really machine learning. But they share, there's a lot of overlapping concepts. Again, we're going to talk about this in the next video. And here, if you don't have a programming background, Python development environments, which environments are going to use, this is really a great way to start because you're not a programmer, right? You're a data scientist or data analyst. And we use a slightly different set of tools compared to a programmer, you can use programming tools. And just go ahead and use something like Visual Studio code. But we're going to be focusing on things like Jupyter Notebooks and Google Colab, and these kinds of IDEs, just to, you know, clarify that. And great, if you don't have a programming background. Let's say you have a programming background and data science background, but you're not great with data wrangling and data manipulation. Again, this starts with Jupyter Notebook. And then it gets into how I mean, it goes really deep into working with data frames. And the data frame is the programmatic equivalent to

4:17a spreadsheet. So it's a table. And then we have a series, which is like a column. So we're really working with this pandas environment, which is a very common tool for data science. And here we have SQL for data practitioners. And we're going to use Postgres SQL and PG admins. So you can set that up. And actually, it's kind of fun. We work with a movie database in this skill, or this course, which is a collection of skills. And again, it's not too big of a course, but it's really a great resource if you don't have a background working with databases and SQL in particular. And conversely, we're working with Excel. So using Excel is maybe not the most optimal moving forward, because Python is such a popular path to take when working with data science and data analytics.

5:02But what's great about this is that we dive into descriptive and influential statistics and hypothesis testing, t tests and things like that very accessible. And then here 's where we turn up the heat a little bit. So we get into AI agents as an introduction. And then we get into probability. And then we define what machine learning is. And as we get into this, we started getting into data pipelines, linear regression, and then gradient descent for linear regression. This is a super important topic for machine learning. It's a very in depth course. And but it is an introduction.

5:37And then finally, neural network basics with the perceptron. And we start using PyTorch. And then we get into, let me show you down here, TensorFlow. These are the two of the most popular frameworks that we'll use when working with machine learning and deep learning. So I've covered them both. And then moving on to introduction to deep learning, this is a still an accessible course. But now we're getting into neural networks. So here we start with computer vision. And then we get into convolutional neural networks, which is basically for computer vision. And then we start working with real world image data. And then how to avoid overfitting. And when you start to identify any of these gaps that you might have, especially when you take the assessment scale, you're going to notice a lot of these terms are going to jump out at you. Like if you find a knowledge gap, for example, what is overfitting? Maybe you're not, you need to brush up on overfitting. You just, they're going to be in the title. So I try to use semantic titles for these, knowing that we would be searching for these courses if we had a knowledge gap.

6:40And last but not least, if you really want to get into TensorFlow, this is where we start to work with Jupyter Notebook. The main distinction here is that we're working here with Google Colab. And I'm going to show you that in just a second. Actually, let me show you right now. So if you go to col ab.research.google.com, you get this example. Let me open this example here. And this is just like Jupy ter Notebook, but it's, it's part of Google. So all you need is a Google account, a Gmail account . And once you have that, you can actually just go to colab.research.google.com. And these will be saved into your drive. And so these are text cells and then code cells. And you can execute these. And then you get the output right here. So the difference between the introduction to deep learning and the professional TensorFlow developer is this is going to be a lot more technical and not as accessible as the introduction to deep learning. We're going to be using Google Colab here. And really just, you know, it's more of an introduction. But here we're going to really just turn up the heat and use J upyter Notebook locally, which is a really interesting way to do that because you may not have a GPU powerful enough. So if that's the case, that's why we use Google Colab because you can use Google's GPU.

7:55And for $999 a month, you can get a pretty powerful, you know, an array of GPUs to use. All right. So I think that's about it. We covered a lot of information. And the reason that I'm doing this is just to give you an overview of what's available to you. Because in the next scale, you're going to get an opportunity to answer a bunch of questions. And you can also on top of that, use the Findment technique to identify any skill gaps that you may have. And when you identify them, then you might think, well, you know, I don't really feel very comfortable with SQL. Let me go to SQL for data practitioners. Or maybe you identify that you need some, you need to brush up on some computer's vision. So you would go to introduction to machine learning or deep learning. So the way that I would approach these two is to have some foundation of machine learning before going into deep learning. All right, in the next video, we're going to talk about the foundations or the origins of the word in the term data science. See you there.

0:00Welcome back. So the origin of data science. This is a really interesting exploration because there's no really correct answer here. There's a lot of historical evidence for different terms being used a long time ago. But really, this is something quite new. And the story that I heard that is unconfirmed, and I'm going to tell you this story, and then I'm going to refer to a YouTube video that we have, there's something that you can reference. But the story that I heard, which is really kind of similar, which is the hiring manager at Facebook was looking for a data analyst, and they were doing all of these interviews, but they really were turning up the heat in the data analysis department. They wanted to do some really innovative things. So they were looking for the really top talent. And when they found this person, they were like, this data analyst is perfect. And then they made a job offer. The term data analyst didn't sit well with a candidate.

0:54They're like, well, I'm a lot more than a data analyst, right? Because they had a lot of computer vision or not computer vision, computer science and machine learning techniques . So they were applying in statistics. So there was a lot of science happening. So I think you can know where I'm going with this. So they actually really brainstormed and then came up with this, they combined data, the data from data analysis and research scientist, and then smash those together to a mashup and his data science. And so that's a story that I heard. And I saw a video on it and it was the, I just don't know where that video is. It was a hiring manager at Facebook. But let's, I'm going to show you this other one and link the video below this video so that you can actually take a look at it for yourself. It's pretty interesting. So the one that I'm referencing is from LinkedIn, and it's DJ Patil, who was the data scientist under Obama. And what they did is that they were trying to actually come up with a similar situation, come up with a term, and they didn't know what to call this new role because it was a mixture of data analytics and we can put computer science over here and research scientists. So when you take all of these, you can see

2:11there's a lot of terminology and they were coming up with different ones. So what they did is they created a bunch of these job roles because well, they said it is LinkedIn. So we can do this. And the one that everyone applied to that everyone gravitated toward was data scientist. All right. So that's the story. So why am I talking about it like this? Like I said before, everybody has a different role. For example, in my case, I started, I have a programming background and I was a full stack engineer, but I was learning machine learning. That was something that I was really passionate about.

2:48So that was really easy for me to jump into from computer science and full stack development because of Python. I already knew Python and the Python libraries really make machine learning quite easy to get into. And very early on, I found a course on Stanford online from Andrew Ng on machine learning, one of the very, very first courses. And it was something that I just watched and I was immediately into machine learning. And so if you combine computer science and machine learning, all you need is domain knowledge to be a data scientist. And so my education path, because I knew these things, I was asked to continually make courses on data analytics, data science, and then machine learning. And so that's why I'm teaching the data X certification course here at CBT nuggets. That was just my natural path. But if you think about it, let's talk about different kinds of paths, people that I've worked with and paths that I know that are very, very distinct and not based in computer science. So I'm going to say computer science over here. And over here, we can say math and statistics. And here we can say BI for business intelligence. And you can also have a researcher. And I guess we can

4:05put data practitioner. All right, so we have 12345. And this is being a little conservative, because going back to DJ Patel, they were actually trying to find a terms like what do we call these people that do everything with data. So that's who we're talking about. This is a data scientist, people that do everything with data. So we have all of these different roles. And where do you lie here? So this is my path. So I was coming out of computer science, but also research. Okay, so these two apply to me. Now, I've worked with a lot of statisticians. And I 've worked with a lot of business intelligence folks, and people that I would just call data practitioners that were wizards with data, but they were working with Excel. And quite a few of the math and statisticians were also working with Excel. Here, I was working with Jupiter notebook. But you can also have IDEs, like VS code or pie charm, or whatever you use for programming.

5:06And that was something that I was familiar with because I was doing programming before I got into machine learning. And then as a result, was called a data scientist, because I do everything with data. So a researcher can use a combination of these tools. And business intelligence might use things like power BI or Tableau. Right. So there's not programming going on here. They create visualizations and they make presentations to stakeholders. So really, we have a wide array of paths for what is a data scientist. And in closing, the reason I'm bringing up these distinctions is first, I want you to feel comfortable with your path. Number two, I want you to understand that you can actually jump around and augment your other skills that you may not have. It's rare for somebody to have all of these different paths under their belt. And the data X certification is a pretty thorough and pretty wide array of domains, as I showed you before. So it's going to be common for you not to have all of those under your belt. And as a result, I've created these courses that you can use for deep dives. Okay. And now what we're going to do in the next video is talk about what is data science and talk about the domains and subdomains that we're going

6:23to be working with. See you there.

0:00Welcome back. In this video, we're going to explore what is data science because it's a collection of different domains. For example, if I said AI, what would that mean? If I say data science, what does that mean? Let's dive into this and break it down so we know what domains we're dealing with in the realm of data science. And then we'll segue into the data X domains . So let's start with the two largest domains. If I draw one circle here, and then another one like this here, I could say that this is data science. And we can just say that this is AI or artificial intelligence. And inside of this, what is the overlap between AI and data science? And again, AI is a large umbrella term for many subdomains. And so is data science. But the overlapping ones that we're going to talk about in the context of AI is going to be machine learning, and then deep learning. So deep learning is the newer version of machine learning. And that's just a really simple way to hang it in your mind, right? These are just simple terms without jargon that we're going to this is this one came first and this one came next. That's another way to put it. But when we think of AI, we can think of it as expert systems, knowledge graphs, advanced search, things like this. So it's a

1:19larger umbrella that also includes machine learning and deep learning. So what is machine learning? So machine learning are is a domain that includes algorithms that learn patterns from data. And so what is deep learning? So these are multi layered neural networks for automated feature extraction and other cool things. So the takeaway between machine learning and deep learning is that we're using algorithms with machine learning. And with deep learning, we're using neural networks. So what I mean by that, neural networks or deep learning is software that's modeled after the brain, how neurons work in the mind. They have these sort of activation thresholds and they'll fire and move to the next or neuron, or they won't. And that's going to be explained in much more detail when we get into machine learning and deep learning. But the main distinction is algorithms and neural networks. Now what I'd like to do is dive deeper into machine learning and deep learning. And then we 're going to dive a little bit deeper into data science, because there's a bunch of sub domains that we have not discussed for data science. But first, let me just say that data science is end to end data life cycle. So this is everything from collection, cleaning, analyzing, modeling,

2:37deployment, and even communication. So there's a lot to talk about. So when we're talking about machine learning, we said that we're that we're using algorithms. So I'm just going to say algos are short. And we're learning patterns from data. And so some of the tools and techniques. So we can use side kit learn, if you have a background on machine learning, you probably heard this before, there's xg boost, spark, and others. And again, this is a subset of AI. And we 're really emphasizing on predictive modeling from labeled and unlabeled data. We're going to dive into these two terms much more soon. But this is the, I would say the overview. And we're talking about deep learning, we're using neural networks and N for short. And I would say that this is a or deep learning is a subset of machine learning. And some of the tools and frameworks and techniques . So we've talked about TensorFlow, there's PyTorch, there's Keras, and things like the transformer architecture. So let's talk about that. So some of the relationships might include computer vision or CV, natural language processing or NLP and pattern recognition. And when we said CNN, these

3:55are convolutional neural networks and RNN recurrent neural networks. And when we said transform ers, that's an architecture that's widely used with large language models or LLMs. Again, we're going to get into all of this in the course and define all of this. But remember that you have that resource. If you need to deep dive into machine learning, there's a great skill or course introduction to machine learning that starts from the beginning. And then if you want to get into deep learning, you take that other one.

4:26And you can cherry pick, right? Using the Feynman technique, find out where you have gaps and then really just focus on those. But what I would say for both of these introduction to machine learning and deep learning, check out the very first videos because it sets you up with a development environment and kind of like lets you know what's going to happen through the rest of this course. And then you can cherry pick what you need to fill in your knowledge gaps. Okay , so we've knocked out AI and machine learning and deep learning. Now let's talk about data science. But I think we should do that in another video. So let's do that next.

0:00All right, so now we're going to talk about data science. This is a much bigger one. And again, this is a subset or an umbrella. Let me say that, which includes part of AI. And by that, we're talking about machine learning and deep learning. OK, so this is a very broad umbrella term. And so the tools include pandas. We've talked about that briefly. SQL for working with data, Spark, Jupiter, which we talked about that. That's also used in machine learning and deep learning as well.

0:30There's visualization. And we use things like matplotlib and seaborn for visualization. And this also includes MLOps tools. Talk about that as well. That's part of the data X certification. OK, so now we've talked about some of these tools and techniques. But what's the primary focus? So again, end to end data lifecycle. So let me put that here. End to end data lifecycle. All right, so what is the end to end data lifecycle? We talked about that briefly and we're going to get into it together.

1:02Let's just go ahead and do that now in this section. So this is the lifecycle part. So one is going to be data collection. And that also includes warehousing. Two would be-- and this isn't that in any particular order. It's a loose order because data governance, which is the next one, it can be implemented in many different places. But the reason I'm including it here is that it's part of, after you collect the data, you clean the data. And so there's some data governance that goes with cleaning the data.

1:35And number three, we get into EDA. So once you've collected the data and then you've cleaned it and making sure that you're following the rules and regulations because there's a lot to it, you can't just take data from any country and process it anywhere. And also, you need to make sure that you're protecting information and using security, all these things. And so EDA is exploratory data analysis. So this is a term that you may have heard before. And what it is, is just really the initial step.

2:05When you've collected it, you've cleaned the data. What do I mean by clean the data? Let's say there's a bunch of stuff that you don't need, maybe some garbage columns or something like that, or some missing data that you need to handle as part of cleaning. Once you've got the data sort of prepared, you do some exploratory data analysis, usually visualization. You check out the data and you're like, okay, this is what we're working with. And we're trying to answer this question or solve this problem. This kind of gets you started. And then we'll get into things like feature engineering.

2:38Maybe you want to create or combine a few columns to create a new column of information, but it goes a lot deeper than that. And then number five, we get into things like modeling. And you would build a model and then you would use validation, measure the performance of that model, make sure that it's performing. There's a lot to this, but I'm going to say that you want to have a certain accuracy and make sure that the model is not, I'm going to say, and this is including data, machine learning and deep learning, right?

3:09When you're building these models, they can be statistical models too. So modeling is a larger term, but you want to make sure that you have an accuracy that you're working with and that you're not including bias or that you're not overfitting. So these are terms that we're going to go into a little bit later, but this is the part of the life cycle for right now. And so modeling and validation on to say, VAL. And then number six, this is where you would do the deployment, deployment and monitoring. And so this monitoring part is what we call MLOps, and we'll dive more into that as well.

3:44Now what we need to do is dive back into machine learning and break it down a little bit more because there's some more granularity that we didn't talk about. We just talked about it really just in a bird's eye view. So let's dive a little deeper. So now diving into machine learning, number one, we have supervised learning. And this is something that we're going to talk about in depth, but you can also look at this in the introduction to deep learning. And when you're talking about this, this is normally things like classification , regression, and then we have unsupervised learning.

4:19And here we're talking about clustering and something called dimensionality reduction. You've heard of, maybe you've heard of PCA, principal component analysis, as part of that, and also association rules. And number three, we have something called reinforcement learning. And here what we're doing is that we have rewards and penalties in some kind of environment. So let's say that we have like a maze just to make sure that this is super clear. And this is the goal and this is sort of a dead end.

4:51And as the agent goes through this environment, it'll get rewards or penalties based on the decisions that it makes in a nutshell, reinforcement learning. And the introduction to machine learning, I go through this kind of an example using a lot of different use cases. And I call it grand search auto because it's really a search problem. And then we have number four, semi-supervised learning. And here what we're talking about is we might have a small labeled data set and maybe a large unlabeled data set.

5:28And let's say number five, you want to create a model to create labels to predict or to predict tokens. That would be self supervised. And again, this one number five, that's where we create models that create labels or we want to create generative toking or generative models that create tokens. And that would be things like GPT. And then number six, we have multimodal ML. So multimodal machine learning is combining text, images, audio and sensor data .

6:01So computer vision, natural language processing, all of that good stuff. So this is all under machine learning. And the subset of that is deep learning. So all of this is sort of interrelated. All right, so that's a lot of information. And what we're doing here, don't worry, you don't have to memorize this. This is just really breaking down the domain. So we understand what's inside of these larger umbrella terms. The largest one being, or the largest two is AI and data science. And in the next video, I'm going to show you some code examples and some outputs and some visualizations.

6:36So this starts to make a little bit more sense because right now, this is all really abstract knowledge, right? And I'm telling you that you don't have to memorize it. But when you see these images, you're going to start to get the picture. So I'm really assuming that, you know, I'm talking to you like you don't know. But if you know, then, you know, please excuse me and just move on. But I'm trying to make this accessible because as we discussed earlier, there are so many different paths for data science. I don't know what you know, what you don't know. So just take that with a grain of salt and keep that in mind that I'm really trying to make this accessible.

7:11And once we move out of this first skill and the next skill, we're going to be cooking pretty fast. I'm going to make sure to use every not to use any jargon to make things really accessible. But we're really just going to dive into the data extertification course. What I'm doing here is I'm covering some of the domains, some of the information you're going to need to know that's going to be on that test right now. But I'm also doing it in a way that is accessible. All right, see you in the next video.

0:00In this section, we're going to explore a few of the major data science application areas you'll need to master for the data X certification. Each section starts with an intro and ties it back to real-world problem solving followed by a short code example to really make sure that this is tangible and concrete, right? We're moving away from these abstract ideas. And what I've done is let me move over here to Google Colab. So now you know about Google Colab. If you have a Gmail account, great. If not, get one set up.

0:32And once you do, all you have to type is colab.research.google.com. And I've included each one of these code snippets below this video. So you can just copy and paste them into this notebook and then play with the code. But you don't need to even do that because I'm going to show you what they are. Or actually, I'm going to explain exactly what's happening and how this relates to the different subdomains that we just discussed. So the first one here, as you can see, we're making some using simple temperature forecasts. So we're using some historical data over this like time series data.

1:06So this prediction is a task that usually revolves around forecasting future values based on historical data. In this case, we're using temperature. And in this case, it's pretty straightforward because what we're doing is predicting temperature. Or it could be as complicated as a demanding forecast in, let's say, a supply chain management. That can be really complex. Even if you're working with advanced neural networks in production, the fundamentals of setting up a supervised learning task is probably going to be about the same.

1:38You select some features, you train a model, you evaluate it, and then you iter ate. So what we're doing here is you can see this green part is the prediction. And the reason that this is a very simple, basically, this is just a linear regression model that's very, very simple using synthetic data. And what we're doing is that we're measuring the distance from all of these points. And it has this line that's the prediction line. And then here is going to predict that this is where the temperature is going as the days progress.

2:11So this is a prediction example. And again, this is simple linear regression, but you might also use a RIMA profit or long, short term memory when you're working with models in production . All right, so now let's move on to pattern mining. So here, pattern mining is going to focus on discovering non-trivial relationships and frequent patterns and large data sets. So typically, this might involve some kind of market basket analysis, anomaly detection, or sequential pattern discovery.

2:45And this is particularly useful for recommendation engines or identifying relationships among seemingly unrelated data points. Right? So let's say that you're trying to find items that are purchased together. Right? And so here, it's going to show you that these two milk and bread are purchased together more often than these. And this is giving you a basically a number between zero and one. And I don't want to go too deep into this, but this is an example of pattern mining. The next one is segmentation.

3:17Segmentation typically falls under unsupervised learning because you aim to group similar observations together. And common examples include customer segmentation, clustering of documents. That's NLP and the customer segmentation that would be marketing or even partitioning images based on color profiles. And that would be for computer vision. So really, the takeaway here is that the goal is to expose hidden structures in the data.

3:48So here, we're not telling it what this is or what this is. It's just saying that it's finding these two clusters by using K-means segment ation. We'll talk about K-means clustering and segmentation at a later date. But this is just an example of segmentation. Alright, so now moving into natural language processing or NLP, we have an example that handles textual or linguistic data. So this could be everything from sentiment analysis, like let's say movie reviews, you know, or right here.

4:18I love data science. Data science is awesome. I hate spam emails. So we can say that this is going to be positive or negative. And so you can see here we have made a comment there. So these two are going to be positive. These two are negative and this one is positive. And as you can see, that's pretty straightforward. It also includes things like named entity recognition. And this is an even large language models like chat GPT. They all use natural language processing.

4:49The core tasks revolve around extracting meaning from text. So I like to say teaching computers how to read. And then this could be unstructured or semi structured data. And we need to understand things like tokenizing and beddings and model selections and also things like naive base and transformer architectures. These are all essential. So here we're using a naive base model with scikit learn and you can see that we imported scikit learn right here as SK learn and here's naive base.

5:22Alright, so now let's look at network analysis. Network analysis explores how entities that could be people nodes or systems how they're interconnected. This is crucial for things like social media analytics fraud detection. In that case, it would be transaction graphs or biological networks. Those would be things like protein interactions key concepts include graph theory, centrality measures and community. So when we're talking about centrality, we're talking about distributions.

5:54And this is a pretty simple graph because most of the real world networks are large and complex. But here we have a pretty similar simple graph illustrating some sort of hierarchy. Right. So we have child a and or we have root and then child a and child B and then we have a grandchild of called B one. And when you have more than one, you'd have grandchild a one and grandchild a two. So this is just a tree like graph. And now moving on to signal processing. This includes things like images, audio, sensor readings, etc.

6:29This is modern computer vision and we leverage things like deep learning. And that includes convolutional neural networks or things like transformers. Right. There's even vision transformers. So there's a lot of code here, but this is an example of some classification. So you feed it this image and it classifies what it is. So here we have a sandal. This is a sneaker trousers pull over and so forth. And this would be an object detection task.

6:59And it could also include image segmentation in contrast to modern computer vision, which is what we're talking about classic signal processing relies on techniques like four year transforms or wavelet analysis for pattern detection in time series or acoustic data. So audio and I can give you a better example of this. I have this is in one of the, let me see where it is right here in the introduction to deep learning. I believe we get into this emnis fashion data set.

7:30And here you can't really see it in this image, but this is actually showing you the pixels and you can kind of see a shape and that shape is this shoe. And so all of these would be zeros all around it. And if I were to extract this image or make this wider, you would see that. But what we're doing here is that we have this 28 by 28 tensor that are converted into numbers and we're using that and we have these classes here. So it's labeled data and then we can make predictions.

8:00And so in this case, we're looking at these and here is the code where we go through a bunch of training epochs and here is the validation accuracy and the validation loss. So there's two different accuracies. So you can think of this as training data and then data it's never seen. So this is what it uses to learn and this is what we, how we test it because this is data it's never seen. And this is just a good example of a multi class classifier, meaning that it's not just one image. So is it a sneaker or is it not a sneaker? So that would be binary multi classes when you're dealing with several multiple classes.

8:39In this case, each type of fashion or garments in this case, which also includes shoes and sandals. Alright, so that's it for this video. And the next section is going to be a short challenge. And what that is, something that you're going to get used to at the end of every scale we have this sort of challenge section. And it's going to ask you questions and based on what you saw or learned in the skill, you should be able to answer those. And if not, you can just go ahead and review. So it's just going to be a quick review, everything that we just, these examples of data science in application that we discussed.

9:13So I'm going to show you images and then you're going to answer what type of application is that in data science. Alright, see you there.