Explain the Modern Data Analyst Role

0:00Hello and welcome back to the next skill in the CompTIA Data Plus prep course. In the previous skill, we built the roadmap for the exam, how it's structured, how it's scored, and how to study smart using science-backed techniques. In skill two, we'll zoom in on the person this certificate is really built around, the modern data analyst. So what does that really mean in 2026? What about AI? We've got a lot to unpack in this skill where we explain the modern data analytics roles. We're living in what many call the golden age of analytics. Organizations are literally swimming in data, storage is cheap, and competing power is everywhere. And it's also rentable in the cloud, which means leadership knows there's real money hiding inside of those numbers. But raw tables don't create decisions. Raw data by itself does not have value. And that's precisely where the translation layer comes in, turning messy data into usable insight. That's the analyst's job.

1:07So you might be asking, well, why does this matter for the CompTIA Data Plus prep? Here's why. Data Plus' five domains aren't random exam buckets. And what makes this really easy to approach educationally is that all those domains are more or less the steps for data analysis. And that's what we're going to leverage in this skill, because these five domains are basically a job description broken into parts. So now going back to this raw data that needs to be converted. First, you need a development environment. And that's the first domain, understand data environments. Step two is where you acquire and prepare data. So now we have step one, which is a development environment.

1:54Step two, we have the raw data, and now we're going to prepare it. And step three, which is analyze it. And you could also do some quick EDA here as well. Now you're starting to turn that raw data into some kind of value. Then number four, you're going to visualize this. This is super important, because not everybody understands Python, but they sure do understand visualization. And that's why it's a universal language, if done right. And then finally, we need to lock this down with governance, which is now an ever growing field, thanks to AI. And now we have something called AI governance. It's not just data governance. Now it's AI governance. These are intertwined because in AI governance, you need to understand how that data is being used to train. So data governance and AI governance overlap. Super interesting. So what does this all mean? By the end of this skill, you'll be able to explain what a modern data analyst does end to end, why that role is growing super fast, even with AI, and how data plus domains map directly to real analyst work.

3:07How does that translate into what we're going to actually cover? Let's break it down. In this skill, we're building a clear picture of modern data analysts from two angles. First, why the role exists at this scale right now. And this is really about the golden age of analytics, forcing analytics to the central part of a business. And then B, we'll explore what analysts actually do day by day. This is the actual workflow, which includes the techniques and the tools. We'll walk through the temple mental model. This is a visualization of analytics on top for three pillars, which includes data storage and compute. And here in the roof, we can call this analytics. And here we have governance. Then we'll walk the analytics process end to end and connected to reporting and dashboards. And finally, we'll introduce the techniques such as descriptive, inferential, predictive, and prescriptive. And this is also where EDA fits into the picture. Finally, we'll connect modern AI flavored work. So that is the AI

4:23machine learning and deep learning, which includes generative AI, NLP, which stands for natural language processing. And we'll also cover RPA, which is not AI, but it is part of automation and people get them confused all the time. So we're going to make sure that these are clearly delineated and that we understand that they're different for this skill. The second skill, we're mostly focusing on domain 1.0 data concepts and environments, 3.0 data analysis with a little bit of overlap into the second, fourth, and fifth domains. But generally we're working with domain one and three. All right. So that's it for this video. And next up, we have the golden age of analytics and why analytics are in demand. See you there.

0:00Welcome back. So what is the golden age of analytics? The core idea here is simple. Organizations now treat data as a strategic asset and not just exhaust from operations, which was the case for quite some time. As a strategic asset, these organizations are also investing in dashboards and analytics programs and data teams because leaders expect answers like where should we cut costs or which customers are likely to churn, but most importantly, they need so with evidence. And that's why modern analysts are in high demand. So companies start with this raw material or that raw data, but they need people who can translate it into decisions.

0:48So you have that raw data that is now becoming valuable because leaders want to make evidence based decisions. And to do that, they need people to pull the right fields, clean the messy data, do the analysis, build visuals, and explain the story all in plain language. For data plus this context matters because CompTIA questions are usually framed like workplace decisions. That means given this situation, what would a competent analyst do? And if you understand the role, the exam stops feeling like random trivia and starts feeling like job scenarios. And now we can talk about the analytics temple. I guess we could say that this is a Greek style temple.

1:35And you can see that the roof here is going to be analytics and it's being supported by three pillars. We have data storage and compute or computing power, but no building is complete without a strong foundation. And that means that the whole thing sits on a foundational slab labeled governance. So this picture explains precisely why analytics is exploding right now instead of decades ago. And what's super interesting about this is that right before AI, that's when everyone is really rushing to use all of the data resources. And that's why data analytics is also very in demand. Before that it was in demand, but for larger companies. Now everybody's jumping on the bandwagon because data is the new gold. And since AI is here, well, what does AI run on? Data. Data has exploded. Transactions, customer interactions, sensor logs, web events, photos, videos, audio, and storage used to be painfully expensive. Now it's cheap enough to keep far more historical data for far longer. And that means that analysts can now spot trends and long tail behavior, but also computing power has skyrocketed and also cloud, which is a

2:58new focus of the CompTIA Data Plus. And that means that analytics aren't just limited to supercomputers. You can rent serious horsepower on demand through the cloud. And in fact, that's what we're doing with Google Colab. I have access to tons of GPUs through Google Colab. I do not have supercomputers here at my house. I pretty much rent all of my compute through the cloud. I do have machines locally for learning and building local models and things like that, but the heavy horsepower, I rely on the cloud. And don't forget this foundation of governance. That's what keeps all that power from becoming chaos because we have access control, privacy, quality standards, and rules that are responsible for use. Now let's do a real analyst workflow on a churn scenario.

3:51If we decide to zoom into the day-to-day, the data analytics process has five stages. And what I was super excited about is that, well, these five stages are also the five stages of data analytics at your job. So that end-to-end analytics process are mirrored in the five domains of Data Plus, which is convenient and probably by design. All right. So we start with acquisition, which is domain two, because we don't need to focus on domain one, which is our development environment. Acquisition means pulling from internal and external sources, but also consider reality, cleaning and manipulation. Then reality hits because you also have to contend with cleaning and data manipulation. You have missing values, invalid codes, duplicates, inconsistent formats, and all of this could consume a huge share of project time. And this is where the analyst earns their money, really, because you can make data usable and trustworthy. And that's why cleaning and manipulation takes so much time. And sure, because it offers the most value. And again, I could just say that this is where the analyst really earns their money because they're

5:07making the data usable and trustworthy. Then we move into analyzing. And this is often where we start with EDA or exploratory data analysis. You can run simple summaries and visuals to help you better understand distributions, maybe spot some outliers or form a general hypothesis. Then we move into visualization and reporting and communication because we have to turn those results into something humans can act on. And crucially, the process is iterative. That means that once we're done here, we'll go all the way back if needed to the first step, which means getting new data and then cleaning it, analyzing it, visualizing it, and then communicating those results via a report or dashboard, some form of communication. All right, so enough theory.

6:01In the next video, we're going to dive into Google Colab and try this out. We're going to perform an end-to-end analytics process, much like you would find at a job. See you there.

0:00Welcome back. In this video, we're going to talk about Google Colab, which is exactly what you see here. The one that you're going to open looks like this. It has a bunch of information or code here, but I do want to explain what we're doing so that this is not confusing. All this allows us to do is to use Google in the cloud and we have Python loaded here. And if I go to runtime and then I go to change runtime type, I could choose different GPUs. I have a CPU here, but I can also use a T4 or V5E-1 or any of these like H100, but you have to pay for these.

0:36They do let you use these for free, which is pretty cool. And you can also use R and Julia, not just Python. So in short, this is probably the easiest way to get Python up and running on your machine. So I can prove it to you. I can just say print. This is what programmers would use when they're testing out a development environment to make sure that they have Python loaded. And it takes a second to connect to the cloud, but there it is. So Python's version. But to do this, we're going to need the exclamation mark so we can talk to the backend.

1:07And that lets us know that this is Python 3.12.12. But don't worry. If you don't know Python, you definitely are going to learn some Python because I'm going to make it really intuitive. But more importantly, I'm going to show you that you don't have to use Google Colab. You can use whatever tool that you're more familiar with. For example, let's say you work with spreadsheets and that's easier for you. Let me show you how you can use those. All right. So now that you have Google Colab, all you really need is a Google account. You can see that I'm signed in here in the top right.

1:38And once you're logged in, all you have to do is click on this Google Docs link and it'll open up and you should be able to see everything that I see and you should be able to run everything that I run. However, you will not be able to make changes because this is not yours. But if you want to make a copy of this, all you do is go to file, save a copy and drive and then it'll copy this notebook for you. I accidentally clicked on text, which I'm glad I did. But if you do that, you can click on delete here. And if you want to run these code cells, you can do that with this play button or you can use shift enter.

2:14But if you hover right here in this center area, you'll get either code so that you could write code or text so you could write text. I'm going to delete these two and I'm going to show you how to run this first cell. So shift enter. And this is talking to the cloud, so it's going to run and then you'll see this check mark. It completed in zero seconds. It was very, very fast. And before I show you how you can use this in a spreadsheet, we have to load these sources because in real work, you almost never start with a perfect table.

2:44I wish you start with files from different systems. So here we're going to use our CRM and our demo file. So this CRM right here is an internal CRM that has churn and some interesting behavior. And we can call this the internal truth. And then here we have a kind of a demo. I call this demo and this is just enrichment. This is external data, but there is data mismatch and duplicates and missing data because I want it to be a little bit realistic.

3:16So let's click on this folder here and you're going to see that if I click on refresh, you just have sample data and all of this. Not really a big deal. But once I run this, we're loading the data. So this is the very first part. So now that you've loaded this data and you click on refresh, now you have CRM customers CSV, which is a comma separated value. And now you have demographics dot CSV. That's the demo, which is the enrichment data, which is dot CSV as well. So you can just click on these and then download these.

3:47So once you've downloaded these, you can go to Excel or even Google Sheets, go to file and then import. And once you do that, then you would go to upload here and then browse or drag a file. And then once you uploaded that, now you would have that same CSV for the CRM, which is, I closed this one. We don't need that, which is here. OK, so this is the CRM data. So you don't have to use Google Colab. You can use spreadsheets. So if this is more familiar for you, you can see that we have customer ID from 1001 to 1009.

4:21If we go back to this data here, you can see that we have 1001 to 1010. And we're going to explain that in just a second. Why do we have 1010, but there's no 1010 here? This is going to come up and this is part of the lesson. OK. So now that you have these two data sets loaded into a table, you can convert to a table like this. And I'm going to close this. And now you can do things in this table. So I can do math if you have the correct data types. And again, that's part of cleaning. So let's go back over here and get started.

4:53All right. So all I've done is just imported pandas, which is a digital version or let's say a Python version. Right. It's a Python package for tables and data manipulation. And then we have NumPy, which because pandas is built on top of NumPy. And then you have matplotlib for visualizations. Normally, Colab has these installed, but I'd like to show this as a best practice in case you're using non-Colab like Jupyter Notebook, which is a local version of what you see here or a non-cloud version.

5:24All right. So don't worry about all of this stuff. We're just loading the data. That's all we're really doing. And I'm giving you some options so that you can use spreadsheets or any other tool by downloading the CSV and performing those actions. Because this is a vendor neutral certification, which means, well, if you understand the process on multiple tools, that's all you really need to do. So you can follow along with me and it won't be too difficult to do that. We're not writing any code. You can use Google Colab or you can use the spreadsheet.

5:57I'll leave it up to you. All right. So you've loaded this data or you have it in a spreadsheet. So now I'm going to return the first five rows with this code right here. And now you can see that we have 1001 to 1005. But let's say you want to look at the last five, you can do something called tail. So the last five here. And you can see that we have 1010 to 1004. Now let's do the same with the CRM tail. OK. And you see this one goes to customer ID 1009.

6:28It doesn't go to 1010. So that is just a little foreshadowing of something that we'll need to address together. All right. This is pretty much what data acquisition actually feels like. So where are we in this process? We basically are in the first stage, which is acquisition. And this is before you clean anything. You can confirm what you actually have. And that's what we've done here with head and tail. I could also just do CRM like this and get the first 10. And in this case, it's trying to show me as much as it can.

6:59It will truncate it if this is a big data set. And so the first thing that I would do is to inspect the columns and the row counts. So the columns is going to tell you what fields exist. So here are the columns. So you have customer ID, plan, tier, churn, monthly, and so on. And if you see some missing values, then you'll know what kind of values are missing. Row count tells you if you're about to lose data during a join. So sometimes you can perform a join where you take two tables and combine them.

7:31And based on that join, sometimes you can lose rows. And looking at the row count is a good way to make sure that you're staying one step ahead of that, meaning you're not losing any rows during a join. All right. So this is the habit. You want to always check the columns, number of rows, and use a tail or head or just look at the data like this. And you can also do so here in a spreadsheet. All right. Now let's move down over here. So we've already acquired and loaded that.

8:03So let's, well, we did that here inside of Colab, but we need to load those here. And the way that you would do that, let's say that we didn't use pandas, but instead we have them here, right? And if that's the case, you can upload CSVs to Google Colab. And if you run this, then this is the load and inspect. And then here, it's going to tell me what the column names are and the row counts for each data set. So I have 10 and then eight, oh, oh, warning. Let's make sure that we keep this in mind.

8:35And whenever we perform joins, we want to make sure that we're not losing rows. And now here's where we make our money. This is the stage where analysts actually earn their paycheck and we're making the data set joinable and trustworthy. So what does that mean? So first, let's go ahead and deal with this one right here. So when you're cleaning and manipulating, that includes fixing things like white space, which are hard to see, and also data type mismatch. So before we do this, let me just show you what this is here.

9:05Let's inspect this a little bit more. Let's say crm.info. OK, so what we want to do is make sure that the CRM customer ID and the demo or demographics customer ID are both going to be strings, because right now it's an integer. So we want to change that to string. And we also want to fix the white space, remove the white space, because when you're doing a join, it's very strict. If you have one extra space, there's going to be no match, meaning if you had one extra space, we would get a bunch of unknown regions and the join would fail.

9:38So that's why we're doing this. So let's go back to here. Now we're going to see that they're integers and we're going to run. Let's go ahead and just run this here so that you can see the changes. So now I'm going to run this cell again so that I can see what's changed. And now I'm going to be paying attention to customer underscore ID here. And now you can see object and object is a string. That's just what Panda calls a string. So just have to memorize that if you're working with Pandas. And if you're working with a spreadsheet, it's going to be slightly different.

10:10But what we're doing is just changing it from an integer or a number into text or a string, which Pandas calls an object. And if I change this to demo, we would see that we also have now the customer ID as an object. OK, so I'm just going to delete this because we're going to do this down here again. All right. So next you want to drop duplicates. What you're doing is you're collapsing multiple records to one per customer ID. And here's something important to consider. In real work, you don't blindly keep the first row right here.

10:43And I put this here because we're just doing that because this is a toy demo. You would decide that based on the data that you're working with, maybe the latest update or the most complete row, et cetera. In this case, it's a toy data set. So we're just going to keep the first row like this. So that's something to consider. And then here we're going to merge before we do merge the duplicates. The reason that we're doing this, too, is that duplicates can inflate counts and distort rates. So this is important. Now we're going to do a merge, which is a join. So here we're going to emphasize the left join.

11:17I put that here in the comments, but you can see here how is a left join. So our CRM is the analysis population and demo is the optional enrichment, the demographic. And one thing to notice here is that we're not using demo as the base. And as a result, if we go back up here, you can see that we have 10, 10 here and demo. And since we're not using 10, 10 or demo as the base, that means that it's going to disappear here when we perform that join. Let's go over here. OK, so that explains that join there.

11:48And then finally, we have simple missing handling. And for that, we're just going to handle missing values with fill and a unknown. And that's how we do that right here. We're just going to handle the missing values. And when we see a known, we're not saying that the region is truly unknown. What we're saying is that we're labeling missing data so that we can track it. That's the whole idea here. All right. So now I'm going to grab this little line here and remove it. And then I'm going to run this cell and paste this here.

12:19And this is where you check everything, right? We can do this one by one as well. So here we have the rows after dupe. Let's do this one here. OK, so nine. That's good. And then finally, we're going to check for the percentage of missing demographics. And here we have 33.3 missing. And then finally, we can do this, which is merged head. And now you can see that you have both of those tables merged. So what's important to consider here is that every analyst should run sanity checks after a merge.

12:49And that's what these were right here. So you want to check your work, right? If you do a join in these spreadsheets, you want to do the same thing because this is vendor neutral. We just need to do the sanity checks after you do the join. And now we can finally analyze. But we start simple. We're going to use descriptive statistics first and a little bit of EDA, but not too much. So we want to analyze the churn rate by plant tier. And we're going to say churn by tier.

13:20And that's how we get churn rate by plant tier. And then here's a very quick EDA. And what we're doing here is just asking questions. Do the ranges look same? Do we see weird minimums or maximums, right? Or is the data skewed in some particular way? So again, here for the EDA, we're just asking questions, not making predictions about churn just yet. You're just identifying patterns that are worth investigating. So let's go ahead and run this. So we have basic and enterprise at 50% churn, and then we have pro at 33.

13:55Here's some very simple statistics. I think this is dot describe. Yep. This is how we do that. And here we can create a simple visualization, which is, in essence, the same visualization or the same insight, I should say, as the table, but faster for humans to scan because it's visualization and not all this code. So let's go ahead and run this. All right. Let's check this out. So here it's the same information, right? 50%, 50%, 33%. But the cool part here with this, bar charts are great for category comparisons.

14:28Look at that. You can see that and you can show this to non-technical audience and you can add labels and titles, whatever you need for interoperability. Okay. So here, let's do a quick distribution check on last login days. So what we're doing here is pretty classic EDA. How is the engagement distributed? All right. So histograms will show you a shape like, and that means like a cluster or let me just show you. All right. So if you had a normal distribution, you would see something that goes up like this, right?

15:02Let me change the pen color. A normal distribution. So again, here's a different color. A normal distribution might be clumped up like this in the middle, but you can see that this distribution is skewed like this and this is called a right skew. So now we're looking at the distribution and again, we're just looking at the shape and this supports hypothesis building. You can say our most inactive customers churning and other questions. And you can think of this as a investigation, like a quick investigations.

15:33That's sort of like what I like to think of EDA. And if we go down here, finally we have some kind of communication, right? So we're not going to get too deep into this, but if we run this, here's actionable takeaway. The highest churn is in the basic tier. Well, it is, but it is also in the highest, in the, where is it? Let's go here. It's also an enterprise tier. They're both 50% and next step segment that tier by inactivity and then some kind of next steps. All right. So this is pretty cool.

16:04We acquired and loaded that CRM CSV, it was multiple steps, right? And then we clean and manipulate. We fix IDs dedup, meaning deduplicated. We merged and we label missingness as unknown. Here we're going to clean and manipulate like this, what we just talked about. And then finally analyze. We compute churn rate and run some kind of a quick EDA. And then over here, we're going to check out the distribution. And this is also just the histogram. That's what it's called.

16:34And then some kind of communication at the end. This is just to show you how this works. And since I didn't go into it very detailed, let's do that in the next video where we talk about communication and insights. And to do that, we'll look at tables and visuals and reports and dashboards. See you there.

0:00A big part of being a modern data analyst is communication or the skill of communicating insights. You could say that, okay, we're choosing tables, visuals, reports, and dashboards and call it a day. But when we're looking at tables, we know that they are precise, but they're hard for humans to scan for patterns. They're precise, but there is a higher cognitive effort, meaning that a chart often makes the same point almost effortlessly and instantly. You can spot trends, outliers, and comparisons.

0:34Then we package visuals into deliverables. A report is a point in time snapshot because these charts and maps not only give those patterns visible at a glance, but a lower cognitive load. So a report is visuals plus narrative that explains what happened and why it matters. A dashboard, on the other hand, is for ongoing monitoring, meaning visuals that are updated regularly and support filtering and drill down. And be prepared for a scenario where you're given an audience and a goal and which deliverables and visualization choices might be best.

1:13But we really want to look at these deliverables and examine reports and dashboards with a little more detail. So now let's go back to Colab and try this out. Just remember, this is about communication. Analysts don't just compute answers, they package answers. That's a good way to think of it. Now in this Colab notebook, we're going to take the same churn insight and deliver it as a table, a chart, a short report, and a dashboard like view. Again, we're not going to be able to go into super detail here because we're just doing an overview of how all of these work together in the role of a modern data analyst.

1:53All right. So we've run our imports and now we need to load or recreate the merged data set because we already merged it. And so this should be pretty familiar. So I think that we can go over here to this folder. You'll see that there are no files here. So if you need those files again, you're going to need to go over here and download them from this place because I made them downloadable here. I turned them into CSVs just for this exercise. So definitely go to demo one. You should still have these downloaded if that's what you're doing.

2:23I'll close this and now we can see our table and the way that we're doing this is DF head. So let's go through this together. And a quick note, this is domain four style of thinking. You're choosing the best deliverable for the audience and goal. And again, we have two data sets, two different worlds. We have the CRM, which is the internal system. And then we have the demographics, which is this third party external enrichment. This could be region, age, income, and so on. And all of this mirrors a real business workflow.

2:57You want to join business behavior with some kind of context. And as you see here, we're also taking care of the white space because otherwise if the customer ID is numeric, like an integer, the join could fail silently because the keys don't match. And that's another important reason why we're doing that. Here we're dropping duplicates and here we're doing that join. And we're using fill NA for those missing records. And we're saying that we don't know what those missing values are. We're not saying anything specifically like unknown age.

3:30We don't know what age it is. It's just more like cracking the missing value, right? Okay. So now we're ready for that merge. Okay. So here's our table. So we have a pivot table. And what we want to do here is keep all the CRM customers and pull in the demographics information when available. And that's why we're also going to do, let's go back up here. I believe the join is here. There's the join, a left join. This means that the left is the CRM, which is the truth set, meaning the customers we have churn labels for.

4:01Demographics is the optional data, the enrichment. Okay. So let's go ahead and run this first table here. We could also say this is the table version, meaning that we have precise compact and we're aggregating raw rows into decision ready metrics. All right. So looking at this first pivot, this looks pretty familiar. And here we're also sorting the values and this makes the story, I guess, more readable. You're sorting and renaming. And this is something you could consider this reporting polish, right?

4:33To make it look good. And what we're trying to convey here is which tier churns the most. So let's do the second one here. And here we want to shape it like a dashboard matrix. Okay. This is more of a report place in time. So let's go ahead and do this one here. It's categories on the side, and then you have these segments across the top. And so here the index is now the plan tier and the columns are the different regions. Okay. And this creates a grid that's going to be visually easier to support comparisons. And that's why we're doing this.

5:04And you could also say this is where the raw data becomes a monitoring view. And if you're working in a spreadsheet, well, pivot tables are the backbone of Excel and dashboards and BI summary. So this should be very easy and other tools as well. Okay. Now going down to this pivot chart here. And like we said before, these tables are exact, but charts are fast for human brains. So let's go ahead and run this. All right. So it's really easy to see that we have the highest return rates are both basic and enterprise, nothing new.

5:35And now I want to compare a report versus a dashboard. This is really important. And I'm going to leave these here for you. A report is a point in time and a dashboard is ongoing. That's the takeaway. So when we're talking about a report, we really mean narrative bullets and some kind of chart or table or a snapshot. So you could say that churn is concentrated around the less engaged customers, but you actually haven't proven it in this notebook. So it really depends.

6:06This is toy data, so we can't take it too far. That would be more of a hypothesis that you'd need to validate with some kind of a quick check, like a correlation or grouping by last login days or something like that. So what we're doing here is labeling what's confirmed or comparing that to what's suspected, right? So here we're labeling what's confirmed versus what we suspect. Now let's take a look at the dashboard. Well, let me run this cell here. OK, so here's a dashboard. It's saying here that the highest churn rate is basic, but this is just a demo, right?

6:40You can see that they're tied. So we get the highest churn and churn appears concentrated among less engaged customers, right? And the next step might be target retention offers for high risk customers in the top churn tier. OK, cool. Now let's look at this dashboard. A dashboard is not a story. That's the first thing to say. It's more of an interface, so it's meant for repeated use. That's the difference between a report and a dashboard. And that's why we're using a reusable function. It's more of an interactive approach.

7:11You could say an interactive mindset. And here we're going to plot churn by tier for a particular region. And you could say this is simulating kind of like a filter. It's the same metric, but we're packaging it a little bit different. It's the same as a pivot table idea, but it's just applied after filtering. That's one way to think of it. We're handling empty filters gracefully. So that's just a little detail. And then we're going to run two regions live. So let's go ahead and run this and let me show you what I mean.

7:42And now let's run this. OK, so now you have basic and pro and then you have basic and enterprise as a comparison. You can also use this UI that I have right here. Let me run this and show you how that works. And you can just click on South. And this is really great for non-technical audiences. Here's the unknown, West and Midwest. So we'll go back to South here. I'm showing you that you could use this user interface dropdown for the dashboard. All right, so that's it for this video. And I'll see you in the next video on core analytics techniques and where EDA fits in.

8:18See you there.

0:00Under the hood, analytics uses different classes of techniques, depending on the question that could be descriptive, inferential, predictive, and prescriptive. And that's what we're going to cover in this video. We're going to connect directly to the data plus domain 3.0 analysis, especially choosing the right method for the situation. And we'll start. So let's start with descriptive here. We're answering what happened and we can use summaries and distributions, but this is where the EDA usually starts.

0:33When we're looking at inferential statistics, we're talking about confidence intervals, which we'll get into a little bit later, including tests and regression. But for now, inferential answers, what can we conclude about a population from a sample? Okay. And when we're talking about predictive analytics, we're talking about forecasting risk and likelihood. And then finally here with prescriptive and you can think of a prescription here. We're recommending actions under constraints, take two cookies and call me in the morning. That's my kind of doctor, but that's just really an easy way to remember this. And again, we'll have a lot more opportunities on how to choose the right method for the question.

1:15When we continue to explore core analytics techniques throughout this course, and we're just getting started. All right. So now to talk about AI here, modern analytics, isn't just charts and averages anymore. And it's increasingly AI powered. And when I say AI, AI, isn't just one thing. So here AI is an umbrella term for machine learning, deep learning, generative AI, but it's not RPA. Then that's what I wanted to talk about. Artificial intelligence. It's really an umbrella term that includes everything such as deep learning, machine learning, generative AI, NLP, and more.

1:54The purpose of this slide is to let you know that RPA is not the same as AI. RPA stands for robotic process automation. Let's break this down. So when you're thinking about AI, just think that AI is an umbrella term. And there's two main fields, machine learning and deep learning. These are subsets of AI. Don't worry about that for now. These are techniques that imitate aspects of human behavior or decision-making. A simpler way to think of this is software modeled after the brain.

2:27It uses artificial neurons and all of this really super interesting stuff that we're going to get into in this course. So by the end of this course, you'll have a good idea of machine learning and all the subsets and related technologies. For instance, when we're talking about machine learning, this is really a subset of AI where algorithms learn patterns from data. Here, when we're looking at deep learning, this is a subset of machine learning, which is a subset of AI that uses neural networks, especially with images, audio, and large scale text. And that's the software modeled after the brain.

3:04And that's why we're using neural networks or more accurately, we should say artificial neural networks. We have biological neural networks and these models, AI models use artificial neural networks, and there's so much more. For example, generative AI, this is where the chat bots like chat GPT and Grok and Anthropic come from, which includes NLP. We have here, which is natural language processing. Again, we'll get into all of this very soon. Just remember in analyst work, this can help draft narratives, summarize documents, suggest SQL code or scaffolding, or translate findings into plain English, but you're still going to verify outputs and follow governance.

3:50You don't just rely on AI. It's actually pretty dangerous to do that. And then finally we have the whole purpose of this slide, which is to compare AI and RPA, which is again, robotic process automation. This is where people get confused. RPA is not learning it's rule-based automation. These are bots that do repetitive steps like exporting a report, moving files, refreshing templates, or maybe sending routine outputs. So just remember, these two are not the same.

4:23RPA is kind of the boring one. And all these other technologies are the exciting AI ones. So just remember RPA is kind of automated, a little bit boring, but that's a good thing because we want it to be automated and rule-based and predictable. And when we look at what machine learning looks like every day for an analyst, you might see something like customer segmentation here. We're grouping customers to support retention or maybe onboarding offers or to identify the strongest customers. Anomaly detection here.

4:55You want to look for those spikes, right? You want to spot unusual spikes and logs errors or activities. Outliers are a good way to spot those. And finally, we have sales forecasting. Here we're estimating future demand from historical data or historical patterns. And all of this power only works if people trust the results and the organization stays safe. That's why governance is the foundation in the temple that we looked at. Remember that analytics roof, the three pillars.

5:26And then now here we're talking about governance, the foundation. These include consistent definitions, access control, privacy rules, quality expectations, and even documentation practices. So if you don't write documentation, maybe you should, it's a big part of data governance and AI governance moving forward. This is what lets analytics scale responsibly. All right. So here we have 1.0. You want to understand, this is the first domain. These are systems, data concepts, and AI terms.

5:59All right. So let's map these to the different domains. So for 1.0, we're really thinking about systems, terms, AI concepts, data concepts. For 2.0, we're thinking about acquisition and cleaning. That means acquiring, cleaning, shaping the data, basically preparing it. And for number three, where we analyze, these are analysis methods, statistics, machine learning choices, maybe deep learning. And then once you have something to talk about, well, maybe you can create some visualization, some charts or a report or dashboard, depending on the context.

6:35And then finally governance. Here, we want to ensure access privacy quality and trust for responsible use. And that's it for this last video, where we explain the modern data analytics roles. And until next time, I hope this has been informative. And I'd like to thank you for viewing.