Explore Data Science Foundational Concepts

0:00Hello and welcome to the ITCA Data Science Foundations course.

0:05My name is Jonathan Barrios and I'm super excited to work with you to

0:09illuminate all the different domains that come together to form the Data

0:14Science Foundations.

0:16But first, let's talk a little bit about me and my approach to this course.

0:21I'm a Data Science and Machine Learning Instructor and I'm passionate about

0:26combining deep learning plus music.

0:28That being said, I create content with a goal of simple understanding without

0:34complicated jargon.

0:35And the goal is that we're going to be using simple to apply terms.

0:45And hands-on application with something called Google's CoLab.

0:51And this is a super fun little notebook that we can enter code and it'll just

0:55spit out the answers.

0:56And it's a great way to work with Data Science, especially when you're working

1:00with the foundational concepts.

1:04In short, I don't think that Slides Alone really is a useful delivery of

1:10information, especially Data Science concepts.

1:14Moreover, foundational concepts. And that's why we're going to use Google CoLab

1:19because the Slides Alone really don't cut it.

1:21So we're going to add stories, hands-on or use cases and hands-on applications

1:26to really cement all of these concepts.

1:29But now let's go ahead and take a look at all of the domains that make up the

1:33Data Science Foundations.

1:36And when we're talking about Data Science Foundations, this is exactly what we

1:40're talking about.

1:42So Data Science here is really an umbrella term.

1:45So if you want to think of this as like a larger Venn diagram, so this would be

1:49number one.

1:50And then Data Structures, right, that sort of all be housed in here, all of

1:55these in different proportions, right?

1:57There's going to be a little bit more than each one.

1:59So here we have Data Structures and this is just talking about the different

2:05data types.

2:06And this could include static, trees, which is a type of data structure, linear

2:17, and nonlinear, right?

2:18So line data and non-line data.

2:21This is probably the easiest way to explain this at this stage, right?

2:25Because we're just talking about the foundational concepts.

2:30And then moving on, we'll dive into statistical analysis.

2:34And these are going to be methods and techniques that study the populations and

2:38samples.

2:40And let's say you have a sample of all of the voters and then, or excuse me,

2:44the population.

2:45So this is all of the voters.

2:47And then here is a sample.

2:48So you take a small, like let's say 10% subset.

2:51And other techniques.

2:54And the idea here is that we want to find trends and other useful pattern

2:58hidden inside of raw data.

3:00Moving on to data management, really about storing how to store data.

3:06And we're really going to dive into relational.

3:11And non-relational databases.

3:20And some SQL SQL.

3:25And don't worry if you're not familiar with relational or non-relational or

3:30even SQL,

3:31because we're going to go over each one of those.

3:33And then we'll dive into data governance.

3:37But what is data governance?

3:38And against, don't worry if you don't have a working definition of this.

3:43Just think data management.

3:46For now, that includes security and protection.

3:55And more importantly, how to work with big data and how to manage that.

4:04Because it can be tricky.

4:07And then we'll dive into data access and protections.

4:11And here you can think about managing permissions.

4:17And securing data.

4:24Which is this little bit of overlap between data management and data governance

4:31.

4:31We'll talk about that because we used security for both of these, but they're a

4:36little bit different.

4:37And more importantly, when you're working with third party vendors.

4:42And here for data discovery and collection, this can include hypothesis testing

4:51.

4:51Understanding business models.

4:55And what is the role?

4:59And what is data taxonomy?

5:05And then we move to machine learning, my favorite.

5:11We're not really going to get into machine learning per se,

5:15but we will discover the data types.

5:18And the some basic concepts.

5:22Because it's super important, right?

5:26It's all the rage right now.

5:27And statistical analysis.

5:32You may have heard machine learning and also statistical learning.

5:35They kind of go hand in hand.

5:36So we'll talk about that relationship.

5:38And then finally, we have data presentation.

5:42Probably the most important because here at the very end, you're going to

5:47explore how to

5:47summarize the entire data science process, right?

5:51Because you're going to have to communicate this, right?

5:56And that's the most important part.

5:58Communicate to stakeholders.

6:02Because once you find that information or you use data science

6:06and your understanding of data structures, right?

6:09And the different data types and you collect that and use some sort of

6:13statistical analysis,

6:14take it through the organization, using data management and data governance.

6:19And then here are the access and protections,

6:21making sure that you don't compromise that data.

6:24And then here you actually find those trends,

6:26use machine learning if you need to.

6:28And then here is where we communicate that to the stakeholders.

6:32And then you can take some sort of action and increase the value of your

6:36organization.

6:37And what we're going to do is take all of these and turn it into one journey.

6:44And we're going to go through this journey together.

6:47And that slide, or video I should say, is next.

6:52So we're going to dive right into data science and I will see you there.

0:00Welcome back. In this video we're going to dive into data science and what is

0:05data. So again in the previous video we talked about everything that we're

0:10going

0:10to learn in this data science course, the Data Science Foundations course. This

0:15is

0:15this first scale in that course and we're going to start off with answering the

0:19questions, what is data science and what is data?

0:25Oops. Alright so what is data science? Data science is the process of

0:33extracting

0:34valuable insights from raw or unprocessed data. So this is really the key.

0:41There's

0:41nothing really invaluable inside of this raw data, which is also unprocessed

0:46data,

0:47right? Data that you would see as is. And again like we said before it combines

0:52techniques from statistics, software engineering, business analysis and other

0:58fields as we discussed. And here is the takeaway, the goal. So the goal of data

1:04science is to uncover useful knowledge and information from large and complex

1:10data sets and various formats. So the formats are the data structures and

1:16that's really tricky because we have to work with all of these structures and

1:20then make them available so that we can actually get the useful knowledge out

1:26of

1:27that raw data right there. Let's take a use case for example. Let's say that we

1:33have some sort of a retail store, right? In a data scientist can analyze

1:41customer purchases. So these could be the purchases, maybe some website traffic

1:50and

1:51most importantly demographics, right? What is the demographic of the user? Then

1:56we

1:56can take all of this and with data science we can make predictions about

2:03future buying. So let's say predict. So buying behaviors, right? Because if you

2:15know what those behaviors are then you can make personalized marketing

2:22strategies. And the takeaway here is that we're processing and interpreting

2:27this

2:27data so that businesses can enhance customer satisfaction, optimize inventory

2:32because imagine if you have no inventory then you're going to make zero money.

2:39And

2:39ultimately we want to drive sales growth and demonstrate the power of data

2:43science to our stakeholders because we can transform raw data into actionable

2:48knowledge. And that's the takeaway, actionable knowledge. Because it comes from

2:53raw data right up here that it's raw or in process and really doesn't have that

2:58much value. So we're taking no value and turning it into value. Pretty cool.

3:03And

3:03those would be called insights, right? So insights are when you extract, whoops

3:09,

3:09let me go back, insights are when you extract useful knowledge and information

3:13from usually big, whoops, let me turn my pan back on that's what's happening. I

3:18'm

3:18trying to write this, turning it into a stylus. So extract useful information,

3:22knowledge from big and complex datasets. And that's going to be also what we

3:28call big data. We're going to dive into that because that's a hold. It's a big

3:32domain unto itself. Let's say that. And there's going to be a lot of different

3:36formats that you can might be familiar with comma separated values, or maybe

3:41even a dot pi file, which is a Python file or a IPY and B, which is this

3:48is a IPython notebook or a Jupiter notebook. These are all very popular data

3:53science data formats, right? And so you have to be familiar with that. Because

3:59without that, you can't offer these insights. For example, maybe we have like

4:06a hospital, let's say that this is like, hopefully this will look like a

4:10hospital

4:10and big door here. Supposed to be like a little cross, right, like a hospital.

4:18We could say this is health care, right? Some sort of a health care

4:22organization.

4:23And what they can do is analyze patient records, medical images, maybe even

4:30genetic data to find patterns, right? And we want these patterns to lead to, I

4:37guess it could be better diagnoses and treatment, right? So ultimately you're

4:46going to get better care. And also maybe even make the operations more

4:51efficient

4:51than drive the costs down. So that's what we do. We bring value to our

4:55organizations. And we can show how important it is to manage and analyze

5:00different types of data, right? All of these data. If you don't know how to do

5:04that, you're not going to get these insights here. Right, that's the takeaway.

5:08And here we can add something really important because data science requires

5:16domain knowledge. And that's a key takeaway to answer what is data science.

5:20And here's why. We use domain knowledge to guide business decisions, right?

5:27And here's a good one, data driven marketing, because we're using data to make

5:33marketing

5:33decisions or just making decisions in general and targeting of services with a

5:38goal of improving

5:40customer and stakeholder expectation. We've talked a little bit about

5:44stakeholders and how we

5:46take all of the steps in the data science foundations to share that knowledge,

5:51right?

5:51We want to share how to increase revenue or add value to your organization

5:57using some kind of

5:59domain knowledge. For example, let's think of a retail company that uses

6:04insights from customer

6:05data to tailor marketing campaigns. And these targeted services can be more

6:11effective by doing

6:12so. So by basing decisions on solid data, businesses can make better informed

6:22choices,

6:23right? And in this case, they can maybe boost customer engagement and meet

6:27expectations better

6:29for their customers. And this approach ensures that we're taking the strategies

6:33and aligning them

6:34with real world insights. And for this case, for retail markets. And last but

6:39not least,

6:40we have strategy. And this is when we leverage the ability to identify useful

6:45patterns and then

6:46use data driven insights to avoid expensive business decisions. For example, if

6:53we're analyzing sales

6:55data, a company can spot some kind of a trend. Let's say that they're doing

7:00some stocking and

7:01they want to avoid like adding too many of one product, right? If it's not

7:07popular, right? Like

7:09let's say that they have 100 here, but there's only a demand for three,

7:13something like that.

7:13This is a big difference. And this is how we can use our resources wisely and

7:19use make

7:20strategic strategic decisions based on again, solid data. And that's why we say

7:28things like data driven.

7:29All right, so that's it for this video. And I'll see you in the next video when

7:36we dive into

7:36what is data, right? We're getting into these foundational concepts. See you

7:40there.

0:00Welcome back. In this video, we're going to answer the question, what is data?

0:04And dive into a little bit of the nuances of the different data structures so

0:09that

0:09when we get into those data structures, especially when we get to that skill,

0:12it's not going to be like, what is all this stuff, right? We have already laid

0:16the foundation and that's the goal of this video. Alright, so what is data?

0:21Data is information that can be collected, measured, manipulated, and analyzed.

0:28So

0:29we've talked about these a little bit, right? We're just making sure that we're

0:32on the same page. It can be numbers, text, images, or other information. So now

0:38we're getting a little bit more granular, right? Numbers, text, and then images

0:45. It

0:46can be sunsets, right? Or what is this other type of information? Well, there's

0:51going to be what seems like an endless amount of information or data structures

0:58,

0:58right? And that's just if you're not familiar with these data structures. After

1:02a while, you see that you work with certain kinds more often than others. But

1:06there's always going to be these more common and less common data structures.

1:10And this is a paramount skill to have because one of the most important things

1:14for data scientists is to work with data, right? With the science of data. So

1:19we're

1:19going to make sure that all of this is solid as we have this iterative journey

1:23and answer these questions along the way. But more importantly, let's answer

1:30this

1:30question. Data is some kind of information, right? It has no value because

1:42it's raw and it's self-referencing, right? So let's talk about this referencing

1:51. It's

1:52not connected to anything else, right? And that makes it stand alone. So if it

1:58's not

1:59connected to any other context, then it's really like it doesn't, it's like not

2:04valuable, right? But it is malleable, meaning that you can change it, but it

2:14lacks

2:15integrity. So these are some really important attributes of data. And this is

2:25what is data. So I think we're good to go to the next one. Now we know the data

2:31is

2:31information that can be collected, measured and analyzed. It can be numbers,

2:35text, images or other types of information. But now we're going to get more

2:38granular and talk about the two big categories of data. Qualitative and

2:46quantitative. So what I would suggest here is to think about the root of the

2:50world, root of the word. So qualitative data, I'm hearing quality, right? So

2:57that's

2:57the root. And it's referring to the descriptive information, right? That is

3:03not numbers. Text, images, it's, let me put it this way. It's not numbers,

3:12right? But

3:14it is text, images or categories. Right? The most famous examples are we

3:20colors, names or labels, right? These are not numbers. And if it ever does use

3:26numbers, they don't have a meaning, right? They're not being, it just kind of

3:30like

3:31male is one, female is zero or the other way around, right? These numbers don't

3:36represent anything except for male females or like genders in this case. So

3:41what is quantitative data? So here, quantitative, like quantity, quantitative

3:47data is a numerical information that can be measured or counted because you can

3:53do that with numbers, right? You can count your age, how many, what is the

3:58collective age of a group of people? What is your height or the median height

4:01of a

4:02group of people? And the same thing with temperature, you can get the median

4:05temperature, the actual temperator, but it's actually some sort of numerical

4:09piece of information, like 74 degrees. I wish it was 74 degrees. Okay, so now

4:15we're circling back to what is unprocessed versus process data because we

4:20talked

4:20about this raw data, right? It has no inherent meaning or value for that matter

4:26.

4:26And data science process allows us to draw conclusions and gain insights. To be

4:34meaningful, the data has to have some sort of dimensionality, connectivity, and

4:39transformability. So dimensionality, multiple features or aspects, and I

4:44promise we'll get into that. If this is not clear, don't worry, we're gonna get

4:47into each one of these. Connectivity, the relationships between the data points

4:52,

4:52you remember when we said that the data was isolated, not connected, had no

4:57context? Well, that's why it was not very useful. So it needs to be connected

5:02and

5:02have relationships with data points. That's the beginning of the value and

5:07transformability, the ability to be cleaned and formatted so that we can

5:12perform analysis. This is a really big takeaway. Cleaning is where we spend,

5:17you

5:18would be surprised, like how much time we spend cleaning data. You would think

5:22that data science, you maybe have a data science background, you may already be

5:26a

5:26data scientist, so you may already have your own understanding of this. In

5:31short,

5:32it's counterintuitive to think that a data scientist is going to be spending

5:35most of their time cleaning data, but it's a thing. So if you didn't know that,

5:39we

5:40spend a lot of time just doing pre-processing so that we can get to the fun

5:44part here of analysis and other cool techniques that are more advanced. As

5:49promised, we're going to dive deeper into dimensionality. So here dimension

5:53ality

5:54is the number of features or variables in a data set. For example, the

6:00dimensions

6:00for time can be days, minutes, and seconds. However, in data science,

6:06techniques like dimension, dimension reduction or dimensionality reduction

6:11are used to simplify the data set. So when you want to reduce the complexity,

6:17you

6:17can reduce the dimensions by selecting important dimensions such as date and

6:22hour without seconds because the context is saying that, "Hey, we just need to

6:26know

6:26what date and what hour it was so we don't need that." So we can reduce the

6:32complexity by taking off the dimensionality of seconds in this case. So if

6:41you're looking at a data set of cars, for example, you might be thinking of a

6:46spreadsheet like this. And you might have attributes like horsepower, weight,

6:59and

6:59what else? Let's say fuel efficiency like miles per gallon, right? Let me write

7:05that.

7:06MPG and so on and so on, right? These are all the different attributes or

7:13dimensions. So the total number of features in the data set of the data set

7:18has three features, for example, horsepower, weight, fuel efficiency or

7:21miles per gallon. We can say that this data set has three dimensions. All right

7:29,

7:29so I said horsepower, weight, and miles per gallon. All right, so this is a

7:39good

7:40stopping point. Now we'll see you in the next video when we dive into an

7:42example

7:43using Google Colab. See you there.

0:00Welcome back. Before we dive into Google Colab, I really feel like I should

0:04explain

0:05what Google Colab is. If you already know what a Jupiter notebook is, locally,

0:10it's

0:10a cloud version of that that is available on the browser. You don't have to

0:13install

0:13anything. You can just start using Python right away. It's actually the best

0:17way to

0:17learn Python. It's just to use Google Colab so you don't have to spend all that

0:21time getting

0:22your development environment up like if you were like a computer scientist,

0:27right? Where

0:28data scientists, we have some computer science, but we're not exclusively going

0:32to be using

0:33the same tool. So this is a great tool. So let's get into it. Let me explain

0:37how all

0:37of this works. Okay. So here, Google recommends using Chrome. No thanks. I use

0:42Brave. It's

0:43a little bit I can protect my data more than Chrome, which is going to power

0:48the this

0:49organization. So I'm going to say don't switch. And then I'm going to say coll

0:58ab. One word,

0:59one L. And then it says welcome to collab. You can click on this or this one.

1:04It really

1:05doesn't matter. So if you click on either one of these, it's just going to let

1:08's click

1:09on this one. I'll just show you. And then you go to new notebook or open collab

1:14. So you

1:15have to have a Google account and be logged in. And once you do that, you click

1:18on new notebook,

1:19you'll get something that looks like this. And I will give you a quick rundown.

1:25So here

1:25you can work with Drive, which is pretty amazing. And there's also a terminal,

1:29which is a newer

1:30feature. And you can also use AI, but we turned it off because we don't want to

1:35use AI just

1:36yet. It's not great yet. It needs a few more iterations before we can actually

1:40use it

1:41for data science. So here, what we can do is just say, let me prove it to you

1:45so I can use the

1:46exclamation mark and that talks to the terminal. Right. So instead of opening

1:51the terminal,

1:52I can just use this exclamation mark. And then I'm going to say here, Python

1:57dash dash version,

2:00and then shift enter, or you can push play. I mean, that was pretty fast, right

2:05? So we have

2:06this in Python installed. You don't have to do anything. You can just start

2:11right out of the box.

2:12Boom, start working with Python. For example, let's do the print Hello world.

2:17So you see that

2:18I'm not using any exclamation marks. And I'm just going to use you can use

2:21single or double quotes.

2:22Let's use double quotes and hello. World exclamation mark shift enter or

2:32alternatively,

2:32you can hit this play button. And then here you get the output, right? So this

2:37is Python and we've

2:37proven it. This is normally how you do that. So now you know what Google Colab

2:41is, and you'll know

2:42more about how it works as we progress through this video. But more importantly

2:47, for data science

2:48and machine learning, you can actually go over here to change runtime type and

2:53boom, you get these,

2:54this isn't totally wild. You can get all these GPUs. Not all of them are going

2:59to be available

3:00for free, but you can also select high RAM. So I'm not going to do that. But

3:04right now I'm just

3:05using the CPU. So if you're not going to be needing a GPU, always use a CPU so

3:10that we save those

3:11resources. Otherwise, Google will get mad and limit you and your ability to use

3:15these. So we

3:17have a free GPU. How cool is that? All right, so then we go over here. And this

3:23is going to be

3:24available to you. And right in under the video, just click on that in this

3:28notebook will open up.

3:30And then what we're going to do here is this run through this. So if I hit

3:34shift, well, let me

3:35explain everything before I run this. So here, these are all the packages that

3:38we're going to be using.

3:40NumPy is actually pandas is built on top of NumPy. And we're going to be using

3:46pandas for data

3:47manipulation and matplot.pyplot as PLT. Also, these are just aliases so that we

3:54can write this

3:56instead of these. So it's just an abbreviation. So it's just NumPy pandas mat

4:02plotlib. And

4:03seaborn is also for visualization and scikit learn. This is something that we

4:07're going, this is for

4:09decomposition. This is for the example, I don't want to get into this here. But

4:13everything up here

4:14is for visualization and data manipulation. And then here we're going to create

4:18some synthetic data

4:20with five dimensions. And I'm going to show you how I remove these features,

4:23right? That's the whole

4:24point. And then here, I'm going to create a data frame. And what a data frame

4:28is, in essence, is a

4:30table. So like a spreadsheet. So we'll get into that. We're also going to

4:34really break down spread

4:35sheets and open spreadsheets and get into that. But for right now, this is kind

4:38of a programmatic

4:40version of that. So let me go ahead and enter shift, enter. And then it's going

4:44to run this. The

4:44first time you do this, you can see over here is is connecting. It just takes a

4:48minute, connect to

4:50some computer in Google's warehouse. And then once we have that, boom, here are

4:55our features. So like

4:56I said, this is rows and columns, just like a table. And here are features 123

5:0245. So if you wanted to

5:03reduce that, you can use PCA, we're going to use that for dimension reduction.

5:08So let me show you

5:08what's happening here, we're going to say that we want to define how many

5:12components we're going

5:13to remove, right? So we're going to reduce the data columns one and two. And

5:19let's just take a look

5:20at that. No need to understand what's happening here, other than what I'm

5:24showing you, that we're

5:25going from five features to two features, right? Remember, we're talking about

5:30horsepower, weight,

5:31miles per gallon, maybe cylinders and some other feature. Maybe we just want

5:37the first two. I just

5:37want to know what the mile per gallon is in the way to car. Right, then you

5:42reduce the dimensions.

5:43And that's exactly what we're talking about. But you may be thinking, well, I

5:47sort of get it,

5:48but I don't get it. Let me show you exactly what's happening here. So here we

5:52have x, y, and we have

5:54x, y, z. So this is a three dimensions, and this is two dimensions. So you can

5:58literally see that

5:59we're reducing the dimensions manually. We took the third dimension on it here.

6:03So now we have two

6:04dimensions and the three dimensions. So hopefully that makes a little bit more

6:08sense. I feel that

6:11visualizing data is really the most superpower thing that we can do as data

6:16scientists. I think

6:17that this really is the takeaway. And on that note, I'll see you in the next

6:22video on connectivity. See you

0:00Welcome back. In the last video, we talked about dimensionality and we were

0:05reducing dimensions,

0:06dimension reduction. And here we're going to continue with connectivity.

0:11Connectivity in the context of raw data, this is super important. We're going

0:19to circle back

0:19on that versus contextualized data. I guess that's the comparison that we're

0:23making, right?

0:24Raw data compared to connected data. So connectivity are the relationships and

0:35dependencies between

0:38different data points or features within a data set. So we talked about these

0:43different features.

0:44So again, connectivity are the different relationships between features.

0:50Furthermore,

0:52it illuminates how data points are linked or correlated, which can be crucial

0:57for understanding

0:58the underlying patterns and presenting valuable insights. Again, this is

1:03increasing the value

1:04to your organization. And that when you do that, you basically get a raise. You

1:12get a promotion.

1:13This is a good thing. This is what your job is really bringing value to your

1:17organization.

1:18For instance, if I said 100, what does that mean? Right? That's raw data. And

1:25but we don't

1:26even know what it means. But how about if I was talking about chance of rain?

1:33And then

1:33I added this, my boom, I've actually added some context and I connected it to a

1:42percentage

1:43and chance of rain. So again, imagine a table. And then each one of these will

1:57have a number,

1:58right? And those numbers could be 100, like we did down here or 70 or whatever.

2:03And these

2:04numbers just won't make sense until you understand that this one is going to be

2:08for temp, right?

2:10This one's going to be for humidity. And this one's going to be for the date.

2:14When you do

2:14that, then you say, Oh, this is 100% chance of rain. And we know the

2:18temperature is going

2:19to be 75 degrees, things like that. I did get into the context a little bit

2:24early when

2:25we're talking about chance of rain. Whoops, let me turn the pencil on. There we

2:32are contextualized

2:32data. So when I was talking about chance of rain, that was context, right? And

2:41so we could

2:42actually get that context from the features. But let's take a look at the

2:46definition. Contextualized

2:48data enhances raw data, right? So by adding layers of information that provide

2:54depth and

2:55context, these could be features. So beyond mere numbers and facts, it includes

3:00elements

3:01such as personal perceptions, individual biases and cultural or industry

3:05specific contexts.

3:06And the takeaway here is that what we're doing is helping us have a better

3:12understanding

3:14of the data, right? Especially in the context of real world implications and in

3:19making informed

3:20decisions based on a fuller picture. So if you didn't know that this was chance

3:23of rain

3:23and you just had a 100 boy, what are you going to think with that, right? You

3:27're not going

3:27to really be able to provide this informed decision, right? It's like, well, it

3:33says

3:33a 100. But if it's a 100% chance of rain, then that's what we're adding here

3:40context.

3:40I'm making this super, super simple, but there are going to be times when you

3:44're working

3:45with data and it's not going to be so evident, right? It's going to be a little

3:49bit more fuzzy.

3:50But that's why these foundations are crucial because whatever data set that you

3:55're working

3:56on, whatever problem you're trying to solve or trying to understand these

3:59foundational

4:00concepts will help. That's the takeaway.

4:05Before we end this video, I did want to show you how to get into Google Sheets.

4:10And so the

4:10way that I did this is I went over here to docs.google.com/spreadsheets, but

4:15you can

4:15also just go to Google Drive and then click on spreadsheet. So what I'm going

4:20to do is

4:20click on this blank spreadsheet here. And so here we have a spreadsheet. And

4:24this is

4:25going to be part of the challenge to the very end. So what we're going to do is

4:29present

4:29you with this challenge involving a spreadsheet. So let me just show you how

4:33these things work.

4:34Let's say that we had, let me add a number here, and then I'm just going to

4:38drag this

4:39down. And then it's going to give us those numbers. But let me copy this or

4:43maybe I

4:43can drag it this way too. And so we have some numbers. It's not important what

4:48the values

4:48are. But let's say that I wanted to reduce the dimensions and I could cut this

4:52right

4:53here like this. Right? And then so I've removed that. But you can also delete

4:59the column.

4:59So delete that column. And so now I've removed the dimension. So we went from

5:03four dimensions

5:04to three dimensions. So just wanted to give you a little bit of context so that

5:08you could

5:09feel confident going into the challenge. And I will see you in that video next.

0:00Welcome back and congratulations on making it to the challenge.

0:04And the way that this works is the challenges are going to be an interactive

0:08step, right?

0:10To make sure that you have all of the concepts down and for example, we might

0:14do something like use Google Colab

0:17and you work with data or in this case we're going to work with a spreadsheet.

0:20And we already talked about how to do that, ignore that.

0:23You would go over here and go to new and create a spreadsheet or if you have a

0:28CSV file,

0:29which I'm going to provide to you, you can use import to import that CSV file,

0:33which is exactly what I did and then we have this.

0:36So that's your first step is to take that CSV file, which is a comma separated

0:41values.

0:42And I'll show that to you. Once you have that, then you import it and then you

0:45'll get this.

0:46And then what you have to do is answer questions about this.

0:51So we're going to talk about data types. We're going to talk about dimensions.

0:55We're going to talk about everything that we talked about or at least test your

0:58knowledge of all of this.

1:00But the good news is that this is not graded. So this is just an opportunity

1:04for you to get the juices flowing

1:06into apply these concepts. So I will see you in the next video where I show you

1:11the basically how I imported this

1:14and talk a little bit about the answers for each one of the questions. See you

1:17there.

0:00Welcome back and congrats on completing the challenge. So let me walk you

0:04through

0:04the steps of how I completed this. The first thing is I had to download the

0:09CSV file that's going to be located right above this video in the challenge

0:15instructions. And once you download that then you go to File and then you go to

0:20Import and then you go to Upload and then you find that file and then upload it

0:25.

0:25I've already uploaded to my drive so I'm just going to click this and then I'm

0:29going to click on Insert and I'm going to say Important Data Adomatically. I

0:35have changed anything and then once that's done, it might take a second but

0:41then

0:41you get this. So this is that Untitled spreadsheet and now we have all of these

0:45values and how many values do we have? Quite a few. Alright, maybe a thousand.

0:49So we have Horsepower, Miles Per Gallon, Weight and Cylinders. Now there's no

0:56right or wrong answer here. This is except for loading the data. You got to be

1:00able to see this. So outside of that this is more about just giving you an

1:03opportunity to experience the different data types and to think about it. In

1:09the

1:09first question is what kind of data type are the features here? This is Horse

1:19power,

1:19Miles Per Gallon, Weight and Cylinders. So we talked about the two different

1:23kinds

1:23of data types which is qualitative and quantitative data. So you have to answer

1:29what type of these? What type of data structure are these? And the easy answer

1:35is they're not numbers so they're going to be qualitative. These are

1:38going to be colors, names, labels. So these are labels for the data below. Then

1:44the

1:44second question is Horsepower. What are the values here? What kind of data type

1:50is

1:50that? So that one is clearly quantitative, quantity because you can count it,

1:57measure it, right? So that's what we're doing here. Horsepower and Miles, this

2:01Horsepower are going to be quantitative. And to be quite honest they're all

2:07quantitative. These are all numbers, right? So all of the values for each one

2:11of the

2:11features are going to be quantitative. And since this is raw data it's not

2:16directly useful but for this question we've been given a task to provide some

2:22sort

2:22of data for an article for a car magazine. And then we want us to compare the

2:28fuel

2:28efficiency of different vehicles. So we have to pick only two features. So

2:35which

2:35two features would you pick? However the question is really focusing on the

2:40most

2:40important feature. So just go ahead and enter Miles per gallon. And so once we

2:48have the Miles per gallon we can use any of these others to calculate fuel

2:54efficiency. I think the Miles per gallon is probably the most important and I

2:58would

2:58say either weight or cylinder but this is all subjective. The most important

3:02one

3:03is definitely Miles per gallon. And for the final one we've decided that we're

3:09going to reduce the dimensionality and we're going to include only Miles per

3:13gallon and cylinders. So we need to reduce the dimensions. The way that I did

3:18that

3:18is I go to data or in the safe which where is it? So I right click on this

3:27feature then I find delete column. There it is. Delete that column and we're

3:34gonna

3:34do the same thing. Yes I did that on purpose. And then here we're going to do

3:40the same thing and delete column. Oh no I need to select just one and delete

3:49column. Okay so now we have reduced our dimensions and now we can have or study

3:57the relationship between Miles per gallon and cylinder. The other data point

4:02that

4:02we're not talking about is like why kind of car but let's just imagine that

4:05those

4:06are handled after the fact once we know what are the most fuel efficient we'll

4:10probably pick the top five then we'll look at this index and find out what the

4:14cars are. So that's just not provided in this case but that's okay because we

4:18don't always have the complete data. But congratulations on completing this

4:22challenge and I will see you in the next skill. Until then I hope this has been

4:27informative and I'd like to thank you for viewing.