Would you like to learn about data science for cloud computing? If so, this video is for you.
Hi, I'm Michael Gibbs and I'm the founder and CEO of GoCloud Architects. And I've been working in
technology for well over 25 years. And I've been coaching and mentoring others in technology to
get their first tech job or get promoted in tech for well over two decades now. And today we're
going to talk about data science. Now, I get asked constantly if I study machine learning,
will that make me a data scientist? And while machine learning is a tool used by
data scientists, it's not data science. So we're first going to talk about what is data science and
then we'll talk about how it works, why it's used and why organizations love data science
and why we think data science is one of the most important cloud computing careers of the future.
So data science is the study of data. So study of data and what's going on here,
the way data is studied, it's combining some mathematics, specifically, a lot of advanced
statistics and computer science. And the goal of data science is to be able to look at the data
and obtain information from the data to give the business actionable intelligence
so they can make better business decisions. So that's why organizations use data science,
they want to make better business decisions. Better business decisions equal competitive
advantage to their competitors. Or if their competitors are hiring data scientists and
they don't have a data scientist, there'll be at a competitive disadvantage. So in the future, we see
data science for cloud computing to be a fantastic career opportunity.
See, what's going on is organizations are collecting a lot of information. And as the
amount of information grows, it becomes very hard to gain any kind of actionable
insights or make better decisions from your data because you just get so much of it.
Especially now, look with all the unstructured data that we deal with in our everyday lives,
media, audio, visual, email, documents, sensor data, it's just coming from everywhere. There's
more data and it's coming more and more from more places at more times. So the data scientists are
going to know how to mine all this data to do things, to make better business decisions.
So all this information just creates so much opportunity. So that's what data science does.
So data science begins by asking the right business questions and framing the problem to
solve because if you don't know what the problem is, you can't solve it. Questions can be, who buys
our products? Or how to predict which markets are going to generate the happiest customers?
These are the kinds of questions that a business would ask and the data science models can actually
help improve and provide answers on this. See what happens is you define a problem and then you start
collecting data from all these relevant sources that could be databases, APIs, logs, web servers,
social media platforms, and more. You're just collecting data from a large period of time.
And once this data is collected, the next step is you to prepare the data,
because if it's all raw data, there's nothing you can do with it. So you take the data,
you prepare the data and you clean it. Now this cleaning step is pretty extensive because
a lot of time has to go through with dealing with missing variables, for example, or duplicate data,
data corrections, and the such. So data preparation is a very important part of
the data scientist job. The idea here is you've got bad data going into your model, you're going
to have bad outcomes. Garbage in, garbage out. The technology things don't change that much,
they just modify as we use them differently. So after the data is collected and then cleaned,
what the data scientists do is they use these exploratory data analysis tools,
and they're using these tools to basically analyze what's going on so they can predict variables.
And then they basically put them typically in some type of a learning
model, it's very common to use machine learning. So as you can see right now,
machine learning is just a tool used by data scientists to actually make better decisions.
See machine learning is incredible. What happens is, as you start collecting this information,
you put it into machine learning algorithm. And depending upon the question you're asking,
the machine learning algorithm can actually help give an answer. So after you get an answer,
you still need to see what the answer actually means. So typically speaking,
you take these results and they're showcased in a data visualization tool. And when you can look
at the data visualization tool, it becomes much easier to manage the data. And of course, as your
data changes over time, your data scientists will have to change their models over time.
So now, you know the basic job of the data scientists. Now, we're going to talk a lot more
about the tools data scientists use and some applications and use cases.
Now let's explore some of the great ways that data science can be used
by businesses to improve performance or other applications used by data scientists.
One of the neat things that you can do with data science is basically anomaly detection.
So look at it this way, you can detect spam email for example, or you can look at credit card
behaviors that don't necessarily make sense. This kind of anomaly detection can be really useful in
businesses organization, but it could also be used in a security environment. You get a
security threat and the security threat determine if there's an anomaly and it can go and fix it and
remediate it for you. So anomaly detection is one of the common use cases for data science.
Another thing that's pretty common is pattern recognition. So lots of things occur in patterns.
And if you can find patterns, in many cases, it can help an organization say, what if there
was a pattern with SOC trades where you could see what was going up and down, and you knew what the
pattern was, and you can buy the stock when it's way up and sell it before it goes down. That type
of pattern recognition is something that's very useful for machine learning, but it can be used
with things like identifying medical conditions or trading systems, as we talked about. Identifying
trends that a consumer would have and even making purchase recommendation. So that's another reason
businesses might use data science for pattern recognition. Predictive modeling is another
use case where, basically, businesses will use these types of models to try and make forecasts,
to try and figure out what customers are going to do so they can then adjust their
product and production life cycles based upon what they feel customers are going to do.
Data science could be used in a recommendation engine. For example, when you log into, say,
Netflix or Amazon, it may look and suggest certain movies based upon preferences you've
given it in the past. That's a type of predictive modeling. [inaudible 00:06:17] for example,
organizations use data science to see customer sentiment. Are they happy? Are they not happy?
And what should they could do and change? Realistically speaking, there's a lot of
tools and these are just some of the many, many use cases used by organizations with their data
science. So data science can really give an organization a real competitive advantage.
Now that we talked about what is data science, and we talked about some use cases for data science.
The next phase is we're going to talk about some tools used by data scientists. Now, since the data
scientists are, realistically speaking, pulling in all these incredible amounts of data from so
many places, they have a lot of tools that they need to use. So we're going to talk about tools in
terms of programming languages and frameworks and visualization tools. Now, obviously there's some
different things for the cloud, and we're going to have another video on that coming very soon.
But the first part of understanding data science and even data science for cloud computing
is understanding data science first, as well as the tools that data scientists typically use.
So let's begin with programming. When you're dealing with this kind of information,
you've got to find a way to get through the information and pull the information. And for
that, data scientists typically have some good coding skills. They typically have program coding
skills in the following disciplines, Python, R, Spark, and also there's a lot of work done with
SQL and even sometimes Excel when you're dealing with data scientists. But really these languages
you're dealing with are re really Python, R, Spark, and a lot of use of SQL as well as Excel.
Now, that's going to help you obtain the information and do some of the things,
but how are you going to look at the information? Well, to look in the information you needed,
but visualization tool. And that's typically accomplished by one of the main visualization
tools. The cloud also has their own, but Power BI is extremely common. Tableau is another extremely
common one. And these are the kinds of things that you typically would use to visualize your data.
Now, to mine through the data, you typically need a framework for that
and the two most common off of the cloud are really TensorFlow and PyTorch, And these are
your most common frameworks for working in the machine learning sort of environment.
And of course, if you're dealing with Python, you may need some extra libraries such as Pandas
or NumPy, or there's a couple of others, but these are the kinds of things they use.
And of course, when you're working in these environments, you need an editor. And typically
speaking, people use something like Jupyter Notebook or Spyder or VS Code for something
like that. And of course, when you work on things, you're going to deposit them somewhere and you
typically use GetHub for that. So now, you know what is a data scientist and you know the use
cases for which businesses use data science and now you know the tools that data scientists use.
So you can see machine learning is a tool used by data scientist. So when people ask the question,
if I study the AWS machine learning specialty, will that qualify me to be a data scientist?
No. Having said that if a data scientist takes that exam and certification, it will teach them
how to do the things that they're used to doing on the cloud, which is a slightly different way.
And we will have another video coming very soon in terms of how you do these data science things
coming on the cloud. But first and foremost, we always want to make sure people have the
fundamentals of how the system works before talking about moving and migrating to the cloud. I
hope you've enjoyed this video on data science. I hope it's been enlightening to you. Data science,
as we believe, is going to be one of those critical cloud computing careers in the future
because you're combining data and all the benefits and things that you can glean from the cloud.
Let me tell you about some free services that GoCloud Architects and GoCloud
Careers provides for the community. Every Monday and every Thursday, we have a completely
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I'd like to also remind you of some free things that we do to help cloud architect community.
We have a completely free AWS certified solution architect associate course. I'll
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architect professional course. We taught at live on YouTube. You can see even people ask
questions and that is completely free. I will leave the link in the description below.
We have a completely free AWS certified solution architect associate. Again,
I will leave the link for that and the description below. Thank you so much
for watching this video and we look forward to seeing you in another video next week. Take care.