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What is Data Science? Factors for success (4:42)
Welcome. Today's question is: what is Data Science and what are the factors for success?
I'm Paul, and I wake up every day, thinking about how to convey scientific topics to those who may not have a scientific training.
Here I'll define Data Science and any definition really depends on who's delivering it, so I'll say something about my perspective.
Next, we'll switch to the Terminal to expand on the second part of the question on keys to success, including prerequisites.
Then we'll return for the question newcomers are quick to ask: "what is your software stack?" It will be a quick 4-minutes, so let's get going.
Okay, Data Science. I often begin new topics with Wikipedia because it's collaborative, less biased and certainly ad-free, which is nice. There is a link to that and other resources in this video's Description on YouTube. The Data Science Association also has a nice succint definition and delineation between Data Science and traditional business analytics that you might find helpful.
So building on that, this is how I'd "explain it to my grandmother", as they say, using what I'll call a 'skills triangle' and in the middle, the purpose is to make decisions, data-driven decisions of course.
The first requirement, to me, is Subject Matter Expertise. In which field are you trying to make decisions? What are you passionate about? Healthcare, finance, advertising, technology, or basically, which industry do you know a lot about?
Second, Statistics, and here is where the procedures come in: data mining, machine learning, regression analysis, modeling, and note these aren't new. They've been used in academia for decades.
But what is new, in just the last 5 years, or so, which I label Cloud, is the mastery of open-source software and particularly cloud offerings.
Okay, so that's my view of the skills required in Data Science, and as covered in Tutorial 1, we're starting here (Beginner) and heading here (Advanced).
So you've seen my perspective, but who am I? Please see the link for more, but my training is in economics, so the social sciences. With a specialty in investments, MBA, CFA Charterholder and most of my I've managed stocks using a quantitative approach.
Now let's jump to Linux, connecting it with what it takes to succeed.
First of all, why are we here? Well, the best way I can say it is, here things don't change unless we want them to.
As you know, especially on the technology side, things change often. And, depending on who you're working with, your tools, or your stack as some call it. Service providers may change the user interface of an App, or a website as often as weekly. Meaning you have to take the time to reacquaint yourself with their changes. Here at the command line, that isn't the case. So this is our safe place.
And what I'm doing here is using a text editor called
nano located on the server, and
creating a directory and a text document summarizing this video.
We'll do a lot of work in
I wanted you to see it early.
I'm connecting from my local machine, or client, using the SSH Protocol in a program called PuTTY because currently I'm using Microsoft, but we will also use an Apple client later in the series.
And if you are new to this don't worry, the point of this Full Stack exercise is to take you through the whole thing and I'll explain it all in due time.
Okay, so this is the picture of our stack which we will be adding to, and for an orientation, I suggest watching video (tutorial) 1.
So there's a definition of Data Science and a little extra. Join us for the answer to "Is programming required for Data Science? The pros and cons of spreadsheets" in tutorial 3.
Have a nice day.
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