An ad-free and cookie-free website.
Videos can also be accessed from our Full Stack Playlist 1 on YouTube.
Data Science Tutorials for the Full Stack (4:57)
Welcome. Today's question is: what is the quickest way to learn data science?
I'm Paul, and thank you for joining me in 'life beyond the spreadsheet'.
In this tutorial, I'll explain these tutorials so you can evaluate whether they're a good fit for you, and your learning style. Normally, we go right to the text editor or command line, like this guy:
But for this tutorial, let's do a six-item orientation instead.
First, why Full Stack? Well, relative to other YouTube content you've seen, I think you will find it more professional, less disjointed and quicker-paced.
They take 10 minutes on something I can cover in 4, but what differentiates it most is the Q&A format and comprehensiveness of the full stack, like you can find at a cloud provider, meaning it's scalable, and those App development dreams are within your reach. Now I have your attention.
Second, for format, we routinely open with a question. All tutorials sit within a Project. Projects span from 5 to 15 tutorials and at a 4 minutes per video, that's 20 minutes to an hour per Project. And here sits a list of tools used during the tutorial.
In YouTube's Description area is a link to the Tutorial Outline which provides a tidy clickable link to all videos, because some find that easier than YouTube's navigation.
Third, for now, I'll just touch on Data Science, and punt on the definition until the next tutorial, because it can be controversial. Some say Data Science is simply a fancy label slapped on the long-practiced field of Statistics, like calling a used car pre-owned. Unlike doctors and lawyers, data scientists don't need a license to practice, so who is an who isn't a data scientist? Well, I do know millions analyze data, and in my view we all sit on a spectrum.
On one end are those confined in a spreadsheet (Beginner), next are those who embrace programming (Intermediate), and finally, at this end (Advanced), we find scholars and developers performing original research and sharing fabulous open-source code for us all to use.
Of course the buzzwords are out there: machine learning, neural networks, big data, and yes, that's where we're headed, together, but let's be realistic, it will take time. We need operating system, database and programming knowledge before we're able to do the fun part, in my mind which is analyzing data.
Okay, let's go to the Terminal.
Don't mind me as I poke around on this brand new installation on a local server built exclusively for this playlist.
I didn't install a GUI because it eats system resources and it really isn't necessary.
This is Linux at the command line, and don't be afraid of it, in fact I think eventuallly you will embrace it.
Think about it for a second. Notice how it isn't buzzing at you, and requiring attention. Notice, not one of those red notification icons, like on your phone, to interrupt your focus.
Now, think about the word 'control'. Look at this Terminal and think about who is control. You are. The computer works for you. When you are ready, you'll type something, otherwise it waits for you. Isn't that empowering?
Sure the drawback is the time it takes to memorize commands, but think about this, in this short time frame I scoped out system resources, programs and users; monitored processes; and saw an empty working directory ready for us to build something in.
So if you want to head here (Data Scientist), with me, beyond that spreadsheet, it's time to embrace the Full Stack.
Next, while you check out our software stack, which will grow over time, I'll run a summary by you.
Here goes. Full Stack is our open-ended path from the metal to the user experience, using open-source software, where each tutorial represents a question, or a task, and we'll check off one 4-minute task at a time until we complete a project. After that, we'll keep going and going until someone calls you a Data Scientist.
So to completely answer the question, try supplementing your school and day-job experiences, with these tutorials, and if you're ready, let's go on to "What is data science?" in tutorial 2, where I will also cover my background and prerequisites.
Have a nice day.
So you don't lose this valuable resource subscribe at our YouTube Channel, follow @factorpad on Twitter and join our no-spam email list.
A newly-updated free resource. Connect and refer a friend today.