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Data Science Tutorials: The Outline
Learn data science one task at a time while building your stack.
Tasks - Break down larger projects into 5-minute tasks.
Projects - Build knowledge by finishing a project
that encompasses about 10 tasks.
Formats - Supplement video learning with code
examples available in web format.
Open source - Learn today's most important
open technologies that are in demand.
Outline - Launch video and web content straight
from this page.
Published: November 03, 2017
Updated: February 21, 2021
See the detailed outline below. How exciting! This is going to be fun.
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Our Outline for Data Science Learning
A data science course uniquely designed for the full stack web
developer. This course offers a way to analyze big data and also
learning Python for data analysis, you will also learn other
open-source tools. In the end, this will help you achieve your
career aspirations. Good luck!
Videos can be accessed individually below by clicking the
symbol (opens a new browser tab
or the YouTube app on mobile devices). Web pages are accessed
with this symbol .
Project 1 - Server and OS Setup
In the opening project we select the Operating System appropriate for
building data science projects that can scale, while walking through an
YouTube Playlist -
01 Server and OS Setup
What is the quickest way to
learn Data Science?
What is Data Science? The
factors for success.
Is programming required for
Data Science? The pros and cons of spreadsheets.
Which operating system is most
common in Data Science? Linux, Mac or Windows?
How should you prepare for a Debian Linux
How do you create a Linux-bootable
flash USB drive?
How do you install Debian Linux
on Intel and AMD hardware using amd64?
How do you install SSH on the
server with openssh-server software in Linux?
The Windows SSH client PuTTY
plus the Mac and Linux ssh commands.
How do you configure PuTTY for
SSH access to Linux from Windows?
Project 2 - Linux for Beginners
In the second project we get a feel for Linux, navigate the system,
learn how commands work, review the setup, install software and get
ready to code.
YouTube Playlist -
02 Linux for Beginners
What are the first steps when learning
Linux? A tutorial for beginners
How do you navigate Linux? Using
pwd cd ls mkdir mv and rm
How do you find commands in
Linux? Using apropos locate find and which
How do you find command help in
Linux? Using whatis man info and type
What are shell builtins? Using
type bash and help in Linux
Which Linux characters do
beginners use most? - / ~ ; # . > | Ctrl-c
How do Linux configuration
files work? Interactive and login sessions
How do you install software in
Linux? Using apt-cache apt-get and dpkg
How do you set permissions in
Linux? Using sudo su id chmod and chown
What is the Linux environment?
Using printenv set export and echo
How do you enable syntax highlighting
in nano? Using .nanorc and touch
Project 3 - Python for Beginners
In our third step we install Python and play with strings,
variables, relationship operators, keywords and escape sequences. It
isn't as fun as crunching data will be later, but this is a necessary
first step with our main programming language.
YouTube Playlist -
03 Python for Beginners
How do you install Python on
What are the rules for math in
Python? Using PEMDAS and # ( ** * / // % + - )
How do you use relational operators
for math in Python? Using == < > <= => !=
How do you create text strings and
variables in Python? Using = ' ' " " print()
What are Python functions?
Using print() type() str()
Are functions in Python similar to
Excel? Using round() int() float()
How do you find help in Python
using help() and pydoc?
What are the top 5 Python
keywords for beginners? True False and or not
What are the first 6 text
functions for Python beginners? print() type() str()
len() ord() chr()
Which 5 escape sequences should
Python beginners learn first? \\ \' \" \t \n
Which 8 math functions should
Python beginners learn first?
Project 4 - HTML and CSS for Beginners
Next, we'll learn a little HTML and CSS because browsers will be
with us for a long time.
HTML and CSS, do you need to learn them for Data Science?
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