In this video you will follow along with me: Professor Meatball (phDog) and learning the
first steps of becoming a data scientist now stay tuned! [boof]
hey! Python! it's a good tool
it is not only what a lot of data scientists use for their technical
stack but it will get you that data science job so let's get started
oh wait. Business casual!
hello students I am professor meatball dogtorate in data science Corgell 1977 in
this course I will have my teaching assistant show you step by step how to
complete a data science project holding your hand start to finish first by
setting up a lean and robust Python data science environment on your system by
the time this video is over you'll have accomplished three things you'll have
installed Python with the most common and useful packages for data science
you'll have installed conda to manage packages and environments and finally
you'll have a single or bust environment which minimizes dependency issues by
relying on the Khan DeForge Channel let's get started
bring on the human Python is not a game it is a way of life and you're smart
smart enough to realize that it is a great language to start data science a
lot of data science environments are specifically supported by Python some
people use are some people use data but honestly for my money
Python is where it's at so let's not waste any more your time this is two
minutes of Python today we're just gonna get you up and started with the best way
THE way. Data Leap way. let's get started come on over my shoulder
each of these downloads will be two minutes each so I can best explain why
this is the best most lightweight way to start in data science here we are at
python.org we see release news and we're the latest version of Python can be
downloaded but hold up! there are two main problems with downloading python
here one this download won't come with a user interface to edit and compile code
something known as an IDE or integrated development environment while other
guides for Python 101 include recommendations for IDEs such as PyCharm
these environments miss out on flexibility in managing packages and the
ability to run code line by line and to your machine might already have an older
version of Python installed out-of-the-box Apple for example uses
Python 2.7 for its own purposes and that's a version of Python that hasn't
been on a landing page since January 1st 2020 so let's kill two birds with one
stone and NOT install Python! Professor show us a better way
The gold standard of data science environments for years have been the Anaconda
distribution which installs the latest stable version of Python along with over
200 packages however this installation would be a big fat fatty mistake sitting
at half a gigabyte of download size and taking up over 2 gigabytes of disk space
once installed this download brings along its tag-along cousin software such
as anaconda navigator which is a graphical user interface for those who
don't want to venture into the command line and Qt console a terminal like
program that makes graphs in line both of these programs are not necessary to
do data science with Python but we do need Conda a vital tool to manage
packages in environments enter mini Conda! A distribution that boils down the
download to only what we need mainly python and Conda
it's one-tenth the size of anaconda and exactly what we need for a robust
environment while it downloads I might mention you will not miss out on
anything anaconda offers you can always download it later using
Conda it grows as you grow once you're done pull up the terminal if you're on a
Mac OS or Linux and pull up the Anaconda prompt if you're on Windows let's make
sure it worked run the command Kondylis to return the
name version build number and channel for each package currently installed
note our friend Python is there too you're two minutes away from finishing
up let's change reality with this line of code so that the terminal doesn't
activate the base environment every time you open it this will pave the way for
us to create our own environments and run Python smoothly to create an
environment all we need to use is this line of code
kondou create an let's call it meatball environment after me make sure to also
activate it after you created now is the time to install Python packages we'll
use it with Condor channels which are just repositories of Python packages for
now I would rather you not install from the defaults channel and rather use
Conda forge the reasons of which I'm numerating here and they are almost
innumerable but finally one of the main reasons to use a single channel is the
consistency it provides with package compatibility will add Conda for jazz
the first channel with this command and Conda is a little finicky when you add
multiple channels let's ensure that Conda Forge is used if the package is
available with this command
let's verify the configuration Church of mercy
finally let's install some packages Kondo will only look in the condo forge
channel unless a package is missing and then turn to the defaults personally a
minimum data science environment has numpy psych pi panda scikit-learn and
matplotlib along with the newest stable version of python it will also have
jupiter notebooks available professor meatball screen might seem a
little faster than yours but does because he's running on 8 gigaBark RAM.
And you're done.
as promised you have now started on the tricky path to data
science thanks to be professor meatball and you did it to the proper Data Leap
way - Join us next time for more tricks more hijinks and proper python etiquette
stay tuned stay curious and good bye
hey if this was helpful leave a comment for professor meatball so that he can
have office hours and take care of those questions for you to share this leave a
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we'll have content like this every week something funny and something
educational for now we'll see you next time peace