SPEAKER: Whether you're organizing your personal data
for your own use or organizing project data for work,
there are certain procedures you want
to follow to make sure your data is easy to find and use.
In this video, we'll cover some best organization practices
and also check out some different ways
project data can be organized.
There are plenty of best practices
you can use when organizing data, including
naming conventions, foldering, and archiving older files.
We've talked about file naming before, which is also
known as naming conventions.
These are consistent guidelines that
describe the content, date, or version of a file in its name.
Basically, this means you want to use logical and descriptive
names for your files to make them easier to find and use.
And speaking of easily finding things, organizing your files
into folders helps keep project-related files together
in one place.
This is called foldering.
For example, all of the files related to your vacation plan
might go in the Vacation 2025 folder.
You might then break that folder down even further
by creating subfolders like Itinerary or Photos,
depending on what else you'd like to easily access.
It can also be useful to move old projects
to a separate location to create an archive
and cut down on clutter.
It's so much easier to find and use my files
when I name them something meaningful and searchable
and when I organize them into folders.
It makes all my data more accessible and useful.
In addition to these three best practices,
there are two more things you'll want to consider when
organizing data for work use.
First, the project data you'll be using for work
could be accessed and used by multiple people.
So it's important to align your naming and storage
practices with your team to avoid any confusion.
Your team might also develop metadata practices,
like creating a file that outlines project naming
conventions for easy reference.
We'll get to talk more about naming conventions for work
files in more detail later.
Secondly, you want to think about how often you're
making copies of data and storing it in different places,
most importantly because if data is stored in lots
of different databases or spreadsheets,
it can contradict itself and lead to mistakes later on.
Also, storing data in multiple places takes up a lot of space.
Relational databases can help you avoid data duplication
and store your data more efficiently.
You can use these practices to organize data in different ways
according to your project.
So let's look at some examples of data organization.
I have some sample project folders here,
each organized in a slightly different way.
Let's open them up and see what they look like.
We'll start with the high-level Finances folder.
The Finances folder has been organized categorically.
There are subfolders like Budget, Invoices, and Payroll
that represent different categories.
Let's click on Invoices to see what's in there.
So in the Invoices folder, you can
see that we have another set of subfolders labeled by year--
2014, 2015.
Yep, looks like these are in chronological order.
Sometimes, the way files are organized
can tell us how the data within those files is also organized.
Let's open a file to see if that's right.
So in the 2014 subfolder, there's
a file with invoices from June.
If we open it, we can see that they've been organized
by date, just like the folders.
There's different ways to organize data, depending
on what you need it for.
The categorical organization of the subfolders in Finances
made it easy for me to go straight to the invoices.
But the chronological organization
of the Invoices subfolder can help
us find financial data from the exact date we're looking for.
There's other ways to organize data too--
in order of importance or even by location.
For example, a company might use hierarchical organization
so that employee data mirrors the structure of their employee
organization.
Or a company working with geographical data
might choose to organize by location.
It's a good idea to take time early on in a project
to consider what the best organization methods will
be for you and your team to stick to.
Here's another way to think about it.
Unorganized data is like a messy room.
It's overwhelming, hard to find anything in,
and gets worse the longer you avoid cleaning it up.
But by making sure early on you know where to put your files,
you can keep your work data organized, easy to use,
and error free.
Now that you see how important it
is to keep data organized for both personal and work use,
we'll take a closer look at file naming conventions
and how they carry over into your databases.
[MUSIC PLAYING]
So you've heard me mention the idea
of using meaningful and logical file names
to help organize your data.
But using consistent file names can also streamline or even
automate your analysis process, saving you time and energy
in the long run.
When you use consistent guidelines that
describe the content, date, or version of a file in its name,
you're using file naming conventions.
And as we've already discovered, these
file naming conventions help us organize, access, process,
and analyze our data.
So here are some general tips on creating
file naming conventions that are both logical and functional.
Here are some quick file-naming dos.
Work out your conventions early to avoid having
to spend time redoing it later.
Align your file naming with your team.
And make sure your file names are meaningful,
with references to the project name, creation date, revision
version, or any other useful information
needed to understand what's in that file.
Now, there's some other simple things
you can do to make sure your file naming conventions are
on point.
First of all, you want to keep your file
names short and sweet.
They're supposed to be quick reference points that
tell you what's in a file.
From earlier videos, we know that we
want to include dates and revision numbers in our file
names.
I recommend formatting it by year, month,
and day because that follows the international date standard.
Different countries have different date conventions,
so keep that in mind.
And when you include revision numbers in a file name,
lead with a zero so that if you run
into double digits of revisions, it's already
built into your conventions.
And another good rule is to use hyphens, underscores,
or capitalized letters instead of using spaces.
Spaces and special characters might not
be recognized by your software.
Plus, avoiding spaces definitely makes it easier to work in SQL.
And my last bit of advice--
create a text file that lays out all your naming conventions
on a project.
This is really helpful if someone new joins your team
or if you just need a quick reminder while you're
working on something.
We talked about this earlier when we covered metadata,
which is data about data.
It helps explain what data there is
and how it's being organized.
When you use consistent, meaningful file naming
conventions throughout your project,
your data will be easy to find and use.
And you can save yourself time too.
Up next, we'll keep looking at spreadsheets,
and we'll talk about security features
and how you can use them to protect your data now
that it's organized.
See you there.
[MUSIC PLAYING]
OK.
Now that our data's organized and easy to find,
it's time to start thinking about how to protect it.
The good news is that spreadsheets
come with security features already built in.
In this video, we'll look at different spreadsheet programs
and how their security features, like sheet protections
and access control, are similar.
When I say "security features," you
might be imagining ways to protect data from other people.
But that's just one kind of security.
Security features can be designed
to keep unauthorized users from viewing certain files
or just lock your worksheets so that you don't accidentally
break your formulas.
This is called data security.
Data security means protecting data
from unauthorized access or corruption
by adopting safety measures.
Whatever spreadsheet program you're using
will have similar security measures built in.
As the data analyst, you'll run into Google Sheets and Excel
a lot.
So let's talk about what they have in common.
First, both programs have features
that let you protect your spreadsheets
or parts of your spreadsheets from being edited,
from the entire worksheet down to single cells in a table.
So if you're collaborating with other users,
you can easily lock down your formulas
so that they aren't accidentally broken.
Speaking of collaborating, Excel and Google Sheets
both have access control features,
like password protection and user permissions.
This gives you more control over who can do what
to your spreadsheet.
Because these programs are located in different places,
these features are slightly different.
For Excel spreadsheets, you can encrypt files and worksheets
with passwords before emailing them to other users.
In Google Sheets, these settings are
found under the Sharing menu, which
allows you to control who can see or edit the sheet online.
Google Sheets can also be copied so
that users can work with that data
without altering the original.
Tabs can also be hidden and unhidden in Sheets and Excel,
allowing you to change what data is being viewed.
But remember, even hidden tabs can
be unhidden by someone else.
So be sure you're OK with those tabs still being accessible.
As a data analyst, data security will be a priority.
But no matter which program you use to create spreadsheets,
there are security features to help you
keep your work safe and secure.
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