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You have completed Intro to Data Analysis!
You have completed Intro to Data Analysis!
Preview
Walkthrough a small example of a typical workflow.
Terms
- Count - Counts the number of values in a column
- Mean - The average of a set of values
- Median - The middle value of a set of values
- Mode - The value that occurs the most frequently in a set of values
Data Analytics
- What is data analytics?
- A Day in the Life of a Data Analyst (2017)
- A Day in the Life of a Data Analyst (2022)
Thinking about spreadsheets?
Thinking about databases?
Thinking about Python?
- Python can also be used for creating websites, games, automating work, and more.
- Begining Python
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A data analyst follows a typical workflow,
regardless of the tools they use.
0:00
First, you need a question or
reason for doing your analysis.
0:06
Sometimes you may need to look at your
data first to come up with your question
0:12
and other times you may have
your question Then go searching.
0:17
Our question is, which author has
the most books in our library.
0:22
Then you need data.
0:28
Depending on the project or
where you are working, you may already
0:30
know the data you have access to, or
you may need to go out and find it.
0:35
I will walk through
a small example project
0:40
to show the analysis process
from beginning to end.
0:43
Don't worry about needing to know
the calculations or what I'm doing.
0:48
I just want to give you a quick
peek into this process.
0:52
I'm going to use the CSV file as my data.
It lists out all the books in our library.
0:56
Now I'm going to clean the data.
1:04
There's one row where most
of the data is missing.
1:07
So I will delete that row.
1:10
If this was my master spreadsheet, I would
want to make sure I was working on a copy,
1:14
so any changes I make
1:19
don't affect my dataset.
1:21
I could complete my analysis
in the spreadsheet, but
1:23
I will bring it into a Jupyter
Notebook to show that off a bit too.
1:26
My data is now imported.
1:40
and they need to find the author
with the most books in our library.
1:42
First I'm going to look at
my dataset's information.
1:47
This gives me a reference to go back
to when creating an analysis so
1:56
I can make sure my analysis makes sense.
2:01
For instance,
if I had 100 books listed here and
2:04
at the end of my analysis I
only showed data on 10 books,
2:08
that wouldn't really be a great
representation of the overall dataset.
2:13
I'm also going to look at the number
of unique authors in our dataset.
2:19
This will give us another check.
2:23
In our dataset,
we have 6,643 different authors.
2:34
Now let's see who has the most books.
2:42
When I run it, I see that Stephen King and
P.G. Wodehouse are tied for the most books.
2:59
Before presenting my findings, I can check
against my dataset. The length of my
3:07
result matches the number
of authors in our dataset.
3:12
So I think we can say my
answer is accurate.
3:19
Finally, I can present my findings to my team
or stakeholders. This may be a quick
3:23
email with a screenshot of my work or
a detailed report or presentation.
3:31
Rinse and repeat.
3:37
Once you complete the process a few times,
it will become second nature.
3:39
Try it out on your own with
some data you have, like,
3:45
what day does the family dog go out and
play the most?
3:49
Or which snack gets eaten
the fastest by your roommates?
3:52
Have fun with it.
3:56
If this all seems great,
then data analysis is for you.
3:57
If you're unsure,
see the Teacher's Notes below for
4:02
some more information to help you see
if data analysis is right for you.
4:05
If you're thinking,
I don't think this is for me, no worries.
4:10
There are many other ways to use
the tools that we've talked about.
4:15
I'll list them in the Teacher's Notes too,
so
4:20
you can find what matches your interests.
4:22
This workshop was just a small
glimpse of data analysis.
4:25
Thanks for hanging out.
4:29
See you later.
4:30
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