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In this video, we will discuss what is meant by cleaning or scrubbing a dataset, and why it’s an important step in data analysis.
New Terms:
- Data Cleaning -- The process of fixing or removing incorrect, incomplete, and irrelevant data from a dataset. Also called data cleansing, preparing, or scrubbing.
- Example -- A single observation, case, or member of a dataset, usually a row in a table.
- Feature -- A descriptive or measurable characteristic of an example in a dataset, usually a column in a table. Also called a variable.
- Raw Data -- Data that has been collected but not cleaned. Also called source, primary, or atomic data.
Data Sources:
data.world
Kaggle competition datasets
National Health and Nutrition Examination Survey
The Star Wars API (SWAPI)
Further Reading:
Why the ‘Boring’ Part of Data Science is Actually the Most Interesting
Data Science: A Kaggle Walkthrough Pt 1: Introduction
Data Science: A Kaggle Walkthrough Pt 2: Understanding the Data
Data Science: A Kaggle Walkthrough Pt 3: Cleaning Data
Tidy Data
Python Resources:
[MUSIC]
0:00
Hi, am Alyssa,
a computer programmer and data analyst.
0:09
Welcome to the Cleaning and
Preparing Data course.
0:13
There are a lot of resources available for
learning how to analyse data.
0:16
But most assume your dataset
has already been cleaned.
0:20
In this course you'll
get hands on experience
0:23
learning how to clean a dataset.
0:26
Also called data cleansing,
repairing, or scrubbing.
0:28
This is an, an important step to making
sure your data is relevant and accurate.
0:31
Data cleaning is typically
the biggest time commitment
0:36
in any data analysis project.
0:39
Often taking up as much as
80% of your time and effort.
0:41
As you go through this course,
we'll explore a practice dataset, and
0:45
discuss the problems we can
find during data scrubbing.
0:48
We'll talk about how to deal
with these problems, and
0:52
how to decide which data to include or
exclude from our dataset.
0:55
We'll use hands-on examples
with a real dataset so
0:59
you can practice your new skills and
get real-world experience.
1:02
By the end of this course, you should
be able to prepare your own dataset for
1:06
use in a data analysis project.
1:10
You can adjust the speed of each
video if you need to slow it down.
1:12
And feel free to pause a video so
you can practice what you just learned.
1:16
It's also a good idea to read
the teacher's notes for each video for
1:20
additional information and resources.
1:23
Remember to check the prerequisites for
this course.
1:26
These videos will assume you're
familiar with programming and
1:29
Python, as well as the NumPy and
Panda's libraries.
1:31
If you don't know Python, NumPy or
Pandas yet, check the teacher's notes for
1:35
links to courses that can teach
you what you need to know.
1:39
Now that we've covered the course
essentials, let's get started.
1:43
Data cleaning, preparing, or
scrubbing is the term for
1:46
fixing any issues in a dataset.
1:48
So that all data is present,
relevant, and correct.
1:51
You usually do this when
a dataset is first collected, or
1:54
when multiple sources of data
are merged into a new larger dataset.
1:57
It's also an ongoing process as you can
discover new problems with the data
2:01
at any point in your analysis.
2:05
A common way of arranging
data is in table format,
2:08
like you would see in a spreadsheet or
database query.
2:11
In this set up each row represents and
example or observation, and
2:14
each column represents a feature.
2:18
A feature is a descriptive or
measurable characteristic such as Length,
2:20
Crew capacity, or Lightspeed
capability of fictional vehicles.
2:25
Each examples contains all the feature
values related to a single unit
2:29
such as the Millennium Falcon.
2:33
Data preparation is a very important step
before trying to get any information
2:35
out of a dataset.
2:39
The results of your analysis can only be
as good as the quality of the data itself.
2:40
Any mistakes in the data will
cause mistakes in your results.
2:45
A common way of phrasing this
idea is garbage in, garbage out.
2:49
There are many sources of data
available on the Internet.
2:53
While some of these datasets
have already been cleaned.
2:56
You will likely need to create your own
dataset to meet the needs of your specific
2:59
problem and questions.
3:02
You may need to gather raw or
unclean data from various sources.
3:05
Or you may be given a dataset that
has been collected, but not cleaned.
3:09
Even if a cleaned data set is available,
3:14
it's often beneficial to look
at the raw data yourself.
3:17
And understand the steps taken
during the data cleaning process.
3:20
Cleaning a dataset involves many
decisions on how to deal with
3:24
problematic data entries.
3:27
And you may want to deal with them
differently than the person who originally
3:29
clean the dataset.
3:32
I've included resources for finding
data as well as further reading on data
3:34
cleaning in the teacher's notes for
this video.
3:38
The rest of this course we'll walk
you through the process of scrubbing
3:41
your practice dataset.
3:44
In our next video,
3:45
we'll outline the types of problems you
will find incorrect throughout the course.
3:47
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