Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.
Raw and Processed Data
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Del curso dictado por Johns Hopkins University
Getting and Cleaning Data
5699 calificaciones
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Week 1
In this first week of the course, we look at finding data and reading different file types.
Conoce a los instructores
Jeff Leek, PhDAssociate Professor, Biostatistics
Bloomberg School of Public Health
Roger D. Peng, PhDAssociate Professor, Biostatistics
Bloomberg School of Public Health
Brian Caffo, PhDProfessor, Biostatistics
Bloomberg School of Public Health
This lecture is about raw and processed data.
So I'm gonna be talking a little bit about how raw data may
be different depending on who you're talking to.
So some people might consider one version of the data raw, and
some people might consider another version of the data raw.
So first I was gonna talk about the definition of data.
So as we saw in the data scientist toolbox, data are values of qualitative or
quantitative variables, belonging to a set of items.
So the set of items might be the population or
the set of objects that you might be interested in.
And the values of course quantitative variables and
the variables are the things that your measuring.
And you might measure them in qualitative terms or in quantitative terms.
So usually when we think about variables we think about things like this.
If you look down here at the bottom.
We think about country of origin, sex, treatment, and
quantitative variables like height, weight, blood pressure.
A lot of these measurements are actually derived
from much lower level measurements.
So for example, if you think about blood pressure,
blood pressure's actually measured by calculating a pressure measurement.
And so there's actually a lot of low level things that go
into calculating that pressure measurement.
And so those low level things are the kinds of things
that we're going to be talking about in raw versus processed data.
So the raw data are the original source of data,
they're often very hard to use for data analyses because they're complicated or
they're hard to parse or they're very hard to analyze.
Data analysis actually includes the processing or the cleaning of the data.
If you go and obtain a dataset that's actually a raw image file and you process
it, and turn it into a nice data frame that then you use to analyze an ARM.
That data processing actually is part of the data analysis,
your data science pipeline.
In fact a huge component of a data scientist's job is performing those sorts
of processing operations.
The raw data may only need to be processed once, but regardless of how often you
process it, you need to keep a record of all the different things you did.
Because it can have a major impact on the data stream analysis.
The processed data is data that is ready for analysis.
So the processing of the data might include merging, subsetting, transforming,
or you might go into a file and extract out a part of an image.
You might go into a file and
extract out a little bit of text from a preformed text field.
Or you may do a number of other things.
Depending on the field that you work in, there may be standards for processing.
For example, in the area where I work, genomics,
there are a lot of really standard preprocessing techniques that need to be
applied before you can analyze data.
A critical, critical component is that all steps should be recorded.
I can't state this strongly enough.
Preprocessing often ends up being the most important component of a data analysis in
terms of affect on the downstream data.
So paying attention to all the steps that you did is critically important if you're
going to be a data scientist who's careful about understanding what's really
happening in the entire data processing pipeline.
So, I'm gonna give you one really quick example of a processing pipeline just to
illustrate what I mean by there being different levels of raw data.
So, this an Illumina HiSeq machine,
so what this machine can be used to do is to sequence DNA.
And so that sequencing is much, much faster now than it used to be in the past.
When the original Human Genome Project got started, it took almost a decade and
over a $1 billion to sequence one human genome.
And that same process can now be performed in about a week for
about $10,000 using a machine like this.
So it's an example of how data are becoming more and more cheap and
easier to collect.
And so the way that this machine works in a very, very rough overview is,
you end up with, you can imagine how you could start with fragments of DNA.
So it starts with little fragments of DNA which are bound to a slide.
So each fragment might be 500 letters long,
so you can think of your DNA as a string of 3 billion letters.
And so you take a small chunk of that, 500 letters, and bind it to this slide.
And then there's a chemical process by which multiple
copies of that same sequence are made.
And so what ends up happening is
this process is performed through sequencing by synthesis.
And so what happens is the complementary base or the complementary letter to each
letter in the sequence that's attached to the slide is attached one at a time.
And each different letter A, C, T, and G get a different color.
So what happens is, for each little cluster,
you get a color at every single new nucleotide that's synthesized.
And so those colors create a series of images.
And so, for example,
when you're synthesizing the first nucleotide you might get this image.
Then this image at the second one and this image at the third one and so forth.
If you zoom in on one specific little dot, that corresponds to the sequence of
exactly one of these little clusters of sequences that are exactly the same.
And so what you can do is you can follow along from image to image,
you can see what the color is in that image, and in that image, and
in that image, and in that image.
And what you end up with is, for each image, the color corresponding to each
letter, whichever one is the brightest is the one that you assign to that sequence.
So for example, in the very first letter for this particular fragment is
going to be a C, because you can see that of these four letters right here,
the C is actually the highest.
And then in the second nucleotide, you can actually see
that the highest letter out of these four, or the most bright letter, is the G.
And so the next letter that we'd assign would be a G and so forth.
So, the final thing that you end up with is something like this FASTQ
file that I've shown a few times in the class.
So the FASTQ file is a text file where, for
each of these little fragments that you've got on the plate,
you actually see a specific set of letters, As, Cs, Ts and Gs.
So you can think about the raw data being in several different steps.
So the raw data might be these image files down here, so you have to process those
image files in order to get these profiles here for each different fragment.
And then after you have the profiles you have to process those in order
to make predictions about which letters should go into the sequences
that actually end up right here.
So any of these stages could be considered raw and
in each of these stages there are a number of computational steps that could have
major impact that must be applied.
And so one thing to keep in mind when you're doing data analysis is, frequently,
you might end up analyzing these so called reads, that come off of this machine.
Or you might even analyze something that's downstream,
say some counts based on adding up some of those reads.
When you do that,
you're glossing over the fact that all of these processing steps happen beforehand.
And so those processing steps can have a major impact.
And part of this course is sort of understanding what those processing
steps are so you can make sure that your analysis isn't being driven by
artifacts caused by the way that you went from the raw data to the tidy data.
So that's what getting data is all about, is taking raw data and
turning it into processed data.
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