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What is Data?

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The Data Scientist’s Toolbox

Johns Hopkins University

4.5 (20,144 ratings) | 340K Students Enrolled

Course 1 of 10 in the Data Science Specialization

In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.

Skills You'll Learn

Data Science, Github, R Programming, Rstudio

Reviews

4.5 (20,144 ratings)

5 stars
13,181 ratings
4 stars
5,179 ratings
3 stars
1,348 ratings
2 stars
281 ratings
1 star
155 ratings

AI

Apr 24, 2018

This course was a good intro especially in setting all the necessary software for future courses. I suggest to read the manuals, books and other readings the profs suggest. The resources are helpful.

WC

Sep 26, 2017

I really don't know much about this stuff, I think the jury's still out on whether the last four weeks will be helpful in the future. We'll see how much I think I've learned at the end of the course

From the lesson

Week 3: Conceptual Issues

The Week 3 lectures focus on conceptual issues behind study design and turning data into knowledge. If you have trouble or want to explore issues in more depth, please seek out answers on the forums. They are a great resource! If you happen to be a superstar who already gets it, please take the time to help your classmates by answering their questions as well. This is one of the best ways to practice using and explaining your skills to others. These are two of the key characteristics of excellent data scientists.

Types of Questions9:09

What is Data?5:15

What About Big Data?4:15

Experimental Design15:59

Taught By

Jeff Leek, PhD
Associate Professor, Biostatistics
Roger D. Peng, PhD
Associate Professor, Biostatistics
Brian Caffo, PhD
Professor, Biostatistics

Transcript

Since you're in a data science track, it might

be a good idea to figure out what is data.

So, for a definition of data we're going to go

to the number one source of all information, Wikipedia.

And, we're going to get the definition, data are values of

qualitative or quantitative variables, belonging to a set of items.

So, the first thing that you can see from

this definition is that you need a set of items

to be measuring things on and so, the set

of items is sometimes called the population, in statistical inference.

It's basically, what you're trying to discover something about, so,

it might be the set of all websites or it might

be the set of all people coming to websites, or it

might be, a set of all people getting a particular drug.

But in general, it's a set of things that you're going to make measurements on.

Variables are measurements or characteristics of an item.

So, they might be measurements as in, you

measure the height of a person, or you measure

the amount of time a person stays on

a website, or they might be more qualitative characteristics.

So it might be the places that the person looks on the website

or Whether that you think the person visiting is a man or a woman.

And so you, have both qualitative and quantitative variables.

So qualitative variables are things like country of origin, sex, or treatment.

They're not necessarily ordered, and they're

not necessarily measurements in that way.

Quantitative measurements on the other hand are usually Measured on

a continuous scale, they're things like height, weight and blood pressure.

And, they usually have an ordering on that scale.

So, what do data look like?

So, you might think in your mind, when I say data, it's something like a big excel

table, or something like that, but, actually, most

data actually starts off in a very raw form.

So, this is an example of a fast queue file, which is

a type of file that's produced by a next generation sequencing machine.

So, a typical experiment will produce hundreds of millions

of lines of text file that look like this.

And so, you can actually see here in this file, you can actually

see a sequence here, of a particular read that comes from a person's genome.

And so, what you want to be able to do is parse that

file, and collect those data, and maybe infer something about their genome.

Another way the data might look is an API.

So this is actually a picture of the Twitt, Twitter API website.

And so, what you might do is you might try to get a particular URL.

So you might try to access that URL and get information from it.

And what you would get back is something like

this, which is a structured form of data, that isn't

necessarily something you would see in Excel cloud, but it's,

a structured form of data that you might get back.

Here's an example of a medical record.

So, this is again another form of data.

People are very interested in studying medical records,

and trying to understand how people can either

improve the way that we insure people or

improve the way that we give people medical care.

And so, these data again are text files that,

are not necessarily formatted in a very nice way.

So what you might want to be able to do is

Do things like extract the allergy name from this text file.

Or subtract or extract with different things were ordered and so

forth, and then use that data to maybe answer some questions.

Data might also be a video, so in

this particular case machine learning experts developed an

algorithm that could learn whether a video was a cat, or a video was something else.

It seems like kind of a trivial application.

It's actually quite a hard problem And they solved.

But in this particular case, the data were actually the videos themselves.

They're stills from the video themselves.

Data might also be an audio file.

So this is an example, DarwinTunes, where

people actually study the evolution of music over

time, where people decided whether, innovations introduced

into the audio file were interesting or not.

So, overtime they evolved music that was more

melodious and more interesting for people to listen to.

So, the data itself was actually the audio files in this study.

Data might also look like access to files, whether

through an API or through spreadsheets through open government websites.

So, for example, data.gov has a lot of data sets that might

be accessible, and those data sites might be in any number of

formats, from very simple to tab or comma separated files, to excel

files, to something that's much more complicated and messy like RAW text files.

Rarely do data look exactly like what you'd expected to

see at, at the beginning of a data science project.

So, here's a very clear easy data set where

you've got variables and columns, and observations in rows.

And it seems like it's very easy to analyze.

It's very rare that the data come that

easily processed before the beginning of a study.

So, the data are actually the second most important thing in data science.

The most important thing, in in data science

is actually the question you're trying to answer.

So, the data should follow that question.

It's the second most important thing to the question.

Often the data will limit, or enable the questions you're trying to ask, so

in other words, you start with the question, and you might not have the

data to be able to answer that question, so you have to modify the

question, to be able to answer, sort of a sub-question or a related question.

But having data in general, can't save you

if you don't have a question that you're asking.

And this is [INAUDIBLE] of the key take home message,

maybe the theme of this data scientist toolbox, is focusing on

having a question that you want to answer with your data

and not being driven by the fact that you have data.

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