By now you have definitely heard about data science and big data. In this one-week class, we will provide a crash course in what these terms mean and how they play a role in successful organizations. This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff. We've designed this course to be as convenient as possible without sacrificing any of the essentials. This is a focused course designed to rapidly get you up to speed on the field of data science. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know. 1. How to describe the role data science plays in various contexts 2. How statistics, machine learning, and software engineering play a role in data science 3. How to describe the structure of a data science project 4. Know the key terms and tools used by data scientists 5. How to identify a successful and an unsuccessful data science project 3. The role of a data science manager Course cover image by r2hox. Creative Commons BY-SA: https://flic.kr/p/gdMuhT
Data Scientist Toolbox
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From the course by Johns Hopkins University
A Crash Course in Data Science
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From the lesson
A Crash Course in Data Science
This one-module course constitutes the first "week" of the Executive Data Science Specialization. This is an intensive introduction to what you need to know about data science itself. You'll learn important terminology and how successful organizations use data science.
Meet the Instructors
Jeff Leek, PhDAssociate Professor, Biostatistics
Bloomberg School of Public Health
Brian Caffo, PhDProfessor, Biostatistics
Bloomberg School of Public Health
Roger D. Peng, PhDAssociate Professor, Biostatistics
Bloomberg School of Public Health
This lecture is about the data scientist's toolbox.
So by the data scientist toolbox, I mean the collection of tools that are used
to store, process, analyze and communicate results of data science experiments.
And so the first thing that we're gonna talk about is where do you actually
store the data?
So data are typically stored in a database.
For a smaller organization that might be single, small MySQL database.
And for a large organization,
it might be a large distributed database across many servers.
And so I'm showing you here an example, the PostgreSQL database, but
there are a large number of other options.
The key take home message here is that in the database the data are are stored, and
backed up.
Usually, most of the analysis that takes place and most of the production doesn't
actually happen in the database, it happens elsewhere.
So, you usually have to pull it out with another programming language to
pull the data out of that database and analyze it.
So, the two most common languages for doing that.
The first one is R Programming Language.
So the R programming language is basically a statistical programming language that
allows you to pull data out of a database, analyze it, and
produce visualizations very quickly.
This forms the basis for our data science specialization.
The other major programming language that's used for
this type of analysis is Python.
So Python is another similar language that allows you to pull data out of databases,
analyze and manipulate it, visualize it.
And connected to downstream production.
So the other thing that you need to do to be able to use these languages and
pull them up out is actually some kind of servers or
some kind of computers that you can run those programming languages on.
So you have the database, which stores the data.
And then you have the servers which you will use to basically analyze the data.
And those servers will run the languages R or Python or and so forth.
Here I'm showing you an example, Amazon Web Services.
This is a set of computing resources that you can actually rent from Amazon.
And so many organizations that do data signs actually just directly rent
their computing resources.
Rather than buy and manage their own.
This is particularly true for small organizations that don't have a large IT
budget, so they would actually go out and just rent their competing resources,
that then they would be able to use to analyze data.
Once you've actually done some low-level analysis and maybe made some discoveries
or done some experiments, and decided actually how you're gonna use data to
make decisions for your organization you might want to scale those up.
So there's a large number of sort of analysis tools that can be used to
provide more scalables analysis of data sets, whether that's in a database or
by pulling the data out of the database.
So two of the most popular right now are the Hadoop framework and
the Spark framework.
And both of these are basically ways to analyze, at a very large scale, data sets.
Now you can do interactive analysis with both of these, particularly with Spark.
But it's a little bit more complicated.
It's a little bit more expensive,
especially if you're applying it to large sets of data.
So it's very typical in the data science process to take the database,
pull out a small sub sample of the data, process it and analyse it in R or
Python and then go back to the engineering team and scale it back up with Hadoop, or
Spark, or other tools like that.
So the next tool that
is in the data scientist toolbox is actually communication.
A data scientist or a data engineer has a job that's typically
changing quite rapidly as new packages and new sort of tools become available, and so
the quickest way to keep them up to speed is to have quick communication and
to have an open channel of communication.
So a lot of data science teams use tools like Slack to communicate with each other,
to basically be able to post new results, new ideas.
Be able to communicate about what the latest packages are available.
And then data scientists are frequently going to the internet to search for
how do I this really, what seems like a very simple task,
how do I match IDs together from two different log files.
That might not be something that they know how to do, but there are a large number of
sort of help websites like Stack Overflow, that allow people go out and
sort of search for the questions that they need to answer.
Even if there quite technical, and quite detailing, get answers relatively quickly.
And that allows people to sort of keep the process moving,
even though the technology is changing quite rapidly.
Once the analysis is done, and complete, and
you sort of want to share it with other people in your organization,
you need to do that with sort of reproducible or literate documentation.
What does that mean?
It basically means a way to integrate the analysis code and
the figures and the plots that have been created by the data scientist
with sort of plain text that can be used to explain what's going on.
One example is the R Markdown framework.
Another example is iPython notebooks.
These are ways to sort of make your analysis reproducible and
make it possible that if one data scientist runs an analysis and
they want to hand it off to someone else, they'll be able to easily do that.
You also need to be able to visualize often the results of the data
science experiment.
So the end product is often some kind of data visualization or
interactive data experience.
And there are a large number of tools that are available to build those sorts of
interactive experiences and visualizations.
Because at the end user of a data science product is very often
not a data scientist themselves.
It's often a manager or an executive who needs to handle that data
understand what's happening with it and make a decision.
So, there's a large number of tools.
I'm showing one here, Shiny,
which is a way to build quickly data products that you can share
with people who don't necessarily have a lot of data science experience.
And then finally, most of the time when you do a science data experiment,
you don't do it in isolation.
You wanna communicate your results to other people.
So, people frequently make data presentations whether that's the data
science manager, the data engineer, or the data scientist themselves, that actually
explains, how did they actually perform that data science experiment.
What are the techniques that they use, what are the caveats, and
how can it be applied to the data to make a decision like you'd like to?
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