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
The Structure of a Data Science Project
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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 discusses the basic structure of a data science project.
A typical data science project will be structured in a few different phases
that I'll talk about in separately in this lecture.
So there's roughly five different phases that we can think about
in a data science project.
The first phase is the most important phase,
and that's the phase where you ask the question and
you specify what is it that you're interested in learning from data.
Now, specifying the question and kind of refining it over time is really
important because it will ultimately guide the data that you obtain and
the type of analysis that you do.
Part of specifying the question is also determining the type of question that
you are gonna be asking.
There are roughly six types of questions that you can
ask going from kind of descriptive, to exploratory, to inferential,
to causal, to prediction, predictive and mechanistic.
And so figuring out what type of question you're asking and
what exactly the question is, is really influential.
And so you should spend a lot of time thinking about this.
Once you've kind of figured out what your question is, but
typically you'll get some data.
Now, either you'll have the data or you'll have to go out and get it somewhere or
maybe someone will provide it to you, but the data will come to you.
And then the next phase will be exploratory data analysis.
So this is the second part, there are two main goals to exploratory data analysis.
The first is you wanna know if the data that you have is suitable for
answering the question that you have.
Then so this will depend on a variety of factors depending on very basic things
like is there enough data, are there too many missing values, things like that.
To more fundamental ones, like are you missing certain variables or
do you need to collect more data to get those variables, etc?
The second goal of exploratory data analysis is
to start to develop a sketch of the solution.
And so if the data are appropriate for
answering your question, you can start using it to kinda sketch
out what the answer might be to get a sense of kinda what it'll look like.
This can be done without any formal modeling or any kind of the statistical
testing of things like that just to get a good picture of what it might be.
The next stage, the third stage, is formal modeling.
So if you're sketch kind of works out,
you've got the right data and it seems appropriate to move on,
the formal modeling phase is the way to kind of specifically write
down what questions you're asking, what parameters you're trying to estimate.
And it also provides a framework for challenging your results.
So just because you've come up with an answer in the exploratory data analysis
phase doesn't mean that it's necessarily going to be the right answer and
you need to be able to challenge your results through a variety of
approaches where the sensitivity analysis are other types of analysis.
So challenging your model and just developing a formal framework is really
important to making sure that you can develop robust evidence for
answering your question.
The next phase is interpretation so once you've done your analysis your
formal modeling you wanna think about how to interpret your results and
there are a variety of things to think about in the interpretation phase
the data science project.
The first is kinda like think about how your results jive with kinda what
you expected to find when you where first asking the question.
And also you wanna think about the kind the totality of the evidence
that you've developed.
At this point, you've probably done many different analysis,
you probably fit in many different models.
And so you have many different bits of information to think about and
part of the interpretation phase is to kind of
assemble all that information to weigh the different pieces of evidence.
So that you know what kind or
which are more reliable, which are more important than others and to get a sense
of the totality of evidence with respect to kind of answering the question.
The last phase is the communication phase.
Any data science project that is successful will wanna
communicate its findings to some sort of audience.
Now that audience may be internal to your organization, it may be external,
it may be to a large audience or even just a few people.
But communicating your findings is an essential part of data science in it
because it informs the data analysis process and
a it translates your findings into action.
So that's the last part which is not a formal part of a data science project
necessarily, but often there will be some decision that needs to be made or
some action that needs to be taken.
And the data science project will have been conducted in support
of making a decision or taking an action.
So that last phase will depend on more than just the results of the data size or
the data analysis, but may require
inputs from many different parts of an organization or from other stakeholders.
So ultimately if the decision is made,
the data analysis that was done will inform that decision and will support and
the evidence that was collected will support that decision.
So these are roughly the five phases of a data science project.
There's the question, there's exploratory data analysis, there's formal modeling,
and there's interpretation, and there's communication.
Now, there is another approach that can be taken,
it's very often taken in data science project.
And that is to really start with the data and
to start with an exploratory data analysis.
So often there will be a data set available, But,
it won't be immediately clear kind of what the data set will be useful for.
So it can be useful to kind of do some exploratory data analysis, to look at
the data, to summarize it a little bit, make some plots, and see what's there.
And to generate some interesting questions based on the data.
So this is sometimes called hypothesis generating because it kind of produces
questions that were already there.
Once you've produced the questions that you wanna ask,
based on your initial kind of exploratory data analysis,
it may be useful to kind of get more data or other data
to kind of do an exploratory data analysis that's specific to your question now.
And then continue with the formal modeling, interpretation and
communication.
One thing that you have to be wary of is to do the exploratory data analysis in one
data set, develop the question, and then go back to the same data set.
And pretend like you hadn't done the exploratory data analysis before and
come at it with say a fresh question.
That goes on to the rest of the stages.
This could often be a recipe for kind of, for bias in your analysis.
Because the results were derived from the same data set.
So it's important to be careful about doing that and to try to obtain other data
when you're using the data to generate the questions in the first place.
So this is the secondary approach to data science that can be very useful and can
often result in many interesting questions that are generated from the data.
Data science projects have a variety of phases and it's important to kind of
understand which phase you're in so that you know kind of how to progress and
how to move forward with any data science project.
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