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Del curso dictado por Johns Hopkins University

Data Science in Real Life

1384 calificaciones

Johns Hopkins University

Data Science in Real Life

1384 calificaciones

Curso 4 de 5 en SpecializationExecutive Data Science

Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real data analyses? In this one-week course, we contrast the ideal with what happens in real life. By contrasting the ideal, you will learn key concepts that will help you manage real life analyses. This is a focused course designed to rapidly get you up to speed on doing data science in real life. 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 how to: 1, Describe the “perfect” data science experience 2. Identify strengths and weaknesses in experimental designs 3. Describe possible pitfalls when pulling / assembling data and learn solutions for managing data pulls. 4. Challenge statistical modeling assumptions and drive feedback to data analysts 5. Describe common pitfalls in communicating data analyses 6. Get a glimpse into a day in the life of a data analysis manager. The course will be taught at a conceptual level for active managers of data scientists and statisticians. Some key concepts being discussed include: 1. Experimental design, randomization, A/B testing 2. Causal inference, counterfactuals, 3. Strategies for managing data quality. 4. Bias and confounding 5. Contrasting machine learning versus classical statistical inference Course promo: https://www.youtube.com/watch?v=9BIYmw5wnBI Course cover image by Jonathan Gross. Creative Commons BY-ND https://flic.kr/p/q1vudb

De la lección

Introduction, the perfect data science experience

This course is one module, intended to be taken in one week. Please do the course roughly in the order presented. Each lecture has reading and videos. Except for the introductory lecture, every lecture has a 5 question quiz; get 4 out of 5 or better on the quiz.

Report writing8:34

Version control4:58

Conoce a los instructores

Brian Caffo, PhD
Professor, Biostatistics
Bloomberg School of Public Health
Jeff Leek, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health
Roger D. Peng, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health

Hi, my name is Brian Caffo and this is a brief lecture on version control.

Remember the theme of this course is contrasting

what happens in real life versus the ideal data analysis.

I think version control is one of those instances where it is exactly the ideal

for data analysis, but it's a fairly easily attainable ideal as opposed to

many of the other topics in this class where it's very hard to obtain it.

This version control just adds a little bit of work, but

often saves you a lot of time.

So now, just to return to this quote, When you write code,

keep in mind that you're collaborating with your future self.

And the ability to look at your code and

go back to previous versions and see the history of your

process is an incredibly important part of data analysis.

So we've talked in our report writing section,

we talked about how important it is to create reproducible reports.

That's certainly one component of having a very well done and documented data

analysis but keeping all your versioning of your code as you go is another one.

Not only does it help you for back up and archival purposes,

but it also helps keep a log of exactly what you did, and for

data and analytic purposes.

So, in general, version control is just good practice.

And, unlike a lot of the other topics in this class, it's pretty easy and

it's directly under your control.

Having said that, I'll take this moment to complain about Roger Pang.

And, so I've been using version control for a long time.

I think the earliest version control I used was RCS,

a fairly old version of version control.

And then CVS, which was another, older version of version control.

And then Roger Peng at one point came into my office and said,

everyone is using this program called subversion, and you've got to switch.

No one's using CVS anymore.

And so, I resisted for a little bit.

And then I said, you know, Roger's almost always right about computing things.

I'm going to switch.

Switch to subversion.

Which was a monumental effort changing all my CVS repositories to Subversion

repositories.

And then the next day, Roger comes into my office and says, everyone's using Git.

You gotta quit using Subversion.

So, I resisted then for

another long period of time to using Git and stuck with Subversion.

But once I finally switched to git, I was very happy about it.

And I've noticed that I've been using Git for

a while and Git seems to be here to stay.

It's not gonna get supplanted by the next version control system.

At least it doesn't appear to be so any time soon.

At any rate, for the people that you're managing, you should be

inspiring them to either use Git, or Subversion,

or Mercurial or some of these kinds of tools for

their analyses, some of these version control tools.

So just to orient you to the software I think

if I'm going to make a recommendation I'm gonna suggest Git.

And so Git is the versioning version control software.

It is what helps you keep a complete history

of your code and your analysis files, and if you want, even your data files.

And so on as you go.

So you can go backwards to any point in time or

go forwards if you've gone back again.

Just at no point is anything that gets checked into your Git repository does it

get lost.

So Git is the tool for your version control and

then you need a place to host it.

Now you can do this yourself.

If your in a company presumably you have version control systems in place.

If you're at a smaller company,

you might want to look at one of these get hosting services.

Some of the most popular ones, at least the two that I'm most familiar with

are GitHub which I think is clearly the largest, and then Bitbucket.

So these are not version control tools themselves,

these are hosting places where you are pushing your code and files up.

This is, in essence, hosting the Git software for you, and

then keeping track of your files.

And so they're wonderful tools and I would highly suggest this as using Git and

using version control as a part of best practices.

And as part of your management strategy encouraging the use or

maybe requiring the use of these tools.

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