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Data Engineer: Qualifications & Skills

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Building a Data Science Team

Universidad Johns Hopkins

4.5 (2,422 calificaciones) | 27K estudiantes inscritos

Curso 2 de 5 en Executive Data Science Programa Especializado

Data science is a team sport. As a data science executive it is your job to recruit, organize, and manage the team to success. In this one-week course, we will cover how you can find the right people to fill out your data science team, how to organize them to give them the best chance to feel empowered and successful, and how to manage your team as it grows. This is a focused course designed to rapidly get you up to speed on the process of building and managing a data science team. 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. The different roles in the data science team including data scientist and data engineer 2. How the data science team relates to other teams in an organization 3. What are the expected qualifications of different data science team members 4. Relevant questions for interviewing data scientists 5. How to manage the onboarding process for the team 6. How to guide data science teams to success 7. How to encourage and empower data science teams Commitment: 1 week of study, 4-6 hours Course cover image by JaredZammit. Creative Commons BY-SA. https://flic.kr/p/5vuWZz

Destrezas que aprenderás

Team Building, Data Science, Management, Team Management

Revisiones

4.5 (2,422 calificaciones)

5 stars
1,468 ratings
4 stars
713 ratings
3 stars
179 ratings
2 stars
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1 star
22 ratings

BB

Mar 31, 2019

Well thought and constructed instructional materials. Easy to understand and more practical. I actually struggled with the quizzes unlike the First Course in this executive program.

O

Nov 08, 2016

The mode of teaching was interesting and I hope you can do better in the area of providing answers to questions we missed after the course. Thanks for the opportunity given to me.

De la lección

Building a Data Science Team

Welcome to Building a Data Science Team! This course is one module, intended to be taken in one week. the course works best if you follow along with the material in the order it is presented. Each lecture consists of videos and reading materials and every lecture has a 5 question quiz. You need to get 4 out of 5 or better on the quiz to pass. Overall the quizzes are worth 17% of your grade each, with the exception of the last quiz, which is worth 15%. I'm excited to have you in the class and look forward to your contributions to the learning community. Click Discussions to see forums where you can discuss the course material with fellow students taking the class. Be sure to introduce yourself to everyone in the Meet and Greet forum.If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems with the Coursera platform, visit the Learner Help Center.Good luck as you get started, and I hope you enjoy the course! -Jeff

Data Engineer: Qualifications & Skills5:51

Data Scientist: Qualifications & Skills8:28

Data Science Manager: Qualifications & Skills4:41

Impartido por:

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

Transcripción

One of the key members of a data science team is a data engineer.

And so I'm gonna talk a little bit about what are the qualifications and

skills that you might need in a data engineer.

So what does a data engineer do?

They might do things like build infrastructure.

So they would build out what are your databases, the hardware for that.

Whether it's purchasing that, and organizing it, and ordering it.

Or they might build out your system for actually computing on that,

whether it's what servers they're gonna buy and how they're gonna organize that.

And what software they're gonna run on top of the database,

what software they're gonna run on top of the server.

And then, they manage the data storage and use.

So they might monitor how those work.

They might monitor who, what people are using which data.

They might pull data out and give it to somebody.

And then they might implement production tools.

Now each of these is a different thing, and so

you might not have one person that does all of these things.

Certainly at the beginning, when you're a very small organization,

you might need one person that can kind of handle the entire data infrastructure.

And then you might get more specialized people as you get to be a larger and

larger organization.

So the question is, what skills do they need?

So they might need some knowledge of what's the right hardware to be looking

for, both in terms of storage and in terms of computing.

They might need to have some knowledge about databases software.

They might need to know a little bit about software for computing and

what are the different software needs that your organization might need.

In that sense, they need to be able to interact with the data scientists,

once you have them.

They need to be able to know what software they need to be able to install,

what kind of data pulls that people will frequently do.

That will all inform users of hardware choices that they're going to be making.

And so, they also need to understand data processing at scale, almost always now,

and if you're a data driven organization,

you're collecting a massive amount amount of data.

So you need to be able to run at scale those data processes and

those data prediction algorithms that you've developed.

And then they need to understand software engineering at the level of,

they need to know how they're gonna interact with all these other components

of the data science team.

And so the background for data engineers is often computer science and

computer engineering, but they could also come from other places.

They might come from a quantitative background with some computer science

experience that they picked up maybe in online courses or in courses in person.

Or maybe they come from information technology where they've actually been

involved in infrastructure building and so forth.

Again, sort of like with the data scientist,

you might depend on what your organization needs a little bit,

which of these specific backgrounds is most useful to you.

But the primary thing you're looking for

is, can they execute those jobs that your organization needs them to execute?

Are they able to build infrastructure?

Are they able to maintain the infrastructure that you need them to

be able to maintain?

Can they implement the data science,

or machine learning algorithms, or statistical experiments, at scale for

your organization in the way that you would like them to.

So again,

the key is to get them to be able to solve the problems that you need now.

With a data engineer, the balance of solutions versus software might be

a little bit different then a data scientist.

Since this person is the person that's going to sort of be maintaining the data

stack for you, you need them to be able to do that in a way that's consistent with

the way that your organization does things.

So they need to be able to have an idea about what are the specific software and

hardware needs that your organization has.

There are a few sort of key things that they need to know, they might need to know

how to build and manage some databases, things like SQL, things like MongoDB.

They might also need to know how to do things like implement or run,

things like Hadoop, which is a parallel processing infrastructure.

Now it's not necessarily true that they need to know any one of these buzzwords

or another.

But it is true that they need to have the combination of skills that allow them to

build out a data infrastructure that's supportive and that can be maintained.

So there's a couple of key characteristics that you're looking for

when you're looking for a data engineer.

So first, again, they need to be willing to find answers on their own.

This is again, a person that often will be one of the few people that's solely

responsible for the data infrastructure.

And so, often they need be able to answer those questions themselves.

They need to be able to go out and get the quotes on the Internet.

They need to be able to ask questions and figure out what's the right hardware,

and what's the right security measures to be taking, and so forth.

Often they will have to do that a little bit on their own, in the sense that,

the data engineering team is often very specific and expert in an area.

Where it's not clear that other people within your organization will be able to

give them a lot of advice.

They need to know a little bit of data science.

They need to know how data is used, how it's pulled, how it's analyzed.

So that they know how to build the infrastructure that's useful for them.

Ideally, they'll be closely collaborative with the data scientists,

maybe they've done that before.

And then they need to be able to work well under pressure.

And so one reason for that is that the data infrastructure for

an organization is often very critical.

And if it goes down then, say, your website might go down, or

you won't be able to do any analysis, or the organization sort of grinds to a halt.

And so having a data engineer that's able to work well under pressure,

that's able to keep things up and running, and keep things maintaining.

That makes good decisions about software maintainability, and hardware

maintainability, and somebody that's critical for your data engineering team.

And again, friendly but relentless, so

they need to be able to interact with people.

Again, their personal communication skills are highly undervalued but are very,

very important.

There's gonna be a lot of steps at which a data engineer is gonna have to interact,

especially at first in a small organization but even later on,

with data scientists, with other people in your organization, and external units.

And so you need to be able to have a friendly conversation with them.

And ideally,

they can explain in very simple terms why they need to make certain decisions.

So often the decisions, especially these days, are quite technical,

quite involved with the hardware and software involved.

And so it's very useful if they can, in very simple language,

explain what's going on, what the problem is, how it's gonna be fixed, and so forth.

That can be a huge advantage to your organization.

So that's what a little bit about, what a data engineer is.

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