Before signing up for your new gig, consider negotiating for ways to hold your data engineering colleagues accountable f

This might be exactly the kind of fun you want — as long as you’re going in with open eyes. Building your data engineering team could take years. Sure, it’s nice to have an excuse to learn something new, but in all likelihood, your data science muscles will atrophy as a result.

Grocery shopping is easy if you’re just cooking something for your own dinner, but large scale turns the trivial into the Herculean — how do you acquire, store, and process 20 tons of ice cream… without letting any of it melt?

If you’re considering taking a job as a head of data science, your first question should always be, “Who is responsible for making sure my team has data?” If the answer is YOU, well, at least you’ll know what you’re signing up for.

Similarly, “data engineering” is fairly easy when you’re downloading a little spreadsheet for your school project but dizzying when you’re handling data at petabyte scale. Scale makes it a sophisticated engineering discipline in its own right.

Since data science is at the mercy of data, merely having data engineering colleagues might not be enough. You might face an uphill struggle if those colleagues fail to recognize you as a key customer for their work. It’s a bad sign if their attitude reminds you more of museum curators, preserving data for its own sake.

http://live-stream.munich.es/twr/video-Al-Rayyan-Al-Gharafa-v-en-gb-1ykx30122020-24.php

http://live07.colomboserboli.com/niy/video-Al-Rayyan-Al-Gharafa-v-en-gb-1khz-23.php

http://news24.gruposio.es/ktn/Video-Qatar-SC-Al-Kharaitiyat-v-en-gb-1rxf-4.php

http://news24.gruposio.es/ktn/video-Al-Rayyan-Al-Gharafa-v-en-gb-1ztg-.php

ect-based courses are where the real learning is. Come up with a small product you want to build, do some research on the necessary parts to learn, learn them on-demand as you build it up. When you are done, deploy it and try to get some users. Write an article teaching others by explaining the process in detail. It could become more useful than your resume in the future! This is top-down learning, an approach that is most effective in learning anything practical but is usually not adopted by traditional schools.

If data science is the discipline of making data useful, then you can think of data engineering as the discipline of making data usable. Data engineers are the heroes who provide behind-the-scenes infrastructure support that makes machine logs and colossal data stores compatible with data science toolkits.

Unlike data scientists, data engineers tend not to spend much time looking at data. Instead, they look at and work with the infrastructure that holds the data. Data scientists are the data-wranglers, while data engineers are the data-pipeline-wranglers.

Instead of expecting data people to be able to do all of it, let’s start asking one another (and ourselves), “Which kind are you?” Let’s embrace working together instead of trying to go it alone.

While it’s true that you’re a key customer for data engineering, you’re probably not the only customer. Modern businesses use data to fuel operations, often in ways that can hum along nicely enough without your interference. When your contribution to the business is a nice-to-have (and not a matter of your company’s survival), it’s unwise to behave as if the world revolves around you and your team. A healthy balance is healthy.

In other words, if a company doesn’t have any data or data engineers, then accepting a role as Chief Data Scientist means putting your data science career on hold for a few years in favor of a data engineering career — that you might not be qualified for — while you build a data engineering team. Eventually, you’ll gaze proudly at the team you’ve built and realize that it no longer makes sense for you to do the nitty-gritty yourself. By the time your team is ripe for those cool neural networks or fancy Bayesian inference that you did your PhD on, you have to sit back and watch someone else score the goal.

As an analogy, imagine you’re a translator who is fluent in Japanese and English. You’re offered a job called “translator” (so far, so good) but when you arrive at work, you discover that you were hired to translate from Mandarin to Swahili, neither of which you speak. It might be stimulating and rewarding to take the opportunity to become quadrilingual, but do be realistic about how efficiently you’ll be using your primary training (and how terrifying your first performance review may be).

If you’ve just felt the urge to run off and study both disciplines, you might be a victim of the (stressful and self-defeating) belief that data professionals have to know the everything of data. The data universe is expanding rapidly — it’s time we started recognizing just how big this field is and that working in one part of it doesn’t automatically require us to be experts of all of it. I’d go so far as to say that it’s too big for even the most determined genius to swallow whole.

You can’t do data science if there’s no data. If you get hired to be head of data science in an organization where there’s no data and no data engineering, guess who’s going to be the data engineer…? You!

Maybe. It depends how much you love the discipline you already know. Data engineering and data science are different, so if you’re a data scientist who didn’t train for data engineering, you are going to have to start from scratch.