Day: June 30, 2020

For data scientists, drudgery is still job #1

The hassles of data intake and cleaning, problems with biased models and data privacy, and difficulty finding experience and technical skills—all these ranked among the biggest challenges facing data scientists and software engineers in data-science disciplines according to a newly released survey.

Anaconda, makers of the Python distribution of the same name for scientific computing applications, conducted its 2020 State Of Data Science survey with 2,360 respondents from 100 countries, slightly less than half of those hailing from the U.S.

Despite all the advances in recent years in data science work environments, data drudgery remains a major part of

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Samsung’s Galaxy Watch 3 firmware leaked — here’s what we know

Apple might be the world’s most popular smartwatch maker, but Samsung is a (not particularly) close second. And if any item is going to somehow knock the Apple Watch down a few rungs, then Samsung’s Galaxy Watch is probably your best bet.

So, what does the Korean tech giant have in store for us next? Well, the firmware for Samsung’s Galaxy Watch 3 has been leaked and now we have a far clearer picture.

Last night, Max Weinbach tweeted a range of info he’d pulled from the software, summarizing the main points succinctly here:

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3 things the pandemic taught us about cloud computing

We’re in a recovery now, and at some point, things will be back to near normal…hopefully. We learned that some businesses fared better than others during the upheaval. Nine times out of ten, those businesses leveraged cloud successfully to navigate the quick IT changes needed during the pandemic.

Many enterprises have learned some hard lessons. Indeed, I suspect more will come. Enterprises discovered more about the advantages and limitations of cloud computing in the last four months than in the previous two years. Here are three of the big ones I see consistently:

Cloudops is more important than we thought.

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Kaggle: Where data scientists learn and compete

Data science is typically more of an art than a science, despite the name. You start with dirty data and an old statistical predictive model and try to do better with machine learning. Nobody checks your work or tries to improve it: If your new model fits better than the old one, you adopt it and move on to the next problem. When the data starts drifting and the model stops working, you update the model from the new dataset.

Doing data science in Kaggle is quite different. Kaggle is an online machine learning environment and community. It has

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