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BI Corner
is an online resource for Business Intelligence, Analytics & Data Science Professionals. Featuring a combination of original & curated content, bicorner.com delivers the best independent collection of current, relevant and interesting Business Intelligence & Analytics information available.
Sincerely,
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Do Data Scientists Really Have the Best Job in America?
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Why data scientists love their jobs
If you're involved or interested in IT, then you're probably well aware that Big Data has exploded in popularity, relevance, and interest over the past several years. From family-owned farms to national governments, everyone seems to be seeking to incorporate big data and data analysis into their business models. This explosion in the relevancy and utility of big data has also generated high demand for data scientists and individuals skilled in data analysis. Recently, employment facilitator Glassdoor ranked Data Scientist as the best job in America. With a median base salary of $110,000 for the average data scientist, it might seem fairly obvious why data scientists love their jobs.
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Ensuring Effective Collaboration Between Data Scientists And Software Engineers
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How do you ensure a smooth hand-off between data science and engineering on ML projects?
Answer by Lili Jiang, Data Science Manager, on Quora: How do you ensure a smooth hand-off between data science and engineering on ML projects? Excellent question, and one that I've been thinking a lot. At Quora, my data science team works closely with machine learning engineers. Joe Isaacson already provided a great answer to this question, and I will add some of my learnings.
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Build the Right Team for Big Data Success
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Get the Right People on Your Big Data Bus
You may have been successful with a center of excellence for your business intelligence or analytics practice. But big data success requires a different approach, a bus.So you want to invest in big data capabilities? You may be asking yourself who should be on the big data team, and what project should this team work on first? How do you put together the capability - the team - to tackle the big questions? Organizations may find themselves hung up on these types of questions, whether they are just starting out with big data or even if they already have projects underway.
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Who Will Untap The Next Big Thing in Machine Learning? Hint: It's Not Our Data Scientists
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What's needed is a better balance of data scientists and engineers who can produce machine learning insights at industrial scale
In this special guest feature, Michael Liberty, COO and co-founder of Signifyd, argues that an overabundance of data scientists is slowing the growth of machine learning. Prior to Signifyd, Michael was the Head of Digital Goods and Platform Risk at PayPal. Michael holds an A.B. in Economics from the University of Chicago and an MBA from Stanford. One of the unfortunate, unanticipated side effects of the title Data Scientist growing in popularity is that we've developed a misguided idea of how machine learning should be applied to the problems we're trying to solve.
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| 3 Ways Data Dashboards Can Mislead You |
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Executives love dashboards
And why wouldn't they? Single-screen "snapshots" of operational processes, marketing metrics, and key performance indicators (KPIs) can be visually elegant and intuitive. They show just-in-time views of what's working and what isn't - no need to wait for weekly or monthly reports from a centralized data center. A quick scan of a dashboard gives frontline managers transparency and, ideally, the opportunity to make rapid adjustments.
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