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    Introduction Did you know that you can execute R and Python code remotely in SQL Server from Jupyter Notebooks or any IDE? Machine Learning Services in SQL Server eliminates the need to move data around. Instead of transferring large and sensitive data over the network or losing accuracy on ML training with sample csv files, […]
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  • Weekly Digest, July 16
    Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. Featured Resources and Technical Contributions Best Machine Learning Tools:…
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  • Critically Reading Scientific Papers
    Critically reading scientific papers is critical for Data Scientists working some areas - especially those working in health. With that in mind, here are some key considerations in reading scientific (peer-review, grey literature) papers: Theory: Is the theory sound? Are there theoretical issues in the design that cause…
    Howard Friedman
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What makes an expert data scientist?

The data scientist role has been described as “part analyst, part artist.” Anjul Bhambhri, vice president of big data products at IBM, says, “A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a Renaissance individual who really wants to learn and bring change to an organization.” The Data Science Beagle defines this as an expert.

Data Scientists ExplainedExpert vs. Automation

Harvard Business Review

Excerpt “It all made for messy data and unwieldy analysis, but as he began exploring people’s connections, he started to see possibilities. He began forming theories, testing hunches, and finding patterns that allowed him to predict whose networks a given profile would land in. He could imagine that new features capitalizing on the heuristics he was developing might provide value to users.”

Wikipedia

Excerpt “Data scientists use their data and analytical ability to find and interpret rich data sources; manage large amounts of data despite hardware, software, and bandwidth constraints; merge data sources; ensure consistency of datasets; create visualizations to aid in understanding data; build mathematical models using the data; and present and communicate the data insights/findings. They are often expected to produce answers in days rather than months, work by exploratory analysis and rapid iteration, and to get/present results with dashboards (displays of current values) rather than papers/reports, as statisticians normally do.”

IBM

Excerpt “What sets the data scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge. Good data scientists will not just address business problems, they will pick the right problems that have the most value to the organization.”

Thinking of hiring a data scientist?

How to Interview a Data Scientist

By Chris Pearson Big Cloud.io Excerpt “Having spent the last year interviewing a large number of Data Scientists, I’ve developed a simple set of questions that help me to understand the what, the why and the how of what they do.” ……….”It’s worth pointing out that if you find someone who has nailed all of the above questions and you have that gut feel that they may do wonders for your business, please don’t get too precious about culture, team fit, etc. Don’t get me wrong, these things are important, but people like this can be incredibly hard to find…sometimes harder than finding that missing piece of Mr Tatum. ”

Here’s why they may be difficult to manage.

 

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