RSS Featured Blog Posts
  • 3 Reasons to Learn the Expected Value Framework for Data Analysis
    One of the most difficult and most critical parts of implementing data science in business is quantifying the return-on-investment or ROI.  In this article, we highlight three reasons you need to learn the Expected Value Framework, a framework that connects the machine learning classification model to ROI.  …
    Matt Dancho
  • Remotely Send R and Python Execution to SQL Server from Jupyter Notebooks
    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, […]
    Kyle Weller
  • 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:…
    Vincent Granville
  • 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
  • The art of data science...
    In 2018, Fast Company declared ‘Data Scientist’ as the best job in America for the third…
    Ziyad Nazem

Data Scientist Interviews

What data scientist’s do all day at work

Ram Narasimhan of GE talks about the importance of curiosity and what makes his day

“What I do as a Data Scientist” Dan Mallinger

Excerpt from Data Scientist Interviews “I’m a data scientist with degrees in mathematical sciences and organizational psychology; I also have significant academic training in computer science and sociology. I’ve spent my career in statistics, analytics, and technology roles but almost entirely under business groups, which has framed much of my professional outlook. Today, I am the Director of Data Science for Think Big and have been with the company for four years.”

Interview: Michael Brodie – We Can’t Rely on Machines

Excerpt from Data Scientist Interviews “So yes, there is a lot of hype?But I actually think it is far more profound and powerful than most people are conceiving it at the moment. It has already changed a very large number of operating processes in health care, manufacturing, marketing and stock markets. How-ever, it is not as widely used as one might think. Big Data and Big Data Analytics are in their infancy with respect to operational deployment and our understanding of it.”

Crushed it! Landing a data science job by Erin Shellman

“Data science interviews are the worst because data science is interdisciplinary: code for “you have to know everything about all the disciplines.”  Depending on the company and the team, your interview might look like a software developer’s interview, or it might look a like a statistician’s interview, and the bad news is that virtually none of the material overlaps.  I recently spent a ton of time studying for interviews and I’ve got some hot tips to pass along if you’re thinking about a move soon.”

 

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