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How Data Science Can Take Your Company to the Next Level

In this special guest feature, Deep Varma, VP of Engineering at Trulia discusses how managers can best support their new team of data scientists, bridge the gap between data science and product teams, and what to look out for during the interview process.

Why Data Science Is Becoming So Important in Cybersecurity

In this special guest feature, Mike MacIntyre, Chief Scientist at Panaseer discusses where data science comes in with respect to cybersecurity. With the correct data, CISOs can translate technical risk into business risk, deliver a business case to solve it and demonstrate success. The current struggle is that CISOs have information that is meaningful but not timely, or it is timely but not meaningful because the content is too technical and siloed. What they really need is data that will enable them to market and measure the security program.

Booz Allen & Kaggle’s Annual Data Science Competition Puts AI to Work Accelerating Life-Saving Medical Research

To spur automation of biomedical analysis, Booz Allen Hamilton (NYSE: BAH) and Kaggle launched the 2018 Data Science Bowl, a 90-day competition that calls on thousands of participants globally to train deep learning models to examine images of cells and identify nuclei, regardless of the experimental setup—and without human intervention. Creators of the top algorithms will split $170,000 in cash and prizes, including an NVIDIA® DGX Station™,

Using Data Science for Social Good

In this contributed article, Dr. Ken Sanford, the US lead Analytics Architect for Dataiku, discusses how some data scientists share a deep concern for social welfare and hope to use their discoveries to promote the betterment of our world. From advancements in healthcare technology to major activist efforts, the relationship between social good and data science can be found all around us, often in ways we may not expect.

How Data Scientists Are Wasting Their Time

In this contributed article, Abhi Yadav, Co-founder & CEO at ZyloTech points that while data scientists are flawed and there are lots of ways in which they could improve, so too are machines. It would seem that the best way forward is to work side-by-side, fleshy-arm-in-robotic-arm with the new race of machines and robots that will undoubtedly make our lives easier.

AI for the Enterprise: The Citizen Data Scientist

In this special guest feature, Rick Rider Product Director, Technology at Infor offers four important areas on which AI software providers can capitalize and place increased focus in order to find success.

Top Skills Data Scientists Need To Learn in 2018

Data scientists are in high demand, taking the number 1 spot in Glassdoor’s Best Jobs in America list in 2016 and 2017, with 4,84 position available and boasting a median base salary of $110,000. According to Jim Webber, Chief Scientist at Neo4j, the following is a short-list of the most essential tech skills for data scientists to adopt this year.

Separating Great Data Scientists From OK Data Scientists: Statistics

In this contributed article, technology writer and blogger Kayla Matthews discusses the importance of a strong foundation in statistics and probability theory for practicing data scientists. Data scientists, thanks to their background in statistics, can look at a set of information and come up with important trends and patterns.

Interview: Ida Johnsson, Ph.D. Candidate at the Department of Economics at USC

I recently caught up with Ida Johnsson, a Ph.D. Candidate at the Department of Economics at University of Southern California, to discuss how she is actively transitioning to the field of data science. This interview can serve as a compelling example for others wishing to move into the field of data science from other disciplines and explore career opportunities.

Pay Attention to Spatial Data, It Is the Next Frontier

In this special guest feature, Madhusudan Therani, CTO at Near, points out that with an almost endless list of sources – including map and satellite data, catchment areas, service points, building and customer locations, land use data, urban data, and communication pathways – spatial data is a valuable global commodity which comes in many forms. So why do businesses need to process spatial data and what are some of the challenges they face in doing so at scale?