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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?

Best of arXiv.org for AI, Machine Learning, and Deep Learning – December 2017

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

Top 10 2017 Big Data White Papers: Data Science, Machine Learning, AI, Deep Learning & More

In this continuing regular feature, we give all our valued readers an annual heads-up for the top 10 most downloaded articles appearing on insideBIGDATA. Over the past several months, we’ve heard from many members of our audience that this feature will enable them to catch up with important news and features flowing across our many channels. We’re happy to oblige! We understand that busy big data professionals can’t check the site everyday.

5 Misconceptions About Data Science

In this contributed article, technology writer and blogger Kayla Matthews examines the 5 most common misconceptions floating around about data science and what project administrators and business managers need to be aware of. Remember these tips before getting involved, and be sure to do the necessary research. With the right people and knowledge on your side, you’ll be on your way in no time, rocketing to success.

The heroic Data Engineer – Lending a Helping Hand to Data Drowned Scientists

In this contributed article, Ida Jessie Sagina, a content marketing specialist at Mobius Knowledge Services, discusses the important role of the data engineer in today’s enterprise, and how to lay those data pipes and save the lives of drowning analysts and data scientists.