by Angela Guess Scott Howser recently wrote in Smart Data Collective, “Having the technology, team, and overall support in place at the onset will only help data scientists succeed and further augment results. Data scientists rely on software like machine intelligence—focused on human-interpretable results and quick iteration—to create models. If technology is outdated and/or data […]
Building Cohesive, Productive Data Science Teams
by Angela Guess Miles Johnson and Sam Hochgraf of IBB Consulting Group recently wrote in InsideBigData, “Look no further than the sports world for proof that just having all-star talent doesn’t always guarantee success. They must work together cohesively, have a solid strategy and be organized in a way that plays to each member’s strengths. […]
50 Years of Data Science: What It Is and What It Can Be
by Angela Guess Steve Miller writes in Information Management, “I recently came across an outstanding article on data science, thanks to the always-informative R-bloggers website. Written by Stanford professor David Donoho, ‘50 years of Data Science’ views DS through an historical lens and as well provides a conceptual framework for the evolving discipline. The point […]
Data Science Trends to Look for in 2016
by Angela Guess Mike Weston recently wrote in Dataconomy, “Many people still don’t realise how much data science touches their everyday lives, from Amazon recommendations to the algorithms powering their Uber app. With adoption of data science up across most business verticals, it’s natural to wonder how the sector will develop in 2016. My gut […]
The Top 10 Skills Required in Data Science
by Angela Guess Bob Hayes recently analyzed the top ten skills needed by Data Science professionals in Business2Community. He writes, “In our ongoing study of data scientists, we ask data professionals to indicate their proficiency in 25 different data science skills. The 25 skills, listed in Figure 1 (above), reflect the set of skills that […]