A new survey from Jinfonet shows that the gap between the supply and demand for analytics insights for commercial customers presents a huge opportunity for companies.
- By Dean Yao
- November 10, 2016
Lead data scientist Alejandro Correa Bahnsen develops machine learning algorithms for fraud detection. He described for Upside the basic skills and personality traits he believes are necessary to succeed in data science.
- By James E. Powell
- November 9, 2016
Part 1 of this series showed that data needs the context of information to be useful. Here we explain why information alone is still insufficient.
- By Barry Devlin
- November 8, 2016
Data without context lacks meaning and purpose, but how is data different from information? Defining your terms is the first step to more insightful decisions.
- By Barry Devlin
- November 7, 2016
What does it take to be a data engineer? A background in software engineering doesn't hurt. Although the number of data engineers doubled from 2013 to 2015, that growth rate far outstripped that of data scientists.
- By Steve Swoyer
- November 4, 2016
Today read tips for landing a career in open source development, how to use analytics to improve customer interactions, and the highlights of a new benchmark study for analytics engines on Hadoop.
- By Lindsay Stares
- November 4, 2016
Great data scientists need to be open to a wide variety of perspectives.
- By James E. Powell
- November 3, 2016
To provide you with interesting, current information, Upside is collecting quick perspectives from working professionals. Today, meet Dean Abbott, president of Abbott Analytics.
- By James E. Powell
- November 1, 2016