Data Scientists

How to create a competitive advantage in today's digital marketplace

July 18, 2014
If most organizations are using analytics to improve customer interactions, optimize supply chains and reduce financial risk then where does the advantage in today's marketplace come from? The IBV 2014 Analytics study will explore how organizations are creating a competitive advantage in today's data-driven marketplace. Read More

Real-world experiments and the Facebook controversy over mood manipulation

July 17, 2014
The recent controversy over the ethics of Facebook's attempts to influence moods through tweaks to its newsfeed algorithms is overblown. Essentially, Facebook data scientists conducted one of many real-world experiments that are standard operating procedure with them and with most online businesses these days. This was just a routine real-world experiment in big-data-driven sentiment analysis, content optimization and customer experience management. Read More

Streaming media and narrative power of video content analytics

July 3, 2014
Video content analytics tools are humanity's unblinking eyes, capable of continuously filtering the world's media streams at scale. Video content analytics algorithms can parse the fine details within and between successive frames of specific streams, supporting pattern recognition, gesture recognition, location detection, motion detection, event detection, production-style detection, dynamic video masking and camera tamper detection. Read More

Maximizing the value of your data through analytics

June 27, 2014
You don’t want to miss the next TDWI Solution Spotlight on “Maximizing the Value of Your Data through Analytics.” Claudia Imhoff, president and founder of Intelligent Solutions Inc., will share everything from modern data architectures and use cases to data scientists and “things to ponder” when evolving your analytics programs. Read More

The delicate art of data science project prioritization and triage

June 19, 2014
Prioritizing data mining projects is a delicate art, equivalent to the decisions that R&D managers face every single day. How should you prioritize your data mining efforts and allocate your limited resources most effectively? Most important, how do you decide what NOT to work on? Read More

3 reasons why DataOps is essential for big data success

June 19, 2014
Data science is extremely important in today’s data-driven world, but is only effective if it can be efficiently executed in a production environment. Find out about an essential best practice to make your data science effective. Read More

Scientists beginning to tap the research potential of the quantified self

June 12, 2014
Real-world experimentation of a very personal and hyper-analytical nature is what the quantified-self (QS) movement is all about. QS practitioners are playing with approaches that behavioral scientists have traditionally applied to third-party subjects within controlled laboratory experiments. The scientific establishment is beginning to realize the potential of quantified self tools for gaining primary data directly from human subjects in a way that is organic to the biological, behavioral, and psychological phenomena being studied. Read More

Making data science a team effort

June 12, 2014
Many make out the data scientist to be a Renaissance woman or man who can single-handedly elevate the organization’s analytics savvy. However, preparing students for corporate roles in data science means training them for many positions on a team. At Arizona State University, we work closely with our industry advisory boards to create real-world curriculum for programs in business analytics. Read More

Privacy, big data and analytics: A perfect storm

June 6, 2014
A delicate balance of appropriate uses, relevant policies and consumer awareness is needed to achieve an effective privacy strategy for use by the big data and analytics community. Read More

Use deep learning to filter big data for the otherwise unknowable

June 5, 2014
A data scientist uses machine learning (ML) to find heretofore unknown correlations and other patterns in fresh data. ML is adept at finding both the "known unknowns" and the "unknown unknowns" through the power of supervised learning and unsupervised learning methodologies, respectively. Read More

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