Interview with Rebecca Nugent of Carnegie Mellon University.
In this episode Jeff and I talk with Rebecca Nugent, Associate Teaching Professor in the Department of Statistics at Carnegie Mellon University. We talk with her about her work with the Census and the growing interest in statistics among undergraduates.
Harvard Business Review: Data Scientist Is The ‘Sexiest Job Of The 21st Century’ | Popular Science
What is the sexiest job of the 21st century? If you said “data scientist,” you’re probably an editor at Harvard Business Review and probably not anyone else. The HBR has named the emerging practice of sifting through data to find hidden, below-the-surface meaning and otherwise extrapolate underlying knowledge the “sexiest” job of the new century. But while we love Big Data here at PopSci (we dedicated a whole issue to last year), we’re going to have to argue semantics here. Data scientists are certainly in demand (you might even get away with calling it a “hot” profession), but unfortunately that’s not what “sexy” means. It’s an interesting piece, nonetheless. Read it here.
“Josette Rigsby reports that a recent Jaspersoft study supports the general consensus that there is currently a major lack of skilled Data Scientists. She writes, “Business intelligence platform provider Jaspersoft has released a new survey that examines how companies across the globe are using big data analytics. Although many studies indicate the challenge of managing rapidly growing data volumes paralyzes many companies into inaction, Jaspersoft’s research tells a different story. The data shows 62 percent of respondents plan to implement big data solutions in the next twelve months. Jaspersoft’s new big data survey includes 631 respondents from the company’s user community. The survey includes respondents from more than fifteen countries that are primarily employed by companies with less than US$ 10M in revenue (30 percent).”
Two new hires, a data scientist, and a practice leader on their challenges and motivations, succeeding at IBM, and the qualities future consultants will need.
Data Scientists: Illuminate Your Patterns with Pictures
James KobielusScientific inquiry is all about finding non-obvious patterns in observational data. It’s no surprise that that is also the core of data science.
Patterns may be obvious to any sentient creature, or they may be deeply invisible - until we invent the conceptual or technological tools to bring them to the surface. The conceptual tools may be groundbreaking paradigm shifts, such the “thought experiment” that shaped Einstein’s insight into special relativity, or powerful new frameworks of visual notation, such as Feynman’s diagrams of subatomic particle interactions.
Patterns feel ghostly and unreal until we can actually see them, on some level, with our eyes. The chief technological tools are whatever scientists and engineers can use to bring these ghosts to light. In the realm of the subatomic, the magical inventions have been visualization technologies such as the cloud chamber and the scanning tunneling microscope (the latter was invented by IBM, by the way).
Most real-world data science serves commercial interests, rather than pure science. But the restless search for deep patterns is no less critical in the business wars than among geniuses vying for Nobel Prizes. Today’s data scientists have two broad sets of pattern-sensing tools: advanced visualizations and statistical algorithms. No advanced analytic toolkit is complete without a best-of-breed library of them, with visualizations serving as the core interface at the heart of every step in the development, maintenance, and governance processes. You will find these complementary technologies - visualizations and algorithms - supported within IBM SPSS Modeler and in the complementary Big Data platforms, such as IBM Netezza Analytics, IBM InfoSphere BigInsights, and IBM InfoSphere Streams, where data is stored and resource-hungry computations are performed.
A Data Scientist You’ve Never Heard of Is Now the Master of Your Domain. Crowdsourcing analysis of big data achieved a 340% improvement over Allstate’s ability to predict claims. Via Andrew Mcafee
“In order to solve contemporary business problems, a big data strategy is needed much more than any one product. As I explained in my prior article, “Curing the Big Data Storage Fetish,” there is a growing understanding among enterprises that solving the big data conundrum can’t just be about acquiring more data warehousing technology. To fully exploit the opportunity presented by big data, a value chain must be created that helps address the challenges of acquiring data, evaluating its value, distilling it, building models both manually and automatically, analyzing the data, creating applications, and changing business processes based on what is discovered. Organizations have to figure out a way to increase analytical capacity, not just raw storage capacity.”
The hot tech gig of 2022: Data scientist - Fortune Tech
By the end of the decade 50 billion devices will be emitting information nonstop. Data scientists will help manage it all.
A decade from now the smart techies who decided to become app developers may wish they had taken an applied-mathematics class or two. The coming deluge of data (more on that in a moment) will create demand for a new kind of computer scientist — a gig that’s one part mathematician, one part product-development guru, and one part detective.
D.J. Patil is a pioneer in the field of data science, a new discipline that aims to organize and make sense of all the data generated by machines. It’s a challenge that will grow exponentially over the next decade.
Tech in 2012: Face-offs, failures and fairly big changes at the office
Today there are some 400 million devices connected to the Internet, mostly phones and computers. By 2020 some 50 billion devices, from cars to appliances, will be talking to one another. And companies will need teams of data scientists like Patil to sort through everything from internal inventory metrics to customer tweets. The role is so important that Greylock Partners has hired Patil to serve as a “data scientist in residence” to help its portfolio companies mine their data for patterns or stats that will make them more efficient or smarter than their competitors.
