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As I have said before, I think the term "data science" is a bit of a misnomer, but I was very hopeful after this discussion; mostly because of the utter lack of agreement on what a curriculum on this subject would look like. The difficulty in defining these skills is that the split between substance and methodology is ambiguous, and as such it is unclear how to distinguish among hackers, statisticians, subject matter experts, their overlaps and where data science fits.

From basic statistics to machine learning and new ways to think about visualization, the Data Science Starter Kit gives you the tools you need to get started with data. If you haven't yet taken the leap, why wait? And if you're already experienced with data, the Starter Kit will push you further. The package includes (8) titles on R, basic statistics and data analysis, Python, machine learning, and visualization. This kit includes everything you need from analysis, visualization, to management.

"In a post-fact era, you can be an elected official and have a remarkably flexible relationship with the truth. Take climate science: more than 97% of scientists agree that climate change is a man-made phenomenon, but conservative politicians--and more than 65% of Republicans in Congress--outdo one another to demonstrate just how little they believe in science."

The Manchester social networks group (MSNG) is a cross-disciplinary research group located in the School of Social Sciences. MSNG aims to discuss and to promote the application and development of Social Network Analysis methodology to answer important research questions in social science. The following SNA topics are of interest to the group: Data collection for social network analysis Longitudinal networks and network formation Neighbourhood and network variations in social and health outcomes Qualitative network analysis Social Networks and Social Movements Statistical modelling of social networks Visualising and describing social networks

Rioting and related intra-crowd dynamics are significant human processes, but we know less about the basic behavioral science and subsequent processes that drive and shape rioting than we would like to. This is due, in large part, to the difficulty in studying riots on the ground and to the sheer complexity of riot phenomena. We know even less about the geographical dynamics of rioting, even though there is a dedicated (but only general) appreciation that geography is important. Existing work has, for the most part, adopted the most straightforward path to discovery, by examining coarse (city-scale) geographies of rioting, or in the few instances where intra-crowd riot dynamics are considered they have focused on stylized abstractions of behavior. Because of the difficulties of using standard social science inquiry to study riots (surveys, ethnographic analysis, interviews), many researchers have turned to computer modeling to create synthetic riots that can be configured, sampled, and experimented with. But, building models of something as bewilderingly complex as rioting is really quite difficult and so many short-cuts are taken. In particular, models are usually cellular-based in form (where rasters represent people and their local environment) and founded on physical interactions between relatively "dumb" particle-people (where continuum mechanics, random walks, or particle-particle forces serve as a substitute for socio-spatial interaction and behavior).

Jaron Lanier is a computer science pioneer who has grown gradually disenchanted with the online world since his early days popularizing the idea of virtual reality. "Lanier is often described as 'visionary,' " Jennifer Kahn wrote in a 2011 New Yorker profile, "a word that manages to convey both a capacity for mercurial insight and a lack of practical job skills." Raised mostly in Texas and New Mexico by bohemian parents who'd escaped anti-Semitic violence in Europe, he's been a young disciple of Richard Feynman, an employee at Atari, a scholar at Columbia, a visiting artist at New York University, and a columnist for Discover magazine. He's also a longtime composer and musician, and a collector of antique and archaic instruments, many of them Asian. His book continues his war on digital utopianism and his assertion of humanist and individualistic values in a hive-mind world. But Lanier still sees potential in digital technology: He just wants it reoriented away from its main role so far, which involves "spying" on citizens, creating a winner-take-all society, eroding professions and, in exchange, throwing bonbons to the crowd.

"By Brendan DeMille via DeSmogBlog A new academic study confirms that front groups with longstanding ties to the tobacco industry and the billionaire Koch brothers planned the formation of the Tea Party movement more than a decade before it exploded onto the U.S. political scene. Far from a genuine grassroots uprising, this astroturf effort was curated by wealthy industrialists years in advance. Many of the anti-science operatives who defended cigarettes are currently deploying their tobacco-inspired playbook internationally to evade accountability for the fossil fuel industry's role in driving climate disruption. The study, funded by the National Cancer Institute of the National Institute of Health, traces the roots of the Tea Party's anti-tax movement back to the early 1980s when tobacco companies began to invest in third party groups to fight excise taxes on cigarettes, as well as health studies finding a link between cancer and secondhand cigarette smoke."

Big data might very well be the tech buzz word of the year. And just how hot is it? Consider this:  Data scientist Stephen Purpura - an expert in artificial intelligence, machine learning and predictive analytics who is studying for his PhD in information sciences at Cornell University - has received no fewer than 45 job offers in recent months. And they just aren't any fly-by-night offers, with some rolling in from big-name companies touting salaries of $300,000 or more.