Data Science and Computational Thinking [inc Big Data and Internet of Things]

The Metadata standards committee spoke with Jen Carlino and Lorna Schmid, Metadata Coordinators for the Federal Geographic Data Committee (FGDC), a 32-federal agency member body, in October 2017 with the goal of learning more about their current metadata interests and focus, including questions around how interoperability and inter-agency communication factor into metadata decisions.

In their role as Metadata Coordinator, Jen Carlino chaired, and succeeding her Lorna Schmid now chairs, the FGDC Metadata Working Group. Geospatial metadata issues and implementation, their primary responsibility, cuts across numerous working groups and organizations both federal and non-federal. With input from across this Metadata Community, the Metadata Coordinator assesses available tools and schema associated with national and international standards. She uses this information to propose endorsement by the FGDC of those standards recommended for adoption and implementation by federal agencies. A member of the FGDC’s Office of the Secretariat, she also facilitates metadata documentation best practices to enable access-for-use of geospatial data and services.

Ray Blackwood, vice president of product management at Campus Management, talks about how colleges can leverage machine learning and artificial intell

With first semester due to start in a month, experts say universities will be operating in uncharted digital territory – one that could prove dangerous if not handled correctly.

Digital data expert and EC Integrators managing director Emy Carr said online data was changing the education game and posing new challenges for higher education institutions.

Most prominent among these was the potential for universities to act as a “goldmine for hackers” if security measures were not implemented. Refusal to take such measures seriously could compromise reputation, student personal information, coursework, exam results, administration systems and research. Carr also raised the point that funded researchers were unlikely to contribute to institutions where they felt their security and work would be compromised.

On the other end of the scale, data has become increasingly important in terms of keeping track of students, measuring progress and engagement, determining student needs and assessing the overall quality of the university experience.

So how can universities prepare for the data era? We spoke to Emy Carr to find out more.

What You'll Learn:

Generate IoT concepts and design IoT solutions within your area of expertise.
Map out the process for an IoT solution, and identify the sensors and other devices required.
Evaluate different infrastructure components and network systems, and design the basic network for your IoT ideas.
Apply software solutions for different systems and Big Data to your concept designs, and appreciate how data is managed in the network.
Identify and analyse IoT security and privacy risks, and concept design secure hardware and software.
Produce a viable IoT concept design that solves a problem, is ready to prototype and test, and has an identified route to market.

Since the advent of so-called big data, much has been written about the possibilities and challenges of making the most of the multiple digital traces created online.

Even though research in this area is still emerging, enough has now been done across a wide range of disciplines to form the basis for this hugely ambitious book. Matthew Salganik has made it his mission to sketch out an emerging landscape by looking in turn at “observing behaviour”, “asking questions”, “running experiments”, “creating mass collaborations” and “ethics”. There is also a short final chapter on the future of social research.

A NEW commodity spawns a lucrative, fast-growing industry, prompting antitrust regulators to step in to restrain those who control its flow. A century ago, the resource in question was oil. Now similar concerns are being raised by the giants that deal in data, the oil of the digital era. These titans—Alphabet (Google’s parent company), Amazon, Apple, Facebook and Microsoft—look unstoppable. They are the five most valuable listed firms in the world. Their profits are surging: they collectively racked up over $25bn in net profit in the first quarter of 2017. Amazon captures half of all dollars spent online in America. Google and Facebook accounted for almost all the revenue growth in digital advertising in America last yea

[This article is the fourth in a 2017 series that presents case studies related to the theme of integration and partnerships as reflected in enterprise IT:

Communicating the Business Value of Enterprise IT
Integrating Data and Systems to Support Next-Generation Enterprise IT
Aligning Institutional Processes Through Business Process Redesign
Supporting Analytics through Data Integration and Governance
In this article, IT leaders describe how their institutions have used governance best practices to support institutional analytics initiatives.]

The changing landscape of enterprise IT is characterized by an expanding set of services, systems, and sourcing strategies as institutions move away from monolithic ERP solutions in an effort to increase agility and scalability and meet stakeholder demands for new functionality, services, and information. This time of change offers an opportunity for institutions to examine their data and analytics resources and needs. As the Enterprise IT Program completes its 2017 focus on integration and partnerships, the following case studies address ways that IT can support an institution's continuing and growing need for reliable information in the face of these changes.

Today we're adding MIT's course on Artificial Intelligence to our ever-growing collection, 1,300 Free Online Courses from Top Universities. That's because, to paraphrase Amazon's Jeff Bezos, artificial intelligence (AI) is "not just in the first inning of a long baseball game, but at the stage where the very first batter comes up." Look around, and you will find AI everywhere--in self driving cars, Siri on your phone, online customer support, movie recommendations on Netflix, fraud detection for your credit cards, etc. To be sure, there's more to come.

‘Blockchain’ is the word on every technologist, investor and academic’s lips. Hence, RMIT has just introduced an eight-week, online short course on the digital technology – the first university in Australia to do so. But first, block what? Exactly; that’s why a course cornerstone will be explaining what blockchain is, and how it can be applied. Put simply, it’s the technology behind giant, decentralised online ledgers, which are viewable by anyone. Though transactions are visible, people’s personal details (for instance, their names) are hidden. Only users have control over their inputs and outputs, known as blocks, which makes blockchain highly secure. Though if a user makes an alteration to their initial entry, it has to be approved by all users.

Free resource of educational web tools, 21st century skills, tips and tutorials on how teachers and students integrate technology into education

AI may seem like a confusing phenomenon, but we often interact with artificial intelligence technology on a daily basis.

"Every time a government comes in it needs to think about recommitting itself to some fundamental principles and one of them should be around what kind of digital state it wants to be. It is something that needs active engineering. It doesn’t happen on its own."

Painters’ light flows through floor-to-ceiling windows opposite, light shining on light-shining, as Sir Shadbolt talks transparency.

"Countries are seeing opportunities through the Open Government Partnership but periodically the fundamental principles of open data need to be recommitted to, to remind us that some of the most impressive examples of advance have been through datasets people previously thought about keeping secret," he said. 

In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics. As data science is a broad discipline, I start by describing the different types of data scientists that one may encounter in any business setting: you might even discover that you are a data scientist yourself, without knowing it. As in any scientific discipline, data scientists may borrow techniques from related disciplines, though we have developed our own arsenal, especially techniques and algorithms to handle very large unstructured data sets in automated ways, even without human interactions, to perform transactions in real-time or to make predictions. 

Learning analytics tools have become increasingly valuable for college and university administrators looking to boost student success. But can data also inform decision-making on the part of students themselves? A project at Purdue University (IN) explores that possibility by taking advantage of the "quantified self" movement (made popular by health-tracking apps such as Fitbit) and putting the data into students' hands.

Pattern, one of several teaching and learning apps developed by Purdue Teaching and Learning Technologies over the past few years, allows students to self-track their academic and extracurricular pursuits and rate how productive they are. The app also lets them compare their behaviors to other students to see which activities may yield the best results. Pattern can suggest when to study, recommend ways students can be more efficient with their time, and suggest how long students should be spending on tasks.