The World Wide Web is a fun place to connect with old friends, make new ones, and stay involved in social media. It can also be a dangerous place for those who don’t know how to be safe on the web. Children, teenagers, and young adults with Asperger’s syndrome are especially vulnerable to fraud, sexual predators, and other online dangers.

What is Asperger’s Syndrome?

Asperger’s syndrome is a pervasive developmental disorder on the autistic spectrum. Children, teenagers, and adults with this developmental disorder are not sick. They’re brains are wired differently from people who are not on the spectrum. In the autistic community, people who are not on the spectrum are referred to as neurotypical.

The reason Internet dangers are so much more of a risk for people with Asperger’s syndrome is because of the symptoms associated with it. The best way to describe Asperger’s to someone who is not familiar with it is to call it a social learning disability. The parts of the brain responsible for reading facial expressions, body language, and other social cues do not function properly.

Python and Ruby, each with roots going back into the 1990s, are two of the most popular interpreted programming languages today. Ruby is most widely known as the language in which the ubiquitous Ruby on Rails web application framework is written, but it also has legions of fans that use it for things that have nothing to do with the web. Python is a big hit in the numerical and scientific computing communities at the present time, rapidly displacing such longtime stalwarts as R when it comes to these applications. It too, however, is also put to a myriad of other uses, and the two languages probably vie for the title when it comes to how flexible their users find them.

A Matter of Personality...

That isn't to say that there aren't some major, immediately noticeable, differences between the two programming tongues. Ruby is famous for its flexibility and eagerness to please; it is seen by many as a cleaned-up continuation of Perl's "Do What I Mean" philosophy, whereby the interpreter does its best to figure out the meaning of evening non-canonical syntactic constructs. In fact, the language's creator, Yukihiro Matsumoto, chose his brainchild's name in homage to that earlier language's gemstone-inspired moniker.

Python, on the other hand, takes a very different tact. In a famous Python Enhancement Proposal called "The Zen of Python," longtime Pythonista Tim Peters declared it to be preferable that there should only be a single obvious way to do anything. Python enthusiasts and programmers, then, generally prize unanimity of style over syntactic flexibility compared to those who choose Ruby, and this shows in the code they create. Even Python's whitespace-sensitive parsing has a feel of lending clarity through syntactical enforcement that is very much at odds with the much fuzzier style of typical Ruby code.

For example, Python's much-admired list comprehension feature serves as the most obvious way to build up certain kinds of lists according to initial conditions:

a = [x**3 for x in range(10,20)]
b = [y for y in a if y % 2 == 0]

first builds up a list of the cubes of all of the numbers between 10 and 19 (yes, 19), assigning the result to 'a'. A second list of those elements in 'a' which are even is then stored in 'b'. One natural way to do this in Ruby is probably:

a = (10..19).map {|x| x ** 3}
b = a.select {|y| y.even?}

but there are a number of obvious alternatives, such as:

a = (10..19).collect do |x|
x ** 3
end

b = a.find_all do |y|
y % 2 == 0
end

It tends to be a little easier to come up with equally viable, but syntactically distinct, solutions in Ruby compared to Python, even for relatively simple tasks like the above. That is not to say that Ruby is a messy language, either; it is merely that it is somewhat freer and more forgiving than Python is, and many consider Python's relative purity in this regard a real advantage when it comes to writing clear, easily understandable code.

And Somewhat One of Performance

Big data is now in an incredibly important part of how many major businesses function. Data analysis, or the finding of facts from large volumes of data, helps businesses make many of their important decisions. Companies that conduct business on a national or international scale rely on big data in order to plot the general direction of their business. The concept of big data can be very confusing due to the sheer scale of information involved.  By following a few simple guidelines, even the layman can understand big data and its impacts on everyday life.

What Exactly is Big Data?

Just about everyone can understand the concept of data. Data is information, and information is everywhere in the modern world. Anytime you use any piece of technology you are making use of data. Anytime you read a book, skim the newspaper or listen to music you are also making use of data. Your brain interprets and organizes data constantly from your senses and your thoughts.

Big data, much like its name infers, simply describes this same data on a large sale. The internet allowed the streaming, sharing and collecting of data on a scale never before imaginable and storage technology has allowed ever increasing hoards of data to be accumulated. In order for something to be considered “big data” it must be at least 10 terabytes or more of information. To put that in perspective, consider that 10 terabytes represents the entire printed collection of material in the Library of Congress. What’s even more remarkable is that many businesses work with far more than the minimum 10 terabytes of data. UPS stores over 16 petabytes of data about its packages and customers. That’s 16,000 terabytes or the equivalent to 1,600 printed libraries of congress. The sheer amount of that data is nearly impossible for a human to comprehend, and analysis of this data is only possible with computers.

How do Big Data Companies Emerge?

All of this information comes from everywhere on the internet. The majority of the useful data includes customer information, search engine logs, and entries on social media networks to name a few. This data is constantly generated by the internet at insane rates. Specified computers and software programs are created and operated by big data companies that collect and sort this information. These programs and hardware are so sophisticated and so specialized that entire companies can be dedicated to analyzing this data and then selling it to other companies. The raw data is distilled down into manageable reports that company executives can make use of when handling business decisions.

The Top Five:

These are the five biggest companies, according to Forbes, in the business of selling either raw data reports or analytics programs that help companies to compile their own reports.

1. Splunk
Splunk is currently valued at $186 million.  It is essentially a program service that allows companies to turn their own raw data collections into usable information.

2. Opera Solutions
Opera Solutions is valued at $118 million. It serves as a data science service that helps other companies to manage the raw data that pertains to them. They can offer either direct consultation or cloud-based service.

3. Mu Sigma
Mu Sigma is valued at $114 million.  It is a slightly smaller version of Opera Solutions, offering essentially the same types of services.

4. Palantir
Palantir is valued at $78 million.  It offers data analysis software to companies so they can manage their own raw data analysis.

5. Cloudera
Cloudera is valued at $61 million.  It offers services, software and training specifically related to the Apahce Hadoop-based programs.

The software and services provided by these companies impact nearly all major businesses, industries and products. They impact what business offer, where they offer them and how they advertise them to consumers. Every advertisement, new store opening or creation of a new product is at least somewhat related to big data analysis. It is the directional force of modern business.

Sources:
http://www.sas.com/en_us/insights/big-data/what-is-big-data.html

http://www.forbes.com/sites/gilpress/2013/02/22/top-ten-big-data-pure-plays/

http://www.whatsabyte.com/

Related:

How does Google use Python?

Top Innovative Open Source Projects Making Waves in The Technology World

Is the U.S. the Leading Software Development Country?

How to Keep On Top Of the Latest Trends in Information Technology

No industry is as global as software development.  Pervasive networking means that software developers can, and do, work from anywhere. This has led many businesses to hiring development subcontractors in other countries, aiming to find good development talent at lower prices, or with fewer hassles on entry into the US.

While this is an ongoing and dynamic equilibrium, there are compelling reasons for doing software development in the United States, or using a hybrid model where some parts of the task are parceled out to foreign contractors and some are handled locally.

Development Methodologies

The primary reason for developing software overseas is cost reduction. The primary argument against overseas software development is slower development cycles. When software still used the "waterfall" industrial process for project management (where everything is budgeted in terms of time at the beginning of the project), offshoring was quite compelling. As more companies emulate Google and Facebook's process of "release early, update often, and refine from user feedback," an increasing premium has been put on software teams that are small enough to be agile (indeed, the development process is called Agile Development), and centralized enough, in terms of time zones, that collaborators can work together. This has made both Google and Facebook leaders in US-based software development, though they both still maintain teams of developers in other countries tasked with specific projects.

Localization For Americans

The United States is still one of the major markets for software development, and projects aimed at American customers needs to meet cultural norms. This applies to any country, not just the U.S. This puts a premium on software developers who aren't just fluent in English, but native speakers, and who understand American culture. While it's possible (and even likely) to make server-side software, and management utilities that can get by with terse, fractured English, anything that's enterprise-facing or consumer-facing requires more work on polish and presentation than is practical using outsourced developers. There is a reason why the leaders in software User Interface development are all US-based companies, and that's because consumer-focused design is still an overwhelming US advantage.

Ongoing Concerns

The primary concern for American software development is talent production. The US secondary education system produces a much smaller percentage of students with a solid math and engineering background, and while US universities lead the world in their computer science and engineering curricula, slightly under half of all of those graduates are from foreign countries, because American students don't take the course loads needed to succeed in them. Software development companies in the United States are deeply concerned about getting enough engineers and programmers out of the US university system. Some, such as Google, are trying to get programmers hooked on logical problem solving at a young age, with the Summer of Code programs. Others, like Microsoft, offer scholarships for computer science degrees.

Overall, the changes in project management methodologies mean that the US is the current leader in software development, and so long as the primary market for software remains English and American-centric, that's going to remain true. That trend is far from guaranteed, and in the world of software, things can change quickly.