Smart Project Management –Best Practices of Good Managers

Project management could be one of the easiest jobs on the planet, and could also be the worst nightmare. The difference between the two extremes depends on smart management of a project. According to the project management institute, there are five phases in project management - Initiating, Planning, Executing, Monitoring & Controlling, and Closing.

Every manager has his own style of project management. But there are a lot of contributing factors that result in a successfully managed project. These factors vary from project to project, but they all contain some common elements.

1. Setting SMART Goals

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

Many individuals are looking to break into a video game designing career, and it's no surprise. A $9 billion industry, the video game designing business has appeal to gamers and non-gamers alike. High salaries and high rates of job satisfaction are typical in the field.

In order to design video games, however, you need a certain skill set. Computer programming is first on the list. While games are made using almost all languages, the most popular programming language for video games is C++, because of its object-oriented nature and because it compiles to binary. The next most popular languages for games are C and Java, but others such as C# and assembly language are also used. A strong background in math is usually required to learn these languages. Individuals wishing to design games should also have an extensive knowledge of both PCs and Macs.

There are many colleges and universities that offer classes not only in programming but also classes specifically on game design. Some of these schools have alliances with game developing companies, leading to jobs for students upon graduation. Programming video games can be lucrative. The average game designer's salary is $62,500, with $55,000 at the low end and $85,000 at the high end.

Programmers are not the only individuals needed to make a video game, however. There are multiple career paths within the gaming industry, including specialists in audio, design, production, visual arts and business.

Designing a video game can be an long, expensive process. The average budget for a modern multiplatform video game is $18-$28 million, with some high-profile games costing as much as $40 million. Making the game, from conception to sale, can take several months to several years. Some games have taken a notoriously long time to make; for example, 3D Realms' Duke Nukem Forever was announced in April 1997 and did not make it to shelves until July 2011.

Video game programmers have a high level of job satisfaction. In a March 2013 survey conducted by Game Developer magazine, 29 percent of game programmers were very satisfied with their jobs, and 39 percent were somewhat satisfied.

If you're interested in a game development career, now's the time to get moving. Take advantage of the many online resources available regarding these careers and start learning right away.

Data has always been important to business. While it wasn't long ago that businesses kept minimal information on people who bought their products, nowadays companies keep vast amounts of data. In the late 20th century, marketers began to take demographics seriously. It was hard to keep track of so much information without the help of computers.

Only large companies in the '60s and '70s could afford the research necessary to deliver real marketing insight. The marketers of yesteryear relied upon focus groups and expensive experiments to gauge consumer behavior in a controlled environment. Today even the smallest of companies can have access to a rich array of real-world data about their consumers' behavior and their consumers. The amount of data that is stored today dwarfs the data of only a few years ago by several orders of magnitude.

So what kind of information are businesses storing for marketing purposes? Some examples include:

- Demographic information — age, gender, ethnicity, education, occupation and various other individual characteristics.