Python programming language is general purpose open source programming language. One of its main features is flexibility and ease of use. Python has a variety of useful set of utilities and libraries for data processing and analytical tasks. Currently due to the rise in demand of big data processing python has grown in popularity because its features are easy to use which are core to the processing of huge chunks of information.

Guido Van Rossum, the pioneer of python, introduced python in the year 1980 and then implemented it in 1989. The intention behind the development of python was to make it open source language that can also be used for commercial projects. The fundamental principle of python is to write the code that is easy to use, highly readable and embrace writing fewer lines of code for achieving a particular task. One of the most popular standard libraries which have ready to use tools for performing a various work is Python Package Index. It was introduced in January 2016 and contains more than 72,000 packages for third-party software usage.

Python plays a critical role in linking data to customers. Recently python has found few entry barriers and many people have had access to have experienced the power of python in the past. So, what makes python the best language for big data analytics?

One of the reasons to choose python is that python ecosystem is very vibrant, the ratings at Redmonk are a proof of the strength python community. The Redmonk ranking is based on StackOverflow discussions and contribution made in Github to determine the popularity of programming language on the method used by users to ask questions about Python and the number of the open source projects contributions.

Programmers often tend to be sedentary people. Sitting in a chair and pressing keys, testing code, and planning out one logical step-wise strategy after another to get the computer to process data the way you want it to is just what life as a programmer is all about. But, is being too sedentary hindering a programmers max potential? In other words, will getting up, moving around, and getting the blood pumping make us better programmers? To answer this question more efficiently, we will need to consider the impact of exercise on various aspects of programming.

Alertness And Focus

It is no surprise that working up a sweat makes the mind wake up and become more alert. As the blood starts pumping, the body physically reacts in ways that helps the mind to better focus. And improving our focus might make us better programmers in the sense that we are more able to wrap our mind around a problem and deal with it more efficiently than if we feel sluggish and not so alert. However, improving one's focus with exercise can be augmented by taking such vitamins as B6, Coleen, and eating more saturated fats rather than so many sugars. Exercise alone may be a good start, but it is important to realize that the impact of exercise on overall focus can be enhanced when combined with other dietary practices. However, it never hurts to begin a day of programming with fifteen minutes of rigorous workout to give the mind a little extra push.

Increase In Intellect

Does exercise cause a programmer to become a smarter programmer? This is perhaps a trickier question. In some sense, it might seem as if exercise makes us more intelligent. But, this may be more because our focus is sharper than because of any increase in actual knowledge. For example, if you don't know how to program in Python, it is highly doubtful that exercising harder will all of a sudden transfer such insights directly to your brain. However, exercise might have another indirect impact on a programmer’s intellect that will help them to become a better programmer. The more a person exercises, the more stamina and energy they will tend to have, as compared to programmers who never exercise all that much. That additional energy and stamina might help a programmer to be able to push themselves to learn things more efficiently, simply because they aren't getting tired as much as they study new languages or coding techniques. If you have more energy and stamina throughout the day, you will likely be more productive as a programmer as well. Greater productivity can often make one program better simply because they actually push themselves to finish projects. Other programmers who do not exercise on a regular basis may simply lack the energy, stamina, and motivation to follow through and bring their programming projects to completion.

Memory

The ability to remember things and recall them quickly is key to being an efficient programmer. Getting up and getting real exercise may be central to making sure that one does not lose control of these cognitive abilities. According to the New York Times, article, Getting a Brain Boost Through Exercise, recent research studies on mice and humans have shown that, in both cases, exercise does in fact appear to promote better memory function as well as other cognitive factors like spacial sense. (1) Consequently, if a person intends to be a programmer for a long time and wants their mind to be able to remember things and recall them more easily, then exercise may need to become an essential part of such a programmer's daily routine.

As much as one might want to resist the need for exercise and be sedentary programmers, the simple fact is that exercise very well could improve our ability to program in numerous ways. More importantly, exercise is critical to improving and maintaining good health overall. Even if a person does not have much time to get up and move around during the day, there are exercises that one can do while sitting, which would be better to do than no exercise at all.

What are a few unique pieces of career advice that nobody ever mentions?

What Options do Freelance Consultants Have with Large Corporations

The mainstay of a corporation is the data that it possesses. By data, I mean its customer base, information about the use of its products, employee roles and responsibilities, the development and maintenance of its product lines, demographics of supporters and naysayers, financial records, projected sales ... It is in the organization of this data that advancements to the bottom line are often realized i.e. the nuggets of gold are found. Defining what is important, properly cataloging the information, developing a comprehensive protocol to access and update this information and discerning how this data fits into the corporate venacular is basis of this data organization and may be the difference between moving ahead of the competition or being the one to fall behind.

Whenever we attempt to develop an Enterprise Rule Application, we must begin by harvesting the data upon which those rules are built. This is by no means an easy feat as it requires a thorough understanding of the business, industry, the players and their respective roles and the intent of the application. Depending upon the scope of this undertaking, it is almost always safe to say that no one individual is completely knowledgeable to all facets needed to comprise the entire application.

The intial stage of this endeavor is, obviously, to decide upon the intent of the application. This requires knowledge of what is essential, what is an add-on and which of all these requirements/options can be successfully implemented in the allotted period of time. The importance of this stage cannot be stressed enough; if the vision/goal cannot be articulated in a manner that all can understand, the knowledge tap will be opened to become the money drain. Different departments may compete for the same financial resources; management may be jockeying for their day in the sun; consulting corporations, eager to win the bid, may exaggerate their level of competency. These types of endeavors require those special skills of an individual or a team of very competent members to be/have a software architect, subject matter expert and business analyst.

Once the decision has been made and the application development stages have been defined, the next step is to determine which software development tools to employ. For the sake of this article, we will assume that the team has chosen an object oriented language such as Java and a variety of J EE components, a relationsional database and a vendor specific BRMS such as Blaze Advisor. Now, onto the point of this article.

When making a strategic cloud decision, organizations can follow either one of two ideologies: open or closed.

In the past, major software technologies have been widely accepted because an emerging market leader simplified the initial adoption.  After a technology comes of age, the industry spawns open alternatives that provide choice and flexibility, and the result is an open alternative that quickly gains traction and most often outstrips the capabilities of its proprietary predecessor.

After an organization invests significantly in a technology, the complexity and effort required steering a given workload onto a new system or platform is, in most cases, significant. Switching outlays, shifting to updated or new software/hardware platforms, and the accompanying risks may lead to the ubiquitousness of large, monolithic and complex ERP systems – reason not being that they offer the best value for an organization, but rather because shifting to anything else is simply – unthinkable.

There’s no denying that these are critical considerations today since a substantial number of organizations are making their first jump into the cloud and making preparations for the upsetting shift in how IT is delivered to both internal and external clientele. Early adopters are aware of the fact that the innovation brought about by open technologies can bring dramatic change, and hence are realizing how crucial it is to be able to chart their own destiny.