With an ever increasing rise in the use of employment testing, certification testing and need to get a degree, I thought I would write this basic guide on how to study for exams.  Although it was originally written with the college student in mind, the fundamentals still apply to all of us in the workforce.

There are few things that strike terror into the hearts of students more than exam day, particularly if they have inadequate study skills. Perhaps these students study for hours and hours, only to discover that by exam time they've forgotten everything they've read. Below are a few study tips to help struggling students remember the information they've reviewed for their exams. 

-Use memory tricks. There are a number of memory tricks that you can use to help you remember large amounts of information. For example, the use of acronyms (such as Roy G Biv to remember the colors of the rainbow) can be very helpful. In addition, you can use visualization techniques, similes, and songs to assist you in recalling your study material.

-Don't cram. Your brain requires time to absorb facts. If you know about a test in advance, start studying right away for a little bit every day, ramping up your efforts as the exam approaches.

-Take frequent breaks while studying. It may seem counter-intuitive that spending less time studying might actually help you remember more of what you've read. But taking appropriately timed study breaks will keep your mind fresh and make sure you don't stress too much.

-Write it out. For many people, writing information down as they read it is the best way to learn it. Don't just write exactly what you read, however; by rewording the information or even drawing a picture or diagram you commit it to your memory in more than one way, allowing you to remember it easier later.

-Teach it to a friend. To remember information, you have to understand it. And in order to teach information, you need to understand it as well. Nothing tests your ability to recall facts better than teaching them to another person. Find a friend unfamiliar with your study material and teach them a lesson in the subject.

-Get plenty of sleep the night before the exam. Finally, be sure to get a good night's rest the night before you take the exam. Falling asleep at your desk will accomplish nothing. This will help you be more alert while you are taking your test, and will allow you to retain more information.

 

As developers we are overwhelmed with the number of language choices made available to us. It wasn't so long ago that C and it's object oriented sibling C++ where the mainstay of any programmer. Now though we have languages which make certain tasks so easy and simple that we simply cannot afford to ignore them.

In this article we are going to look at the overall differences between Python, Perl and TCL. All formidable and worthy in their own right, but each one has been designed to suit a specific programming need.

1)– Perl is the most mature out of the three languages we are looking at in this article. It was originally designed for processing textual data, and it does so extremely well. Of course Perl has grown over time and can be used for a multitude of different programming scenarios.

Companies have been collecting and analyzing data forever, pretty much.” So what’s really new here? What’s driving the data-analytics revolution and what does it mean for those that choose to postpone or ignore the pivotal role big-data is currently having on productivity and competition globally?

General Electric chairman and CEO Jeff Immelt explains it best when stating that “industrial companies are now in the information business—whether they like it or not.”  Likewise, digital data is now everywhere, it’s in every industry, in every economy, in every organization and according to the McKinsey Global Institute (MGI), this topic might once have concerned only a few data geeks, but big data is now relevant for leaders across every sector as well as consumers of products and services.

In light of the new data-driven global landscape and rapid technological advances, the question for senior leaders in companies now is how to integrate new capabilities into their operations and strategies—and position themselves globally where analytics can influence entire industries. An interesting discussion with six of theses senior leaders is covered in MGI’s article, “How companies are using big data and analytics,” providing us with a glimpse into a real-time decision making processes.

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.