As part of our C++ Tutorials series, here is a tutorial on the tricks of the trade for using C++ I/O.  Keep in mind that an application without I/O is just a black box; no communcation is taking place. 

Tricks and Tips for using C++ I/O

We’re often asked by companies about how they can get the most value from Agile/Scrum practices. More specifically, they want to know if they are being as effective as best they possibly can be by using the Scrum framework for their explicit needs.

The other objective for individuals is determining if it necessary to be certified in order to be effective in the Agile Scrum world?   In short, a good Scrum Master must understand four things: the business they work in, the technology they work with, the Agile and Scrum principles, and, most importantly, people!  Based on these facts, Scrum Master Certification is not enough – real life experience and a bit of soft skills should be part and parcel of their training. For organizations, the main goal is to understand industry best practices when adopting and applying agile principles, to build strong teams, understand and distill business needs into software requirements.

In terms of getting a good grip on training for Agile/Scrum, one can opt to pursue a certification in Scrum (CSM) Certified Scrum Master for personal reasons or for a job requirement. Or, one can simply opt to learn the benefits and pitfalls of the methodology and decide the best approach for them.

There are different ways to get started with Agile training. Below are two of the most common paths to Agile our students take.

A string in Python is enclosed in either single or double quotes.  Therefore, either one does the trick.  A common practice is to place single words with no characters that can be interpolated in single quotes and multi-word strings that contain interpolated characters in double quotes.  This may be a carry over from Perl where interpolated characters are in double quotes. 

If you do not want to interpolate a string, use a raw string ... r"\n".  With the exception of the last print statement, each of the print statements prints hello on a separate line from how are you?.  They are great for regular expressions.

Finally, triple double quotes """ some message about a function or class ... """ are used for docstrings.

print "hello \n how are you?"
print 'hello \n how are you?'
print r"hello \n how are you?"

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.