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The differences between Python and Ruby are very subtle and have to do with the history of each language. Python had more ties with the academic world and evolved and grew non-stop as it was adopted by more and more sectors. Ruby became enormously popular with the appearance of Ruby on Rails and is much more commonly used in website development.
The greatest contribution has been to demonstrate that a language that was interpreted and designed to be simple and expressive can be used to generate large software projects. In other words, Python and other comparable languages are not toys in the least, as was once thought. The result has been the introduction in the world of business software of tools that are more typical of the academic and free software environments.
An important factor in this regard was that the companies that used Python (Google, YouTube, Instagram) grew very quickly thanks to this new philosophy of development.
This is mainly due to the personal tastes of its creator, Guido Van Rossum. He may have taken into account that previous dynamic and interpreted languages, such as Lisp or Perl, sacrificed legibility for greater functionality, which made it very difficult to maintain programs written in those languages.
Julia is one of those next generation languages that have provided a new technological twist. The goal of these languages, which include Go and Rust, is to offer the expressive capacity of dynamic languages (Python, Ruby) and the speed of static languages, Java, C, C++).
During my doctorate, I worked with Python, which is very expressive and slower, and Fortran, which is very strict and powerful. To get an idea of what I mean by “powerful”, one of the codes we wrote in Fortran was capable of using nearly two million processors efficiently to simulate a turbulent flow. That is why I am personally very interested in Julia, which is the most science and engineering-oriented among next generation languages.
Julia is already perfectly functional for data analysis, even though it is only four years old. As mentioned in my blog, we are closer to fulfilling the promise of combining expressiveness with performance. This will obviously require some concessions, but that is perfectly understandable.
I honestly think that using a single programming language for everything is a mistake. Languages are tools and some are more capable of solving certain problems than others. The most important lesson is that there is a new generation of languages with enormous potential and the time has come to learn how to use them.
Some of the subjects dealt with at the PyData event, such as big data and machine learning, are of enormous technological interest at this time. Being a part of a worldwide network of conferences that have a common subject matter provides wider coverage. Also, all the content is in English, which attracts both speakers and participants from other countries.
These types of events are important to break through the isolation that has always slowed technological development in Spain.
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Startups and existing companies in the process of digitization need a new set of digital tools to help them transform their businesses and get more in tune with their customers' needs. If possible, they should develop frictionless systems that do not create barriers to entry for the business, as occurs with some banking solutions.
A good cash flow management ultimately leads to more informed and wise financial decision making. Keeping operations up-to-date, knowing the cash flows well and discovering where there may be inconsistencies are just some of the processes that must be carried out on a daily basis to achieve this.
Taking a customer through the entire buying process until it is formalized is an arduous journey and one that faces the constant possibility of the customer leaving. However, there are ways to make the buying decision happen if you are given facilities such as agile, secure financing.