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If you want to upgrade your data analysis skills, which programming language should you learn? For a growing number of people, data analysis is a central part of their job. Increased data availability, more powerful computing, and an emphasis on analytics-driven decision in business has made it a heyday for data science.  R and Python are the  two most popular programming languages used by data analysts and data scientists. Both are free and open source, and were developed in the early 1990s—R for statistical analysis and Python as a general-purpose programming language. For anyone interested in machine learning, working with large datasets, or creating complex data visualizations, they are godsends. But which of these programs is best to learn? As a former data analyst, it is the question I have been asked more than any other in my professional life. Though you could just try to learn both R and Python, each requires a significant time investment—particularly if you have n

Nowada ys , the amount of biological data av ailable online has proliferated, but this has been accompanied by enormous challenges arising from the need to integrate and connect related information from different sources. Common problems include locating resources, differing data formats, am- ambiguity and duplication, relationships between data and the sheer v olume and granularity of the information. As y et, there is no standard memorization and query format for this kind of data, so each resource usually requires a different approach to be properly handled. we introduce BioGrakn, based on GRAKN.AI  which is a deductiv e database in the form of a knowledge graph, allowing complex data modelling, verification, scaling, querying and analysis. The database behind GRAKN.AI uses an ontology to facilitate the modelling of extremely complex data sets, functioning as a data schema constraint to guarantee information consistency . GRAKN.AI stores data in a wa y tha

WSGI- Web Server Gateway Interface:  WSGI, authored by Philip J Eby and published on December 7, 2003, is a Web Server Gateway Interface, a specification that explains how a web server communicates with web applications, and how the applications can be chained together to generate requests. There was one major flaw in Python application, and that was negative interoperability. It was designed for only one FastCGI, mod_Python, CGI, or other API of a specific web server, and they didn’t allow for interoperability. Unfortunately mod_Python, an Apache module, was not an official specification, and there was little security. Developers needed a new solution to combat the execution of Python web application. This was the area where WSGI figured in. It had a standard interface for routing web apps and frameworks to web servers. The framework traces its origins to CGI or Common Gateway Interface, and was in use in the early days of the internet. The success of CGI was because it could wo

Django  Django is a Python-based free and open-source web framework, which follows the model-view-template architectural pattern. It is maintained by the Django Software Foundation, an independent organization  Design your model Although you can use Django without a database, it comes with an  object-relational mapper  in which you describe your database layout in Python code. The  data-model syntax  offers many rich ways of representing your models – so far, it’s been solving many years’ worth of database-schema problems. Here’s a quick example: mysite/news/models.py from django.db import models class Reporter ( models . Model ): full_name = models . CharField ( max_length = 70 ) def __str__ ( self ): return self . full_name class Article ( models . Model ): pub_date = models . DateField () headline = models . CharField ( max_length = 200 ) content = models . TextField () reporter = models . ForeignKey (

Python frameworks can be divided into a few areas as Python is a very diverse language and can be used in various fields. Each of these fields has its own frameworks, some of which are more popular than others. One of the most popular fields Python is applied in is web development, which we will focus on today. The presented frameworks can be broken down into three categories:  Full Stack Frameworks Micro Frameworks Asynchronous Frameworks The web frameworks presented above are merely a small chunk of a bigger and broader family of Python frameworks. Each solution has its strong and weak points and, as with everything else, there is no perfect match that will solve every given task. The most important questions that one has to answer when choosing the framework are dictated by problems that need to be solved. If extensive support on the server and browser side is required, then a full-stack framework might be a good choice.  For smaller projects