Rohith Perumalla | 2/16/2018
A quick look at Python.
Data analytics, sometimes known as analysis of data or data analysis, is the process of inspecting, cleansing, transforming, and modeling data with the objective of finding useful information, suggesting conclusions, and supporting decision-making. These technologies are often used to analyze large amounts of data (Big Data Analytics) in which the findings are used to make data driven decisions. There are various languages and libraries used to analyze data each have their own benefits and drawbacks. Some of the popular programming languages used to develop and build solutions to analyze data include: R, Python, Julia, Java, Hadoop/Hive, Scala, Kafka/Storm, Matlab, Octave, and Go.
Python is a a open source free language that has been around since the 1980’s; since then it has been used as an alternative for the expensive statistical packages such as Matlab and SAS. In recent years, Python has grown in popularity and has been used by large organizations like Google, Facebook, Bank of America, and many others. One of the main reasons that Python has grown so much in use is its simple language that is easy to to teach and use, and has been taught at many academic levels from middle schools to college level classes. Python’s main selling point comes from, its sophistication, practicality, and scalability.
While R is an amazing language to build models and to use for quickly sift through small amounts of data, when it comes to larger data sets and more intense calculation R can quickly go from being a fast swift efficient tool to a slow clunky machine. Thats where python steps in, python is seen to be a hybrid between R’s quick data mining abilities and more practical traditional programming languages. It’s hybrid nature has made it a go to language for many data analytic purposes, in recent years. “It’s the big one people in the industry are moving toward. Over the past two years, there’s been a noticeable shift away from R and towards Python, says Butler.”
Python also has a vibrant community in which developers and data scientists are adding to Python's functionality and improving it regularly. It is also the one of the most popular data analytics language being used by around 39% of data scientists. Pythons ability to efficiently traverse through large amounts of data and scale to larger platforms has been one of its largest selling points as more people in the industry move towards using Python as their primary data analytics language. “Bank of America uses Python to build new products and interfaces within the bank’s infrastructure, but also to crunch financial data. ‘Python is broad and flexible, so people flock to it,’ says O’Donnell.”
Considering all the benefits Python has from its practicality and scalability regarding going through data sets, effectiveness regarding designing and creating data analytics solutions, and its thriving ecosystem it is important that it is important to remember that it is a hybrid of fast data mining languages and practical traditional programming languages. Python’s speedy data analyzing capabilities and flexibility with data set sizes are especially effective when building solutions that work to interface various applications together and pull data from all of them. But even though Python is a great hybrid between R and traditional languages and is an ever-improving open source making it an always growing and improving tool; Python is “not the highest-performance language, and only occasionally can it power large-scale, core infrastructures, says Driscoll.” Regardless, of its inconsistency with supporting large scale applications, Python is great for data analytics due to its hybrid nature and open source benefits.
Hayes, Tyler. “The 9 Best Languages For Crunching Data.” Fast Company, Fast Company, 3 Apr. 2015
https://upload.wikimedia.org/wikipedia/commons/thumb/c/c3/Python-logo-notext.svg/2000px-Python-logo-notext.svg.png
