Python is a powerful programming language that is easy to learn and easy to work with, but it is not always the fastest to run?especially when you?re dealing with math or statistics. Third-party libraries like NumPy, which wrap C libraries, can improve the performance of some operations significantly, but sometimes you just need the raw speed and power of C directly in Python.
Click to learn more about author Ben Lorica. Big Data, AI, Blockchain?the list of enterprise tech buzzwords rages on. With all the hype, it can be difficult for business leaders to determine which technologies to commit their money and minds to in the new year. Because of this, companies often move fast into new technologies [?]
Modern business applications bring together many strands of development. You?re no doubt most familiar with n-tier applications, building on decades of programming skills and techniques, linking UI to code and to data. They?re familiar and easy to understand. But that all changes when you start to add new technologies and approaches, constructing massively scalable distributed computing platforms that take advantage of large amounts of data and machine learning.
Amazon Web Services has added Google?s Go language (Golang) to the roster of supported language on its AWS Lamdba serverless computing platform. Also added is support for Microsoft?s .Net Core 2.0 when developing in the C# language.
Of the many use cases Python covers, data analytics has become perhaps the biggest and most significant. The Python ecosystem is loaded with libraries, tools, and applications that make the work of scientific computing and data analysis fast and convenient.
Click to learn more about author Ashish Trikha. Love it or hate it, but the Internet of Things (IoT) is not going away anytime soon. In recent times, the Internet of Things has permeated our day to day lives to improve the way we live, the way we work, and the way we entertain. In [?]
Click to learn more about author Steve Miller. I did a short consulting gig last year with a company that was building an analytic app driven largely from a web-based data repository. The data were stored as a collection of SAS data sets which ?customers? would subset by uploading SAS data step and proc SQL [?]
According to the 2017 Stack Overflow Annual Developer Survey, Python has emerged as the language developers want to use more than any other this year for building data-intensive projects, which can range from automating robots to fueling internet of things (IoT) networks with sensor intelligence. Once an obscure scripting language, Python now powers some of the most complex applications on the cloud.
by Angela Guess A recent press release states, ?Anaconda, Inc., the most popular Python data science platform provider, today announced it is partnering with Microsoft to embed Anaconda into Azure Machine Learning, Visual Studio and SQL Server to deliver data insights in real time. Microsoft and Anaconda will partner to deliver Anaconda for Microsoft, a [?]
by Angela Guess According to a recent press release, ?Anaconda, the Python data science leader, today introduced Anaconda Enterprise 5 software to help organizations respond to customers and stakeholders faster, deliver strategic insight for rapid decision-making and take advantage of cutting edge machine learning. Building on the world?s most popular Python data science platform with [?]
Mention machine learning, and many common frameworks pop into mind, from ?old? stalwarts like Scikit-learn to juggernauts like Google?s TensorFlow. But the field is large and diverse, and useful innovations are bubbling up across the landscape.
In IT, it?s hard to completely avoid writing programs, even if they?re just small ones. But not all IT folks also want to be developers, and the overhead involved in learning and getting proficient with a language can be huge. For many languages, it takes a lot of code?and a lot of time?to write code to do even simple things.
I just completed the annual "maintenance" on my little stock market indexes returns "app". I've supported a variant of the script for seven years, changing it pretty significantly year to year. The 2016 version was half python and half R, but this year I opted for R entirely. Who knows, maybe next will be all-in python.