Anant Corporation Blog: Our research, knowledge, thoughts, and recommendations about building and managing online business platforms.
For developers in the field of Machine Learning, one of the most popular programming languages is Python. Python is neither the fastest language (easily overtaken by C and C++), nor is it necessarily the easiest language to learn (R and Matlab can have smaller learning curves). Then why is python used by 57% of Data Scientists and Machine Learning Developers and ranked first by the PYPL Index as one of the most popular programming languages today? As a programmer myself, I think it comes down to two things: simplicity of programming and the vast amount of libraries that Python offers.
In one of my earlier articles, I talked about why your company or organization should adopt Sitecore as your Experience Platform. It’s a good platform for users, content authors, and developers to create compelling and engaging digital experiences as well as collect information on website traffic. Machine learning and analytics in personalized content are two of the most compelling features of Sitecore. In today’s world, companies, particularly the Fortune 500, require real-time analytics to help drive stakeholder goals.
As per the Episerver website, the platform allows “You to easily manage content and marketing campaigns in one screen and no longer need to rely on IT to create new experiences.”
The product is a leader in Web Content Management Software (WCMS) and allows your team to control content over multiple digital channels such as web, mobile web, mobile native, and tablet. Once thought to be a small player, Episerver continues to climb up the Gartner WCMS ratings every year for its lightweight framework and agile upgrading features.
In today’s fast-paced technological society, we often hear about how artificial intelligence and machine learning are emerging as powerful techniques for accelerating data analytics amongst a wide variety of business platforms. But what exactly is Machine Learning, and how is it being implemented in platforms? How exactly can we harness the power that Machine Learning brings to create more robust tools to benefit clients?
This is the fourth part of our series covering some of our favorite Business Intelligence (BI) tools. Our previous three posts covered Metabase, Redash, and Tableau. In this post, we will cover DataDeck, an analytics tool that can serve you well if you are just starting out in analytics and want something easy to grasp for you and your team. Check out their online community right here!
In this series we will cover the four following applications: