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Data Science, Artificial Intelligence, and Machine Learning are becoming buzzwords more and more. The terms appear to be valuable according to the trendwatchers, but most often people do not even know what the terms mean, let alone that they use them.

We at Rockfeather help people and organizations to understand and retrieve value from the buzzwords.

In collaboration with Finext and The Hague University of Applied Sciences, Rockfeather conducted a survey among our customers and other data enthusiast. The survey consists of twenty questions that investigate the data maturity, the presence of a data-mindset and how they reach and plan to reach their data ambitions and goals.

What is the optimal set of processes to allocate our resources? What process and steps do we have? These questions seem like questions that every company should answer easily. Unfortunately, a lot of companies are not able to do so. Their processes are just too complex because of changing price constraints, or other variables. There is a solution: Process optimization.

This year’s Magic Quadrant for Data Science and Machine Learning Platforms scored six Data Science and Machine Learning (DSML) Platforms as leaders. The amount of leaders has remained the same, compared to last year’s number of leaders. But has the composition of the leading area changed?

For years, Gartner’s Magic Quadrant for Analytics and Business Intelligence has been the leading report in the world of data visualization. However, many people only look at the 2×2 matrix without going into the details of the report.

Power BI and Tableau are both leading data visualization solutions. Comparing the two options can be a daunting task. There is a lot of marketing noise from parties that sell or work with just one of the two solutions. This will not help you. 

You’ve probably already heard of data science, artificial intelligence and machine learning. But how do you avoid jumping on the data science bandwagon, like many other companies do? And where do you start? In this introduction to data science, we will explain data science in easy-to-understand theories. Start learning today!

In this blog, we are going to use data science as a method to predict housing prices and sales. If you make proper use of predictions, it can set the standard for profits and growth. We will explain regression models by presenting two examples: housing prices and expected sales.

Preventing customer churn is one of the top priorities of companies. You might wonder how data science and machine learning are related to this. In this blog we will tell you all about how to decrease customer churn using data science. Take the next step with data science, using our explanations of classification models and a useful churn case!