Quality DB Quality DB is an easyR module that works within the data lake layer It offers a web user interface that is connected to a powerful relational database The database is explicitly designed to fit the data coming from any kind of quality statistics analysis Quality DB is designed to help Minitab users,…
Run Monte Carlo simulations with Minitab
More and more frequently we see organizations make the mistake of mixing and confusing team roles on a data science or “big data” project – resulting in over-allocation of responsibilities assigned to data scientists. For example, data scientists are often tasked with the role of data engineer leading to a misallocation of human capital. Here the data scientist…
Da oggi Kiwi Data Science è consulente scientifica dell’Azienda Ospedaliera Gaetano Pini di Milano. Tema principale della collaborazione è l’applicazione di metodologie di data science allo studio di dati clinici. Testo della Convenzione
This video is about easyR, a framework by Kiwi Data Science, which is based on the R language and cloud technologies. easyR has been projected as a tool to develop highly customised tools for statistics and data science
Scopri cos’è la Data Science e in che modo stia rivoluzionando il modo di fare e ottimizzare il business
This is an example where data science and statistical analysis is superior to intuition. Here, intuition is misleading you into the wrong conclusions. By twin data points, I mean observations that are almost identical. In any 2- or 3-dimensional data set with 300+ rows, if the data is quantitative and evenly distributed in a bounded…
During the predictive modeling process, there are many places where it’s easy to make mistakes. Luckily, we’ve compiled a few here so you can learn from our mistakes and avoid them in your own analyses:
Data scientists are a rare breed. Part mathematician, part business professional and part computer programmer, these curious individuals play and discover in the world of big data, spotting trends and leading companies around the world toward lower costs, higher returns and better overall products.
Smart data scientists use data virtualization to integrate data from many diverse sources – logically and virtualized for on-demand consumption by different data analytical applications. For example, data virtualization is used to address challenges such as rogue data marts, business intelligence apps, enterprise resource planning and content systems and portals.