This article was posted by Bob E. Hayes on Customer think. Bob, PhD is Chief Research Officer at Appuri. He a scientist, blogger and author on CEM… Read More »Machine Learning Skills Among Data Scientists
Summary: We are swept up by the rapid advances in AI and deep learning, and tend to laugh off AI’s failures as good fodder for… Read More »There’s Nothing Like a Huge Public Failure to Boost Interest in AI
We expect that data scientists and analysts should be objective and base their conclusions on data. Now while the name of the job implies that… Read More »How to Lie with Data
When I first started out learning about machine learning algorithms, it turned out to be quite a task to gain an intuition of what the… Read More »Learn under the hood of Gradient Descent algorithm using excel
Data modeling has been a fixture of enterprise architecture since the 1970’s with ANSI defined conceptual, logical, and physical data schema. As data models developed,… Read More »Streaming Event Modeling
Background Last week, I spoke at the Swiss Mobile Association. The event was held at one of the oldest cross-functional research institutes Gottlieb Duttweiler Institute… Read More »The applications of Artificial Intelligence (AI) in the Telecoms industry
Here are a few challenges for the mathematically inclined – most data scientists are. This is just fun problems if you have some time to… Read More »Seasons in Binary Star Planetary Systems
Analytics has taken world by storm & It it the powerhouse for all the digital transformation happening in every industry. Today everybody is generating tons… Read More »A to Z of Analytics
This article was contributed by Statistical Future. Whether you want it to or not, credit and its availability plays a major role in everyone’s life,… Read More »Ranking All 50 States by Average Credit Score of its Citizens
In this continuation of “Hybrid content-based and collaborative filtering recommendations wi…” I will describe the application of the {ordinal} clm() function to test a new,… Read More »Hybrid content-based and collaborative filtering recommendations with {ordinal} logistic regression (2): Recommendation as discrete choice
