The Business Analytics Institute offers a wide range of training, coaching and consulting services to help management improve their ability to take tough decisions.
If ethics is defined as shared values that help humanity differentiate right from wrong, the increasing digitalization of human activity shapes the very definitions of how we evaluate the world around us.
How can human and machine intelligence together be leveraged to bring tangible benefits to the business?
Let’s explore how the Random Forest algorithm is used in machine learning, its basic assumptions and use scenarios, as well as what precautions we should take when relying on this methodolog
What exactly are vector support machines, how do they work, how do they differ from other machine learning algorithms, and what are their use scenarios in data science?
In the digital economy, competitive advantage is tied to understanding the primacy of trust rather than that of data. Let’s explore the nature of the challenge, to what extent GDPR is the answer, and how companies can harness “Trust by Design” in preparing for a post-GDPR world.
What are the critical success factors in applying profitably Big Data and Artificial Intelligence to airport management?
Patterned after the structure of the human mind, do ANNs allow machines to think like humans? What exactly are ANNs, how do they work, how do they differ from other machine learning algorithms, and what are their use scenarios in data science?
Artificial Intelligence has potentially changed everything: both blurring the lines between human and mechanical processes and shifting the managerial focus from studying best practices to influencing consumer behavior. As a manager in 2019, what will you really need to know about Artificial Intelligence?
What sets these market leaders apart from the competition isn’t the volume of data or the sophistication of their analytics, but their capability to use data to enhance consumer trust. When companies in Europe and abroad evaluate GDPR in light of their digital strategies; they are missing a critical point – data will never be worth more than the confidence their stakeholders have in their data practices.
What is the probability that you are truly exceptional? Why would Bayes’ theorem be useful here, how is it applied to machine learning, what are its assumptions, and what precautions we should take when relying on this methodology?
