# Tag: Bart Baesens (11)

**New Book: Credit risk analytics, The R Companion**- Mar 16, 2018.

Credit risk analytics in R will enable you to build credit risk models from start to finish, with access to real credit data on accompanying website, you will master a wide range of applications.**New E-learning course: Profit-driven Business Analytics**- Jan 8, 2018.

The e-learning course on profit-driven business analytics presents a toolbox of advanced analytical approaches that support subsequent cost-optimal decision making.**Web Scraping for Data Science with Python**- Dec 6, 2017.

We take a quick look at how web scraping can be useful in the context of data science projects, eg to construct a social graph based of S&P 500 companies, using Python and Gephi.**E-learning courses on Advanced Analytics, Credit Risk Modeling, and Fraud Analytics**- Apr 18, 2017.

These online courses, developed by Prof. Bart Baesens and SAS, include videos, case studies, quizzes, and focus on focusses on the concepts and modeling methodologies and not on specific software.**New e-learning course: Fraud Analytics using Descriptive, Predictive and Social Network Analytics**- Jan 31, 2017.

This online course teaches how to find fraud patterns from historical data using descriptive analytics, and social network learning.**Self-Paced E-learning course: Advanced Analytics in a Big Data World.**- Mar 15, 2016.

The course covers the entire analytics process, from data preprocessing to advanced modeling, including ensemble methods (bagging, boosting, random forests), neural networks, SVMs, Bayesian networks, social networks, monitoring and more.**Self-Paced E-Learning course: Credit Risk Modeling**- Mar 8, 2016.

The course covers basic and advanced modeling, including stress testing Probability of Default (PD), Loss Given Default (LGD ) and Exposure At Default (EAD) models.**Online course: Credit Risk Modeling**- Oct 7, 2015.

The course covers basic and advanced modeling, including stress testing Probability of Default (PD), Loss Given Default (LGD ) and Exposure At Default (EAD) models.**Self-Paced E-learning course: Advanced Analytics in a Big Data World**- Oct 6, 2015.

The course covers the entire analytics process, from data preprocessing to advanced modeling, including ensemble methods (bagging, boosting, random forests), neural networks, SVMs, Bayesian networks, social networks, monitoring and more.**Poll: Machine Learning APIs**- Apr 4, 2015.

Poll from Bart Baesens at KU Leuven asks about your usage of Machine Learning APIs and other predictive analytics tools.**New Book: Analytics in a Big Data World – The Essential Guide to Data Science**- May 13, 2014.

For organizations looking to enhance their capabilities via data analytics, this book is the go-to reference for applying Data Science to make the right business decisions.