In this course you will be introduced to basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining. You will cover two core paradigms that account for most business applications of predictive modeling: classification and prediction. You will also study commonly used machine learning techniques and learn how to combine models to obtain optimal results. This course includes hands-on work with Python, a free software environment with statistical computing capabilities.
Dr. Peter Gedeck
Peter Gedeck is at the forefront of the use of data science in drug discovery. He is a Senior Data Scientist at Collaborative Drug Discovery, which offers the pharmaceutical industry cloud-based software to manage the huge amount of data involved in the drug discovery process. Drug discovery involves the exploration and testing of huge numbers of molecule combinations, and much of that testing takes place analytically, hence the need for robust software to handle the data and provide a framework for analyzing it. Peter's specialty is the development of machine learning algorithms to predict biological and physicochemical properties of drug candidates. Prior to this, he worked for twenty y...
