In this course you will continue work from Predictive Analytics 1, and be introduced to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining. Predictive modeling takes data where a variable of interest (a target variable) is known and develops a model that relates this variable to a series of predictor variables, also called features. Four modeling techniques will be used: linear regression, logistic regression, discriminant analysis and neural networks. The course includes hands-on work with Python, a free software environment with capabilities for statistical computing.
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...
