Introduction to Machine Learning and Analytics

Catalog description

This course provides an introduction to machine learning and analytics using contemporary proprietory and open-source tools to quickly and cost-effectively analyze complex data sets. Data analysis will include both traditionally structured and emergent semi-structured or unstructured data types resulting from cloud, social and mobile computing (e.g. Facebook, Twitter, e-mail, SMS, location, etc.) often referred to as “big data”. Big data represents an extreme volume of data that is too large and costly to analyze with traditional relational database and statistical methods thereby requiring new and evolving approaches to data analysis that includes machine learning (e.g. Scikit-learn, Google TensorFlow), distributed cloud computing and storage (e.g. Hadoop) and statistical computing (e.g. R) solutions. The goal of this machine learning and analysis is to identify patterns and trends in data, facilitating an increased understanding of complex data sets necessary for quick decision making cost reduction, identification of new opportunities and continuing increases in stakeholder satisfaction. Pre-requisite(s): CISS 100 Introduction to Computing and Information Sciences and CMPT 115 Introduction to Business Analytics with Microsoft Excel and ( CISS 109 Python Programming or CISS 110 Programming and Logic I ) with final grades of “C” or better or by permission of Department Chairperson.