CISS 218 Introduction to Big Data and Analytics

This course provides an introductory foundation in big data and analytics necessary to apply emergent proprietary and open-source analytic tools to quickly and cost-effectively analyze complex data sets. Big data is an evolving term to describe a wide variety of unstructured or semi-structured contemporary data types resulting from cloud, social and mobile computing (e.g. Facebook, Twitter, email, SMS, location, etc.), and as a result big data represents an extreme volume of data. Big data is too large and costly to analyze with traditional relational database and statistical methods thereby requiring new and evolving approaches to data analysis that include machine learning, distributed cloud computing and storage (e.g. Hadoop) and statistical computing (e.g. R) solutions. The goal of big data analytics is to identify patterns and trends in the data, facilitating increased understanding of complex data sets and human behavior necessary for quick decision making, cost reduction, identification of new opportunities and continuing increases in stakeholder satisfaction.
Pre-requisite(s): CMPT 115 Introduction to Business Analytics with Microsoft Excel ; CISS 100 Introduction to Computing and Information Sciences ; CISS 110 Programming and Logic I or by permission of department chair.
Offered: Fall, Spring, Summer, DL
Credits: 3
Contact Hours:
Lecture: 3