Gaining a competitive advantage in today’s business environment increasingly demands that organizations know how to innovate. Creativity, continuous improvement, and the ability to turn ideas into action are critical to standing out above the rest. Specific topics will include: the innovation process, disruptive technologies, why plans are bad, and when NOT to listen to your customers. We will also apply our knowledge via an innovation simulation.
This course provides an introduction to the field of data and information visualization, a key sub-field in the area of data analysis and mining. Specific analytic topics covered include: tables & charting, multi-dimensionality of data, handling unstructured data, and advanced visualization tools and techniques.
The rapid expansion of data availability has made possible considerable advances in modeling for the purpose of prediction. Virtually all decisions, at least in part, depend on predictions of what will happen if something changes (either under our control or not). This course explores applications of a variety of current predictive modeling techniques to data. Included are multiple regression modeling, logistic regression, decision trees, random forests, neural networks, and simulation analysis. The emphasis will be on applied analysis, utilizing data from a wide variety of areas, including business, politics, socioeconomic conditions, health, sports and entertainment, etc. Students will build and compare predictive models, learn how to evaluate these models, and how to apply model results to improve decision making.
This course explores the topic of Marketing analytics which has grown significantly in recent years in response to the rapidly increasing supply of data generated by marketing campaigns, online sales, websites, social media, customer relationship management programs and integrated marketing communication campaigns. Through enhanced technology, more data are available than ever before. But marketers are faced with the dilemma of how to convert the massive amount of available data into usable information. In this course students will engage in the systematic study of these data which are employed, through the use of statistical analysis and technology, to improve decision making.
Big Data Analytics
This course introduces students to concepts behind the storage and analysis of the large and varied datasets that have become common in today’s business environment. This includes the use of distributed computing to store and analyze these datasets in an efficient manner. Students will be introduced to a variety of tools used to analyze large datasets and learn how to use these tools in appropriate contexts.
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