Data analytics linked to storage, curation, management, and mining with attention to alternative methodological approaches.  The course will demonstrate various methods to explore how big data might be analyzed, stored, and retrieved.

Upon successful completion of this course, students will be able to:

• Use models and structures to depict large data sets within a data analytics environment.
• Apply statistical models to data using SPSS and related software programs.
• Explain, determine, debate, compare and contrast: predictive analytic issues, language issues, algorithms, analytic methods, curation issues, and applications to social networks.
• Create a tutorial guide to assist others in data analytic endeavors to include structuring data sets, using software to analyze data, and depositing and making accessible big data sets.
• Assess how different statistical models might be used to extract meaning from particular data sets.
• Understand how clustering and other statistical models can be used to extract meaning from data sets which will then lead to decision models on particular sub-groups.
• Specify how to manage and curate large sets within a particular application area.
• Address storage, security, and privacy issues as they apply to different types of large data sets.
• Implement appropriate frameworks to identify the overall model for a big data set, how such data might be analyzed, curated, placed in a repository to provide appropriate access points for data set retrieval.