This course provides an overview of the tools and techniques for analyzing website data. The course will focus on interpreting website data to make decisions about performance. Topics included are clickstream analysis, measuring website success and performance, website strategy testing, keyword analysis, and social media and blog analysis. (This course is also offered through SSA. Credits earned using this option will
appear on transcripts with an “S” suffix.)
The course covers the basics of relational databases, including basic terminology and concepts, database integrity, and normalization. The relational model will be examined in detail in order to appreciate database structure, integrity, and manipulation. Current relational database management systems will be explored and contrasted, as will basic relational database design and SQL programming. This course is a replacement for ITS407 as of the 2013-2014 Spring-A term. Students cannot receive credit for both these courses.
Statistics in Business Analytics
A study of data analysis, data production, and statistical inference. Areas of study include: surveys and designed experiments, randomization, causation, regression, and inference using hypothesis tests. This course also explores using statistical methods for the analysis of, data for an enterprise performance and quality, effectiveness, and marketability. Statistical software will be utilized to conduct a predictive analysis, analyze the results, and document the findings. The preparation of input data for analysis from a relational database using SQL is also performed.
Investigate various statistical approaches used for data mining analyses. The preparation of data suitable for analysis from an enterprise data warehouse using SQL and the documentation of results is also covered. A simple data mining analysis project is performed to reinforce the concepts. (This course is also offered through CBE. Credits earned using this option will appear on transcripts with an “EX” suffix.)
Data Science Foundations
This course provides an overview of the tools and techniques for analyzing data using statistics, R Programming, and SQL. Topics include data storage, linear regression, classification, linear models, tree-based learning, R programming, and SQL basic commands. (This course is also offered through SSA. Credits earned using this option will appear on transcripts with an “S” suffix.)