If you have a desire to help create and manage information systems and networks, the data management and analysis program may be worth considering. You can advance your career and stand out to potential employers.
This program will teach you to the best practices to manage enterprise data, how to prepare it for business use, and the various types of analysis that can be performed to help an organization increase its productivity, profitability, and performance.
At the end of the program, you will be able to design a database from user requirements, prepare data for analysis, perform database administrator functions, perform data mining and other statistical analyses, and more.
This program consists of five lower-division online data management and analysis courses for a total of 15 credit hours.
This course examines the basics of relational databases including basic terminology, database integrity, and normalization. The relational model is covered to appreciate database structure, integrity, and manipulation. Current relational database management systems will be explored and contrasted. Basic SQL programming assignments are included. This course is a replacement course for ITS407 as of the 2013-2014 Spring-A term. Students cannot receive credit for both these courses.
This course teaches students to design, implement, and use database management systems. Students gain a working knowledge of available software packages, concepts of query languages, software integration services, and security considerations. Students will also learn fundamentals of structured query language (SQL) in developing common queries and reports. (This course is also offered through CBE. Credits earned using this option will appear on transcripts with an "EX" suffix.)
The promise of cloud computing technology to provide unlimited utility computing and storage capacity to organizations is investigated. The various types of current cloud computing services and big data solutions offered by the major service vendors are studied. The challenges of managing "big data” are reviewed, and the relationships of cloud computing, big data, and data mining are examined. Recommended Prior Course: ITS410.
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. Prerequisite: MIS445. Course not eligible for Prior Learning Assessment (PLA) credit.
Information Technology majors will take ITS325 in place of ITS410.