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Course Criteria
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3.00 Credits
The Federal Aviation Administration-approved Aircraft Maintenance Technician program (14 CFR 147) requires the successful completion of a minimum of 1,900 hours of study (43 university credit hours at SUU) divided into three subject areas (General, Airframe, and Powerplant) and the successful completion of three FAA written, oral, and practical exams (General, Airframe, and Powerplant). This course is one of a group of five courses that are designed to meet the regulatory and content requirements of Federal Aviation Regulation 14 CFR 147 for the General portion of the A&P license. Content: maintenance of aircraft gas turbine engine instrument systems, operation and maintenance of aircraft gas turbine engine fire detection and control systems, operation of aircraft propellers, and the operation and maintenance of aircraft propeller controls. (As Needed) [Graded (Standard Letter)] Prerequisite(s): AMTG 1200 and AMTG 1300 and AMTG 1400 - Prerequisite Min. Grade: C Registration Restriction(s): None
Prerequisite:
AMTG 1200 A AMTG 1300 A AMTG 1400
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3.00 Credits
An introduction to the management and organization of data, with a particular emphasis on the current database tools for industry analytics. Students will learn how to develop a successful data strategy to create business value from data assets. Topics include the logical structure of databases as well as the methods and technology for efficient data storage, retrieval, and presentation. (Fall) [Graded (Standard Letter)] Registration Restriction(s): None
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3.00 Credits
An introduction to data science methods in business, finance, and economics. Includes an introduction to an appropriate programming language for data manipulation and modeling. Provides an overview of descriptive, predictive, and prescriptive methods in data analytics. (As Needed) [Graded (Standard Letter)] Prerequisite(s): MATH 1040 and (MATH 1100 or MATH 1210 or ECON 2500) - Prerequisite Min. Grade: D- Registration Restriction(s): None
Prerequisite:
MATH 1040 ( A MATH 1100 O MATH 1210 O ECON 2500 )
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3.00 Credits
A continuation of ANLY 4100. Covers the primary analytic techniques involved in data mining, including logistic regression, decision trees, kNN, Naive Bayes, and other es. Introduces unsupervised learning methods. Builds on the programming skills established in Data Analytics I. (As Needed) [Graded (Standard Letter)] Prerequisite(s): ANLY 4100 - Prerequisite Min. Grade: D- Registration Restriction(s): None
Prerequisite:
ANLY 4100
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3.00 Credits
An introduction to data science methods in business. Includes an introduction to programming for data manipulation and modeling. Provides an overview of descriptive, predictive, and prescriptive methods in data analytics from a practical business perspective, with a focus on direct application to current data-driven decision-making. (Fall - 1st Session, Spring - 2nd Session) [Graded (Standard Letter)] Registration Restriction(s): MAcc or MBA students only
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3.00 Credits
A continuation of ANLY 6010. Covers the primary analytic techniques involved in data mining for business problem-solving, including advanced regression, decision trees, kNN, and others. Introduces unsupervised learning methods. Builds on the programming skills established in Business Analytics I. (Fall - 2nd Session, Summer - 1st Session) [Graded (Standard Letter)] Prerequisite(s): ANLY 6010 - Prerequisite Min. Grade: C Registration Restriction(s): MAcc or MBA students only
Prerequisite:
ANLY 6010
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3.00 Credits
An introduction to the methods and tools for database management and data visualization with a particular focus on industry analytics. Database topics include the logical structure of databases as well as the methods and technology for efficient data storage, retrieval, and presentation. Visualization topics include an overview of the Tableau software for data visualization and discussion of data visualization principles. Emphasizes skills in retrieving and presenting data for business presentation. (Spring - 1st Session, Summer - 2nd Session) [Graded (Standard Letter)] Registration Restriction(s): MAcc or MBA students only
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3.00 Credits
An introduction to data science methods in business, finance, and economics. Includes an introduction to an appropriate programming language for data manipulation and modeling. Provides an overview of descriptive, predictive, and prescriptive methods in data analytics. (As Needed) [Graded (Standard Letter)] Prerequisite(s): MATH 1040 and (MATH 1100 or MATH 1210 or ECON 2500) - Prerequisite Min. Grade: D- Registration Restriction(s): None
Prerequisite:
MATH 1040 ( A MATH 1100 O MATH 1210 O ECON 2500 )
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3.00 Credits
A continuation of ANLY 6100. Covers the primary analytic techniques involved in data mining, including logistic regression, decision trees, kNN, naive Bayes, and others. Introduces unsupervised learning methods. Builds on the programming skills established in ANLY 6100. (As Needed) [Graded (Standard Letter)] Prerequisite(s): ANLY 6100 - Prerequisite Min. Grade: C- Registration Restriction(s): None
Prerequisite:
ANLY 6100
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3.00 Credits
This course covers prevalent methods and tools for data processing and visualization. Students are introduced to both the Python and R programming languages for processing, analyzing, and visualizing data. In addition, the course includes an overview of the Tableau software for data visualization. Course emphasis is on mastering basic software functionality and developing intermediate to advanced skills in working with and presenting data. (Fall) [Graded (Standard Letter)] Registration Restriction(s): Master of Science in Business Analytics majors only OR instructor permission required
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