APPM 1350 - Calculus 1 for Engineers
Primary Instructor
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Fall 2018 / Fall 2019 / Fall 2020 / Summer 2021 / Fall 2021
Topics in analytical geometry and calculus including limits, rates of change of functions, derivatives and integrals of algebraic and transcendental functions, applications of differentiations and integration. Students who have already earned college credit for calculus 1 are eligible to enroll in this course if they want to solidify their knowledge base in calculus 1. For more information about the math placement referred to in the Enrollment Requirements, contact your academic advisor. Degree credit not granted for this course and APPM 1345 or ECON 1088 or MATH 1081 or MATH 1300 or MATH 1310 or MATH 1330.
APPM 1360 - Calculus 2 for Engineers
Primary Instructor
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Spring 2018 / Spring 2019 / Spring 2020 / Spring 2021
Continuation of APPM 1350. Focuses on applications of the definite integral, methods of integration, improper integrals, Taylor's theorem, and infinite series. Degree credit not granted for this course and MATH 2300.
APPM 4440 - Undergraduate Applied Analysis 1
Primary Instructor
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Fall 2018 / Fall 2019
Provides a rigorous treatment of topics covered in Calculus 1 and 2. Topics include convergent sequences; continuous functions; differentiable functions; Darboux sums, Riemann sums, and integration; Taylor and power series and sequences of functions.
APPM 7400 - Topics in Applied Mathematics
Primary Instructor
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Fall 2020 / Fall 2021
Provides a vehicle for the development and presentation of new topics with the potential of being incorporated into the core courses in applied mathematics. May be repeated up to 6 total credit hours.
DTSA 5001 - Probability Theory: Foundation for Data Science
Primary Instructor
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Summer 2021 / Fall 2021
Probability Theory covers the foundations of probability and its relationship to statistics and data science. Calculate a probability, independent and dependent outcomes, and conditional events. Understand discrete and continuous random variables and see how this fits with data collection. Learn Gaussian (normal) random variables and the Central Limit Theorem and understand it's fundamental importance for statistics and data science.
