Math Stats W/ Application I

Sampling distributions and the Central Limit Theorem, foundational techniques for point and interval estimation, and rigorous frameworks for hypothesis testing, including the Neyman-Pearson lemma, Likelihood Ratio tests, and Chi-square goodness-of-fit tests

Mathematical Theory of Data Science

Mathematical and statistical foundations of machine learning, focusing on concepts like linear algebra, probability theory, and optimization methods to justify algorithms used for clustering, classification, and regression tasks

Introduction to Scientific Computing and Problem Solving

Data analysis, data visualization, numerical approximations, and mathematical modeling or simulation.

Database Management Systems

Designing, building, and querying database systems, covering critical topics such as relational algebra, SQL programming, data normalization, indexing, transaction management, concurrency control, and query optimization.

Operating Systems

Processes, threads, memory management, file systems, concurrency, and resource scheduling.

Theory of Probability

Probability spaces, distributions, expectation, and limit theorems. Math-first approach.

Introduction to Linear Algebra

Vectors, matrices, linear transformations, and eigenvalues. Essential for ML and data work.

Data Structures and Algorithms

Arrays, linked lists, trees, graphs, sorting algorithms, and Big-O complexity analysis.

Software Design and Engineering

Full software development lifecycle — OOP design patterns, team projects, testing, and a capstone deliverable.

Introduction to Women's Studies

Feminist theory, gender studies, and intersectionality. Solid humanities elective.

Discrete Mathematics

Logic, proofs, set theory, combinatorics, and graph theory — the math that underpins CS.

Microcomputers

Assembly language, microprocessor architecture, and the basics of embedded systems programming.

CS II for Majors

Data structures and OOP in C++ — stacks, queues, trees, and recursion.

General Physics with Calculus II

Electricity, magnetism, waves, and optics — physics II with calculus.

Applied Differential Equations I

First and second order ODEs, Laplace transforms, and systems of equations.

Calculus III

Multivariable calculus — partial derivatives, multiple integrals, and vector calculus.

Digital Logic

Binary, Boolean algebra, logic gates, and combinational and sequential circuit design.

Blount Scholars Program — Foundation II

Continuation of the Blount seminar sequence — same small-cohort format, building on the interdisciplinary themes from Foundation I.

General Physics with Calculus I

Mechanics and kinematics — Newton's laws with calculus backing them up.

Calculus II

Integration techniques, sequences, and series. Polar coordinates make an appearance too.

CS I for Majors

First real programming course. Python fundamentals, problem decomposition, and intro algorithms.

Blount Scholars Program — Foundation I

First seminar in the Blount Scholars Program, a selective four-year living and learning community at UA with small cohort classes and an interdisciplinary, humanities-focused curriculum.

Calculus I

Limits, derivatives, and integrals. The mathematical backbone of pretty much everything that follows.

Engineering Foundations

Engineering problem-solving, design process, and technical communication — the how-to-think-like-an-engineer course.

General Chemistry

Atomic structure, chemical bonding, stoichiometry, and intro thermodynamics.