The first course for computer science majors and anyone seeking a rigorous introduction. Develops computational problem-solving skills by programming in the Python language, and exposes students to variety of other topics from computer science and its applications. Carries MCS divisional credit in CAS.

Covers advanced programming techniques and data structures. Topics include recursion, algorithm analysis, linked lists, stacks, queues, trees, graphs, tables, searching, and sorting. Carries MCS divisional credit in CAS.

Representation, analysis, techniques, and principles for manipulation of basic combinatoric structures used in computer science. Rigorous reasoning is emphasized.

Fundamental concepts of computer systems and systems programming. Hardware fundamentals including digital logic, memory systems, processor design, buses, I/O subsystems, data representations, computer arithmetic, and instruction-set architecture. Software concepts including assembly language programming, operating systems, assemblers, linkers, and systems programming in C.

Examines the basic principles of algorithm design and analysis; graph algorithms; greedy algorithms; dynamic programming; network flows; polynomial-time reductions; NP-hard and NP-complete problems; approximation algorithms; randomized algorithms.

Quantum physics as a powerful computational paradigm. Quantum bits (qubits), qubit operations and quantum gates, computation, and algorithms. Computational complexity classes, and efficiency of classical vs. quantum computers. Quantum Fourier transform and Shor's factorization algorithm. Physical implementation of quantum computation.

Limits; derivatives; differentiation of algebraic and transcendental functions. Applications to maxima, minima, and convexity of functions. The definite integral; the fundamental theorem of integral calculus. Carries MCS divisional credit in CAS.

Logarithmic, exponential, and trigonometric functions. Sequences and series; Taylor's series with the remainder. Methods of integration. Calculus I and II together constitute an introduction to calculus of a function of a single real variable. Carries MCS divisional credit in CAS.

Survey of mathematical and computational methods used in modern theoretical physics. Vectors, fields, differential and integral vector calculus. Matrices, matrix transformations, rotations, eigenvalues and eigenvectors. Function spaces, orthonormal functions, Fourier analysis, bras and kets. Basics of ordinary and partial differential equations with solutions by series and numerical methods. Complex variables and analytic functions. Scientific programming in python, computational visualization and numerical methods complementing each of the analytic topics.

The laws of thermodynamics, statistical and information basis of thermodynamics, ensemble theory, equilibrium statistical mechanics and its application to physical systems of interest, irreversibility.

Undergraduate-level introduction to computer programming and methods used to formulate and solve physics problems on the computer. Also touches on more advanced topics such as parallel computing and graphical visualization.