Education

Induction and Recursion, Searching and Sorting, Graph Theory, NP-Completeness, Network Flow, Randomized Algorithms, Hashing

Overview of computer security topics. Encryption, forms of malicious code, network security, ethics.

Fundamental properties of integers, polynomials, groups, rings, and fields

This course deals with current knowledge of the physical, chemical, and geological nature of the planets and their atmospheres derived in part from data provided by manned and unmanned space probes and other techniques. Similarities and differences between the earth and other planets are described and discussed within the role of planetary formation.

Introduction to operating systems. Computer system and operating system architectures, processes, inter-process communication, inter-process synchronization, mutual exclusion, deadlocks, memory hierarchy, virtual memory, CPU scheduling, file systems, I/O device management, security.

File organization and index structures; object-oriented database languages; the relational database model with introductions to SQL and DBMS; hierarchical models and network models with introductions to HDDL, HDML, and DBTG Codasyl; data mining; data warehousing; database connectivity; distributed databases; the client/server paradigm; middleware, including ODBC, JDBC, CORBA, and MOM.

An introduction to ordinary differential equations with emphasis on problem solving and applications.

Introduction to neural networks, hyperparameter tuning, computer vision, and ethics in neural networks.

Program generation, representation, and transformation, scripting, UNIX systems programming and file systems, threads and synchronization, network programming.

Real numbers, sequences, series, continous function, differentiation, Riemann integration.

Standard intro-level statistics class.

Web scraping and plotting with Python.

Digital logic, processors, memory, input/output, computer design.

Runtime analysis, data structures, trees, searching and sorting, heaps, hashing, strings, graphs.

Advanced, proof-based linear algebra.

Public speaking for engineering and science majors.

Seminar on career development in computer science fields.

More advanced data science using bash and SQL.

Discrete Mathematics.

Programming in C.

Systems of linear equations, finite dimensional vector spaces, matrices, determinants, eigenvalues and eigenvector applications to analytical geometry. Generally non-proof based.

A survey of the construction industry.

Introduction to data science in Python.

Introduction to programming in Java.

Planes, lines, and curves in three dimensions. Differential calculus of several variables; multiple integrals. Vector calculus.

Intermediate Spanish conversation and writing.

Introduction to various computer science related tools.

Introduction to data science in R.