This course is designed to introduce new Computer Science students to various programming tools which will aid them in their Computer Science classes, personal projects, and software development job experiences.

Presentations by corporate partners about careers in computer science. Presentations by faculty about careers in academia and research. Students learn about upper-division courses, tour research laboratories, and attend job fairs.

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 in-depth examination of relational database systems including theory and concepts as well as practical issues in relational databases. Modern database technologies such as object-relational and Web-based access to relational databases. Conceptual design and entity relationship modeling, relational algebra and calculus, data definition and manipulation languages using SQL, schema and view management, query processing and optimization, transaction management, security, privacy, integrity management.

Turing machines and the Church-Turing thesis; decidability; halting problem; reducibility; undecidable problems; decidability of logical theories; Kolmogorov complexity; time classes; P, NP, NP-complete; space classes; Savitch’s theorem, PSPACE-completeness, NL-completeness; hierarchy theorems; approximation theorems; probabilistic algorithms; applications of complexity to parallel computation and cryptography. Typically offered Fall Spring.

This course focuses on social, legal, and economic aspects of information security and privacy, also including ethics, policies, and human behavioral issues. The course covers the interactions between non-technological aspects of information security as well as relevant technological aspects. It focuses on how non-technological facets can inform and guide technological choices, and how technological choices can enhance or detract from the broader organizational and societal goals.

This course focuses on applied data mining, machine learning, data analytics techniques, and their application and relevance in information security. The course covers basic concepts of data mining and machine learning, computation platforms in support of big data analytics including Map-Reduce and Spark, machine learning algorithms such as classification trees, logistic regression, naive Bayes, k Nearest Neighbors, Support Vector Machines, Artificial Neural Networks (including Feed Forward, Convolutional, and Recurrence), the application of these algorithms to security tasks such as Spam/Phishing detection, malware detection, intrusion detection, and situational awareness. The future and potential role of applying machine learning techniques in information and data security is explored.

This seminar course engages a number of outside speakers who typically present information on the role of research in computer science, how the research components of computer science relate to each other, approaches to software development in industry, different types of application development paradigms, technological trends, and societal, ethical, and legal issues. The credit may be used only toward free electives.

Planes, lines, and curves in three dimensions. Differential calculus of several variables; multiple integrals. Introduction to vector calculus. Not open to students with credit in MA 17400 or 27100.

This course is the Honors version of MA 26100, Multivariate Calculus; it will also include a review of infinite series. The course is intended for first-year students who have credit for Calculus I and II. There will be a significant emphasis on conceptual explanation, but not on formal proof. Permission of department is required.

Introduction to linear algebra. Systems of linear equations, matrix algebra, vector spaces, determinants, eigenvalues and eigenvectors, diagonalization of matrices, applications. Not open to students with credit in MA 26200, 27200, 35000 or 35100.

Systems of linear equations, finite dimensional vector spaces, matrices, determinants, eigenvalues and eigenvector applications to analytical geometry.

This course provides a data-oriented introduction to applied statistics, covering exploratory data analysis, experimental design, probability distributions, simulation, sampling distributions, and the Central Limit Theorem. Students will learn the fundamentals of statistical inference, including confidence intervals and hypothesis tests for population means, paired and independent comparisons of means, analysis of variance, and regression. The course emphasizes hands-on experience with statistical software and is primarily intended for students majoring in the mathematical sciences.

Descriptive statistics; elementary probability; sampling distributions; inference, testing hypotheses, and estimation; normal, binomial, Poisson, hypergeometric distributions; one-way analysis of variance; contingency tables; regression. For statistics majors and minors, credit should be allowed in no more than one of STAT 30100, STAT 30301, STAT 35000, STAT 35500, STAT 50100, and in no more than one of STAT 50300 and STAT 51100.