Data Science

Introduction to Python Programming

The basic components of Python programming language are studied. Topics include variables and types, expressions, statements, conditions with selection and looping, functions. Concepts and basics on lists, tuples, sets and dictionaries are also presented. Prerequisite: placement level H, A, B, or C. Students with credit for CS 112 or higher by consent only. Offered every fall.

Computer Programming I

An introduction to computer programming using a modern programming language. Topics include the program development process, structured programming, data types, variables and expressions, input/output, assignment, selection, looping, functions, and arrays. Prerequisite: placement level H, A, or B, or MATH 104 with a grade of B or higher, or consent. CS 107, CS 110 or a high school programming class is strongly recommended. Offered every semester.

Computer Programming II

A continuation of CS 112. Topics include advanced programming design in static methods (void and value-returning methods), arrays used in methods, classes, array-based lists, selection sort, binary search, object-oriented programming techniques (inheritance, polymorphism, and composition), and recursion. Prerequisite: CS 202 with a grade of C- or higher, CS 112 with a grade of C or higher, or consent. Offered every semester.

Data Science Using Python

The advanced Python programming techniques and applications in data science are studied. Topics include control structures, lists, tuples, sets and dictionaries, as well as the popular Python modules in data science including math, statistics, NumPy, and Pandas. Case studies on data analysis, visualization, series and frames are also covered. Prerequisite: A grade of C or higher in MATH 210 and a grade of a B or higher in either CS 107 or CS 212, or consent. Offered fall of even-numbered years.

Data Structures

An introduction to commonly used computer data structuring techniques. Topics include abstract data types, classes, queues, stacks, linked lists, algorithm analysis, sorting, searching, trees, and graphs. Prerequisite: CS 212 with a grade of C or higher or consent. Offered every fall.

Numerical Methods

Examines methods used in solving numerical problems with the aid of a computer. Topics include floating-point arithmetic, interpolation, integration, roots of non-linear equations, ordinary differential equations, and systems of linear equations. Identical to MATH 350. Students enrolled in the pre-engineering program with ODU cannot receive credit for this course and the cross-registered courses MAE 340 or CEE 305. Prerequisites: MATH 172 with a grade of C- or higher and CS 212 with a grade of C- or higher, or consent. Offered spring of even-numbered years.

Database Management Systems Design

Emphasizes the concepts and structures necessary to design and implement database systems using a relational database management system. Various database management system architectures illustrating hierarchical, network, and relational models are discussed. Physical data storage techniques, file security, data integrity, and data normalization are also explored. Prerequisite: CS 212 with a grade of C or higher or consent. Offered intermittently.

Internship in Computer Science

An intensive computer science project conducted in a professional setting. Pass/Fail grading. Prerequisite: junior/senior status, cumulative GPA of at least 2.5, and consent. Offered intermittently.

Research in Computer Science

Offers students the opportunity to conduct original scientific research in an area of interest. Students work closely with one or more members of the natural science faculty to develop and conduct a research project, then present their findings orally during the semester's undergraduate research symposium and as a formal research paper. Students are encouraged to present their findings at a conference. Prerequisites: junior/senior status and a major in Computer Science, prior approval by the project advisor, and consent of the instructor. Students may enroll for 2 or 4 hours in a given semester. May be repeated for a maximum total of 8 semester hours. Identical to BIO 489, CHEM 489, EES 489, and MATH 489. Offered intermittently.

Calculus I

Students learn the basic tools of calculus, why they work, and how to apply them in various contexts. Calculus I develops differential calculus through symbolic, graphical, and numerical approaches. Topics include differentiation of algebraic and transcendental functions, applications in modeling and optimization, the Fundamental Theorem of Calculus, and an introduction to differential equations. Prerequisite: placement level A or MATH 170 (grade C- or higher). Offered every semester.

Calculus II

A continuation of Calculus I. More advanced techniques are studied and used to solve quantitative problems in various contexts. Topics include integration techniques, applications of definite integration, polar coordinates, parametric equations, and sequences and series. Prerequisite: MATH 136 or 171 with a grade of C- or higher or consent. Offered every semester.

Introductory Statistics

Introduces students to learning from data. Topics include the basics of data production, data analysis, probability, Central Limit Theorem, and statistical inference. Statistical software is used for data management, calculation, and visualization. No previous knowledge of statistics is required. Students cannot receive credit for both MATH 210 and PSY 210. Prerequisites: sophomore status or higher, and placement level H, A, or B, or MATH 104 with a grade of C- or higher. Not appropriate for first-year students. Students must have a TI-83 or TI-84 graphing calculator. Offered every semester.

Linear Algebra

The study of vector spaces and linear equations in several variables. Topics include systems of linear equations, vector spaces, linear dependence of vectors, bases, dimension, linear transformations, matrices, determinants, and applications. Prerequisite: MATH 172 with a grade of C- or higher, or consent. Offered every fall.

Multivariable Calculus

Topics include functions of several variables, curves, surfaces, partial differentiation, multiple integrals, and vector analysis. Prerequisite: MATH 172 with a grade of C- or higher, or consent. Offered every spring.

Statistical Models

A course in applied data analysis. Emphasizes construction of models for authentic data sets. Statistical software is used extensively for analyzing real data sets from various contexts. Topics include parametric and non-parametric tests, simple and multiple regression, and ANOVA. Prerequisites: ENG 105 with a grade of C or higher, MATH 172 with a grade C or higher, and MATH 210 with a grade C or higher, or consent. Offered spring of even-numbered years.

Probability

Approaches probability as a discipline with applications throughout mathematics and the sciences. Topics include classical and axiomatic probability, random variables, common distributions, density functions, expectation, conditional probability, independence, the Law of Large Numbers, and the Central Limit Theorem. Prerequisite: MATH 273 with a grade of C- or higher, or consent. Offered fall of odd-numbered years.

Numerical Methods

Examines methods used in solving numerical problems with the aid of a computer. Topics include floating-point arithmetic, interpolation, integration, roots of non-linear equations, ordinary differential equations, and systems of linear equations. Identical to CS 350. Students enrolled in the pre-engineering program with ODU cannot receive credit for this course and the cross-registered courses MAE 340 or CEE 305. Prerequisites: MATH 172 with a grade of C- or higher and CS 212 with a grade of C- or higher, or consent. Offered spring of even-numbered years.

Mathematical Models

An introduction to mathematical models and sensitivity analysis. Emphasizes construction of models for real world applications in a variety of areas. Mathematical software is used extensively for problem solving. Topics include optimization models, linear and discrete programming, dynamic models, and probability models. Prerequisites: ENG 105 with a grade of C or higher, MATH 217 with a grade of C- or higher, and MATH 273 with a grade of C- or higher. Offered fall of even-numbered years.

Internship in Mathematics

An intensive mathematics project conducted in a professional setting. Pass/Fail grading. Prerequisites: junior/senior status, cumulative GPA of at least 2.5, and consent. Offered intermittently.

Research in Mathematics

Offers students the opportunity to conduct original scientific research in an area of interest. Students work closely with one or more members of the natural science faculty to develop and conduct a research project, then present their findings orally during the semester's undergraduate research symposium and as a formal research paper. Students are encouraged to present their findings at a conference. Prerequisite: junior/senior status and a major in Mathematics, prior approval by the project advisor, and consent of the instructor. Students may enroll for 2 or 4 hours in a given semester. May be repeated for a maximum total of 8 semester hours. Identical to BIO 489, CHEM 489, CS 489, and EES 489. Offered intermittently.

Introduction to Geographic Information Systems

Introduces the computer-literate student to the underlying theory and practical applications of Graphic Information System (GIS) technology. Lectures are interwoven with hands-on computer exercises that illustrate the principles, develop technical competence, and demonstrate the versatility of GIS. Individualized projects reinforce concepts and help students acquire the knowledge and confidence required to use GIS outside the classroom. Prerequisites: math placement level H, A, or B, and junior/senior status, or MATH 104, or consent. Offered every fall.