UTD Tower

Floor 1

Freshman Fall Semester

Your exciting yet nerve-racking college journey starts here. Can you handle the pressure?

CS 1436

Course Name: Programming Fundamentals
Course Description: Introduces the fundamental concepts of structured programming. Topics include software development methodology, data types, control structures, functions, arrays, and the mechanics of running, testing, and debugging. Programming language of choice is C.

MATH 2417

Course Name: Calculus I
Course Description: Functions, limits, continuity, differentiation; integration of function of one variable; logarithmic, exponential, and inverse trigonometric functions; techniques of integration, and applications.

NATS 1101

Course Name: Natural Sciences and Mathematics Freshman Seminar
Course Description: This course is designed to introduce incoming freshmen to the intellectual and cultural environment of the School of Natural Sciences and Mathematics (NS&M). Students will learn about plans of study and career paths for majors in Biology, Chemistry, Physics, Mathematics, Geosciences, and Science and Mathematics Education. Basic study, problem solving and other skills needed to succeed as an NS&M major will be covered. An overview of the connections within the disciplines of Natural Sciences and Mathematics will be presented, as well as their relationship to engineering, medicine and health, and other fields.

Floor 2

Freshman Spring Semester

CS 1337

Course Name: Computer Science I
Course Description: Review of control structures and data types with emphasis on structured data types. Applies the object-oriented programming paradigm, focusing on the definition and use of classes along with the fundamentals of object-oriented design. Includes basic analysis of algorithms, searching and sorting techniques, and an introduction to software engineering. Programming language of choice is C/C++.

MATH 2419

Course Name: Calculus II
Course Description: Continuation of MATH 2417. Improper integrals, sequences, infinite series, power series, parametric equations and polar coordinates, vectors, vector valued functions, functions of several variables, partial derivatives and applications, and multiple integration.
Prerequisites: MATH 2417

MATH 3315

Course Name: Discrete Mathematics and Combinatorics
Course Description: This course covers basic tools of enumerative combinatorics, equivalence relations, combinatorial proofs and recurrences, inclusion-exclusion, generating functions, and graphs and trees.
Prerequisites: MATH 2417

PHYS 2325

Course Name: Physics - Mechanics
Course Description: Calculus based. Basic physics including a study of space and time, kinematics, forces, energy and momentum, conservation laws, rotational motion, torques, and harmonic oscillation.

PHYS 2125

Course Name: Physics Laboratory I
Course Description: Laboratory course to accompany PHYS 2325 or PHYS 2421. Experiments investigate basic measurements and statistics; one dimensional and two dimensional motion; Newton's laws; conservation laws of energy and momentum; rotational motion; and oscillations.
Corequisites: PHYS 2325

Floor 3

Sophomore Fall Semester

CS 2336

Course Name: Computer Science II
Course Description: Further applications of programming techniques, introducing the fundamental concepts of data structures and algorithms. Topics include recursion, fundamental data structures (including stacks, queues, linked lists, hash tables, trees, and graphs), and algorithmic analysis. Includes comprehensive programming projects. Programming language of choice is Java.
Prerequisites: CS 1337
Prerequisites/Corequisites: MATH 3315

MATH 2418

Course Name: Linear Algebra
Course Description: Introduces and provides models for application of the concepts of vector algebra. Topics include finite dimensional vector spaces and their geometric significance; representing and solving systems of linear equations using multiple methods, including Gaussian elimination and matrix inversion; matrices; determinants; linear transformations; quadratic forms; eigenvalues and eigenvectors; and applications in science and engineering.
Prerequisites: MATH 2417

PHYS 2326

Course Name: Electromagnetism and Waves
Course Description: Continuation of PHYS 2325. Topics include electrostatics and electromagnetics, electric field and potential, electric currents, magnetic fields, laws of Coulomb, Ampere, and Faraday, Maxwell's theory of wave propagation.
Prerequisites: PHYS 2325, MATH 2419

PHYS 2126

Course Name: Physics Laboratory II
Course Description: A calculus-based laboratory course to accompany PHYS 2326 or PHYS 2422. Experiments investigate electrostatics, electric circuits, magnetism, and optics.
Corequisites: PHYS 2326

Floor 4

Sophomore Spring Semester

CS 3345

Course Name: Data Structures and Introduction to Algorithmic Analysis
Course Description: Analysis of algorithms including time complexity and Big-O notation. Analysis of stacks, queues, and trees, including B-trees. Heaps, hashing, and advanced sorting techniques. Disjoint sets and graphs. Course emphasizes design and implementation.
Prerequisites: MATH 3315, CS 2336
Prerequisites/Corequisites: STAT 3355

MATH 3310

Course Name: Theoretical Concepts of Calculus
Course Description: Mathematical theory of calculus. Limits, types of convergence, power series, differentiation, and Riemann integration.
Prerequisites: MATH 2419

MATH 3351

Course Name: Advanced Calculus
Course Description: The course covers the interplay of linear algebra, higher dimensional calculus, and geometry. Topics include vectors, coordinate systems, the elementary topology of Euclidean spaces and surfaces, the derivative as a linear map, the gradient, multivariate optimization, vector fields, vector differential operators, multiple integrals, General Stokes Theorem, and differential forms. Applications are given to geometry, science, and engineering. Basic topological intuition is developed.
Prerequisites: MATH 2418, MATH 2419

STAT 3355

Course Name: Introduction to Data Analysis
Course Description: Graphical and numerical summaries of data; univariate and bivariate probability models; point estimation; sampling distributions; confidence intervals and hypothesis testing; comparison of populations; linear regression; analysis of variance; and implementation of methods using R programming language.
Corequisites: MATH 2419

Floor 5

Junior Fall Semester

CS 4347

Course Name: Database Systems
Course Description: This course emphasizes the concepts and structures necessary for the design and implementation of database management systems. Topics include data models, data normalization, data description languages, query facilities, file organization, index organization, file security, data integrity, and reliability.
Prerequisites: CS 3345

MATH 4301

Course Name: Mathematical Analysis I
Course Description: Sets, real number system, metric spaces, real functions of several variables. Riemann-Stieltjes integration and other selected topics.
Prerequisites: MATH 3351 and MATH 3310.

STAT 4351

Course Name: Probability
Course Description: Sample spaces, probability of events, Kolmogorov's axioms, independence and dependence, Bayesian methodology. Discrete and continuous random variables. Probability distributions, mass functions and densities of univariate and multivariate random variables. Expected values, variances, moment generating functions, covariances and related issues. Probability inequalities. Special probability distributions and special probability densities. Functions of random variables, distribution function techniques, transformation techniques for one and several variables, moment-generating techniques. The law of large numbers, the central limit theorem and classical sampling distributions. Proofs of all main results. Practical examples illustrating the theory.
Prerequisites: MATH 3351

Floor 6

Junior Spring Semester

CS 4375

Course Name: Introduction to Machine Learning
Course Description: Algorithms for creating computer programs that can improve their performance through learning. Topics include: cross-validation, decision trees, neural nets, statistical tests, Bayesian learning, computational learning theory, instance-based learning, reinforcement learning, bagging, boosting, support vector machines, Hidden Markov Models, clustering, and semi-supervised and unsupervised learning techniques.
Prerequisites: STAT 3355, CS 3345

STAT 4352

Course Name: Mathematical Statistics
Course Description: Sampling distributions. Order statistics. Decision theory including minimax and Bayes criterion. Point estimation including unbiased estimators, efficiency, consistency, sufficiency, robustness, the method of moments, the method of maximum likelihood, Bayesian estimation. Interval estimation including the estimation of means, differences of means, proportions, differences between proportions, variances and ratios of variances. Hypothesis testing including Neyman-Pearson lemma, power function and likelihood ratio test. Special tests involving means, variances and proportions. Nonparametric tests. Foundations of regression, correlation, design and analysis of experiments. Proofs of all main results. Practical examples illustrating the theory.
Prerequisites: STAT 4351

STAT 4354

Course Name: Numerical and Statistical Computing
Course Description: Solving linear and nonlinear equations; numerical differentiation and integration; optimization; Newton-Raphson and EM algorithms; QR, Cholesky, eigenvalue, and singular value decompositions; random number generation; Monte Carlo methods; Markov chain Monte Carlo methods; bootstrap and jackknife; power analysis and sample size determination; and use of a statistical software package such as R.
Prerequisites: MATH 3351, STAT 4351

STAT 4355

Course Name: Applied Linear Models
Course Description: Introduction to linear statistical models and their application to empirical data. Topics include linear and logistic regression; multiple regression; diagnostic measures; detection of outliers and influential observations; variable selection; one- and two-way ANOVA; analysis of covariance; model fitting and validation using the statistical programming language R.
Prerequisites: STAT 3355

Floor 7

Senior Fall Semester

CS 4371

Course Name: Introduction to Big Data Management and Analytics
Course Description: This course focuses on scalable data management and mining algorithms for analyzing very large amounts of data (i.e., Big Data). Included topics are: Mapreduce, NoSQL systems (e.g., key-value stores, column-oriented data stores, stream processing systems), association rule mining, large scale supervised and unsupervised learning, and applications including recommendation systems, web and big data security.
Prerequisites: CS 2336, CS 4347

CS 4372

Course Name: Computational Methods for Data Scientists
Course Description: This course will focus on the application of computational tools to solve machine learning problems. Applicable languages may include Python, 'R', Weka, or others at the discretion of the instructor. Students will use these languages to apply machine learning concepts to problem data sets.
Corequisites: CS 4375

STAT 4360

Course Name: Introduction to Statistical Learning
Course Description: Supervised learning methods including linear regression; classification; cross-validation; bootstrap; model selection and regularization; tree-based methods; and support vector machines. Unsupervised learning methods including principal components analysis and clustering. Computer packages such as R or Python will be used for implementation of methods and data analysis.
Corequisites: STAT 3355

Floor 8

Senior Spring Semester

STAT 4475

Course Name: Capstone Project
Course Description: This course is intended to provide hands-on experience in a data science project. Students will work in teams on projects and will be involved in formulating a relevant problem, collecting the requisite data, finding a solution, and developing the necessary computational tools. The deliverables will include a final project report that details these steps and presentation of the project.
Prerequisites: STAT 4355, CS 4375