Center For Science, Technology, Engineering, and Mathematics

This course is the first in a two-course sequence and exposes the student to mathematical modeling, differentiation, and the basics of vectors and operations on vectors.

This course is the second in a two-course sequence and exposes the student to mathematical ideas of accumulation, integration, and basic dynamics.
Prerequisite:  STM 1001

Linear and simultaneous ordinary differential equations, exact solutions, solution by Laplace transform, and solution by numerical methods.
Prerequisite:  STM 1002

This course introduces students to fundamental concepts and techniques in discrete mathematics.  Topics covered include sets, logic, proofs, functions, relations, combinatorics, graph theory, and counting principles.

A first course in probability. Topics include basic probability axioms and counting techniques, random variables, conditional probability and Bayes’ Rule, discrete and continuous probability distributions, joint distributions and correlation, Law of Large Numbers, and Central Limit Theorem.
Prerequisite:  STM 1002

An introduction to statistics emphasizing multivariate modeling. Topics include descriptive statistics, experiment and study design, hypothesis testing, linear regression, analysis of variance, logistic regression.
Prerequisite:  STM 2101

A first course in matrix algebra. Topics include systems of equations, matrix arithmetic and factorizations, the rank-nullity theorem, eigenvectors and eigenvalues, the singular value decomposition, projections, regression, and other applications. Computer programming language will be used throughout the course.
Prerequisite:  STM 1002

An introduction to linear programming. Topics include formulating mathematical optimization models, the Simplex solution method, sensitivity analysis, and basic linear programming theory.
Prerequisite:  STM 2103

Students use Python to wrangle large, messy data sets into forms suitable for modeling and analysis and create visualizations that provide useful insights for decision-making.

Introduction to computer programming for the purpose of implementing solutions relating to data acquisition, storage, processing, analysis, and visualization. The concepts provide foundational knowledge and experience upon which later data science courses will build.

The first of a two-course sequence emphasizing the fundamental principles of classical physics, introducing a variety of applications. Topics include kinematics, linear and rotational motion, forces, energy, collisions, gravitation, wave motion, and simple fluids.
Prerequisite:  STM 1002

The second of a two-course sequence emphasizing the fundamental principles of classical physics, introducing a variety of applications. Topics include electricity, circuits, magnetism, and optics.
Prerequisite:  STM 1002, STM 2501

This course builds on the programming skills developed in the prerequisite course and moves the focus towards a wider software ecosystem in order to solve more complex data science tasks. Students learn and apply foundational principles of program organization including classes and objects, interfaces, inheritance, abstraction, and decoupling. In addition, important command-line skills are developed for data gathering and cleaning, as well as library and software acquisition and use. These principles are utilized through high-level programming in Python to analyze and manipulate real-world data sets.
Prerequisite:  STM 2301

Students become familiar with the use and performance characteristics of common data structures including stacks, queues, lists, trees, heaps, and hash tables. The techniques of asymptotic analysis using big-O notation are introduced as a formal tool to understand how computer programs scale in resource use for increasingly large inputs. A strong emphasis is placed on developing the ability to choose the most appropriate data structures for a given computational task, and to roughly estimate the asymptotic complexity of programs with loops and nested function calls.
Prerequisite:  STM 2301

Provides an introduction to data storage methods and systems. Topics include hardware and software used to efficiently store large datasets, relational databases and data models, SQL, and applications that interact with databases.
Prerequisite:  STM 3301

Provides an introduction to computational machine learning techniques. Topics include learning theory, unsupervised learning, recommendation systems, reinforcement learning, and neural networks.
Prerequisite:  STM 3301

Provides an introduction to low-level aspects of computer design. Topics include performance metrics, instruction set architectures, assembly language, logic design, memory hierarchies, and pipelining.
Prerequisite: Programming I

Provides an introduction to nonparametric methods in statistics and their applications. Topics include the sign test, the rank-sum test, the Kruskal-Wallis test, Kolmogorov-Smirnov type tests, and others. Examines methods from both theoretical and application points of view.
Prerequisite: Probability

Covers a variety of statistical learning topics, including: function estimation with data, bias-variance tradeoff, classification, linear regression, resampling methods, linear model selection and regularization, non-linear modeling, and tree-based methods, support vector machines, and unsupervised learning.
Prerequisite: Statistics

Studies the intersection of people and data. This course covers technical concepts relating to how humans interact with data interfaces and visualizations, as well as ethical questions of how humans interpret, present, and ultimately deploy data science tools.
Prerequisites: Data structures and scalability

Focuses on processing large datasets in a distributed environment, including cloud systems and High-Performance Computing Centers. Topics include NoSQL systems, cloud architecture, and distributed frameworks.
Prerequisites: Data Storage

Presents techniques for designing, analyzing, and implementing computer algorithms. Students gain a solid understanding of algorithmic problem-solving and be exposed to a variety of classical algorithms used in numerous applications.
Prerequisites: Data structures and scalability