Washington University in St. LouisWebsiteAcademic Catalog
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BS Degree in Data Sciencesource 1source 2
CS Courses
- Introduction to Computer ScienceCSE 131 (3)introCSE 131: Introduction to Computer Science
An introduction to software concepts and implementation, emphasizing problem solving through abstraction and decomposition. Introduces processes and algorithms, procedural abstraction, data abstraction, encapsulation and object-oriented programming. Recursion, iteration and simple data structures are covered. Concepts and skills are mastered through programming projects, many of which employ graphics to enhance conceptual understanding. Java, an object-oriented programming language, is the vehicle of exploration. Active-learning sessions are conducted in a studio setting in which students interact with each other and the professor to solve problems collaboratively.
- Introduction to Data ScienceCSE 217A (3)aiCSE 217A: Introduction to Data Science
This course provides an introduction to data science and machine learning, and it focuses on the practical application of models to real-world supervised and unsupervised learning problems. We will discuss methods for linear regression, classification, and clustering and apply them to perform sentiment analysis, implement a recommendation system, and perform image classification or gesture recognition.
- Data Structures and AlgorithmsCSE 247 (3)algsCSE 247: Data Structures and Algorithms
Study of fundamental algorithms, data structures, and their effective use in a variety of applications. Emphasizes importance of data structure choice and implementation for obtaining the most efficient algorithm for solving a given problem. A key component of this course is worst-case asymptotic analysis, which provides a quick and simple method for determining the scalability and effectiveness of an algorithm. Online textbook purchase required.
- Data Manipulation and ManagementCSE 314A (3)sysCSE 314A: Data Manipulation and Management
As the base of data science, data needs to be acquired, integrated and preprocessed. This important step in the data science workflow ensures both quantity and quality of data and improves the effectiveness of the following steps of data processing. Students will gain an understanding of concepts and approaches of data acquisition and governance including data shaping, information extraction, information integration, data reduction and compression, data transformation as well as data cleaning. The course will further highlight the ethical responsibility of protecting the integrity of data and proper use of data.
- Introduction to Machine LearningCSE 417T (3)aiCSE 417T: Introduction to Machine Learning
The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience. This course is a broad introduction to machine learning, covering the foundations of supervised learning and important supervised learning algorithms. Topics to be covered are the theory of generalization (including VC-dimension, the bias-variance tradeoff, validation, and regularization) and linear and non-linear learning models (including linear and logistic regression, decision trees, ensemble methods, neural networks, nearest-neighbor methods, and support vector machines.
- CS Elective
- Approved comprehensive project or experience
Math/Stat Courses
- Calculus IIMath 132 (3)mathMath 132: Calculus II
Continuation of Math 131. A brief review of the definite integral and Fundamental Theorem of Calculus. Techniques of integration, applications of the integral, sequences and series, Taylor polynomials and series, and some material on differential equations.
- Matrix AlgebraMath 309 (3)mathMath 309: Matrix Algebra
An introductory course in linear algebra that focuses on Euclidean n-space, matrices and related computations. Topics include: systems of linear equations, row reduction, matrix operations, determinants, linear independence, dimension, rank, change of basis, diagonalization, eigenvalues, eigenvectors, orthogonality, symmetric matrices, least square approximation, quadratic forms. Introduction to abstract vector spaces.
- Statistics for Data Science IIMath 4211 (3)mathMath 4211: Statistics for Data Science II
This builds on the foundation from the first course (SDS I) and further develops the theory of statistical hypotheses testing. It also covers advanced computer intensive statistical methods, such as the Bootstrap, that will make extensive use of R. The emphasis of the course is to expose students to modern statistical modeling tools beyond linear models that allow for flexible and tractable interaction among response variables and covariates/feature sets. Statistical modeling and analysis of real datasets is a key component of the course.
- Linear Statistical ModelsMath 439 (3)mathMath 439: Linear Statistical Models
Theory and practice of linear regression, analysis of variance (ANOVA) and their extensions, including testing, estimation, confidence interval procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares, etc. The theory will be approached mainly from the frequentist perspective, and use of the computer (mostly R) to analyze data will be emphasized.
- Math Elective
Engineering Courses
- Technical WritingEngr 310 (3)engrEngr 310: Technical Writing
Persistent concerns of grammar and style. Analysis and discussion of clear sentence and paragraph structure and of organization in complete technical documents. Guidelines for effective layout and graphics. Examples and exercises stressing audience analysis, graphic aids, editing and readability. Videotaped work in oral presentation of technical projects. Writing assignments include descriptions of mechanisms, process instructions, basic proposals, letters and memos, and a long formal report.
Science Courses
- 8 units from
Natural Sciences