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Skip to content Information for: CSE resources PhD Students Master's Students Engineering resources Prospective Students Current Students Faculty & Staff Alumni Industry Apply Now! It's easy to start your application. Undergraduate Admissions Graduate Admissions Dual Degree Program Graduate applicants: Attend an info session and skip the application fee McKelvey School of Engineering Computer Science & Engineering Academics Academics The world needs difference-makers. 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Prospective Students Current Students Faculty & Staff Alumni Industry Start your application today Undergraduate Admissions Graduate Admissions Dual Degree Program Graduate applicants: Attend an info session and skip the application fee Search Trending Searches graduate admissions academic programs financial aid academic calendar maps & directions summer school Home Academics Undergraduate Programs Bachelor of Science in Data Science Undergraduate Programs Bachelor of Science in Data Science Students majoring in data science will have the formal foundation needed to understand the applicability and consequences of the various approaches to analyzing data with a focus on statistical modeling and machine learning. They will have the computing skills needed to ingest, manage and visualize data. Additionally, they will be able to program on their own and use common libraries to implement solutions to solve data problems.&＃160;They will have the experience of applying their knowledge and skills in a practical research or industrial setting. This program of study is a collaboration between the Department of Mathematics and Statistics in Arts & Sciences and the Department of Computer Science & Engineering in the McKelvey School of Engineering. Core Course Requirements (CR) Math 131 Calculus I (AP credit may satisfy this requirement) 3 Math 132 Calculus II (AP credit may satisfy this requirement) 3 Math 233 Calculus III 3 Math 309 Matrix Algebra 3 Math 3211 Statistics for Data Science I 3 Math 4211 Statistics for Data Science II 3 Math 439 Linear Statistical Models 3 CSE 131 Introduction to Computer Science 3 CSE 247 Data Structures and Algorithms 3 CSE 217A Introduction to Data Science 3 CSE 314A Data Manipulation and Management 3 CSE 417Tor&＃160;Math 4601 Introduction to Machine LearningStatistical Learning 3 &＃160;Each of these core courses must be passed with a grade of C- or better. Electives (4 courses) *At least one from Math/Statistics (400 or above) *At least one from CSE (ending in S, T, M, or A) *At most one course at the 200 level Computer Science and Engineering CSE 237S Programming Tools and Techniques CSE 256A Introduction to Human-Centered Design CSE 311A Introduction to Intelligent Agents Using Science Fiction CSE 347 Analysis of Algorithms CSE 359A Signals, Data and Equity (Cannot be double-counted in EPR) CSE 411A AI and Society (Cannot be double-counted in EPR) CSE 412A Intro to AI CSE 416A Analysis of Network Data CSE 417T Introduction to Machine Learning (Cannot be double-counted in CR) CSE 427S Cloud Computing CSE 435S Database Management Systems CSE 457A Introduction to Visualization CSE 514A Data Mining CSE 515T Bayesian methods in ML CSE 517A Machine Learning CSE 518A Crowdsourcing Computing CSE 527A&＃160;Natural Language Processing CSE 527S&＃160;Data-driven Privacy and Security CSE 534A Large-Scale Optimization for Data Science CSE 543T Algorithms for Nonlinear Optimization CSE 559A Computer Vision Mathematics and Statistics Math 322 Bio stats Math 420 Experimental Design Math 434 Survival Analysis Math 4392 Advanced Linear Statistical Models Math 449 Numerical Applied Mathematics Math 450 Topics in Applied Mathematics Math 456 Financial Mathematics Math 459 Bayesian Statistics Math 460 Statistical Learning (also possible in core) Math 461 Time Series Analysis Math 4601 Statistical Learning (Cannot be double-counted in CR) Math 462 Foundations of Big Data Math 475 Statistical Computation Math 494 Mathematical Statistics Math 495 Stochastic Processes Math 5047 Diff Geometry Math 5061 Theory Of Statistics I Math 5062 Theory Of Statistics II Math 5071 Advanced Linear Model I Math 5072 Advanced Linear Model II Electrical & Systems Engineering ESE 4031 Optimization for Engineered Planning, Decisions and Operations ESE 415 Optimization ESE 427 Financial Mathematics Energy, Environmental & Chemical Engineering EECE 202 Computational Modeling in Energy, Environmental and Chemical Engineering Linguistics  Ling 317&＃160;Introduction to Computational Linguistics Ethics and Professional Responsibility (EPR) 3 units Ethics and Professional Responsibility (EPR) E60 Engr 4501 Engineering Ethics and Sustainability 1&＃160;unit E60 Engr 4502 Engineering Leadership and Team Building 1&＃160;unit E60 Engr 4503 Conflict Management and Negotiation 1&＃160;unit E60 Engr 450F Engineers in the Community (Engineering Ethics, Leadership and Conflict Management) 3&＃160;units E60 Engr 520P Presentation Skills for Scientists and Engineers 2&＃160;units E81 CSE 359A Signals, Data and Equity (Cannot be double-counted as an Elective) 3&＃160;units E81 CSE 411A AI and Society (Cannot be double-counted as an Elective) 3&＃160;units M21 MSB 512 Ethics in Biostatistics and Data Science 2 units DS Practicum Requirement 3 units of an&＃160;approved comprehensive data science project or experience. A practicum must be approved by the committee of data science faculty. The practicum experience should be completed the next-to-last semester of study (i.e., first semester senior year). It is important that practicum plans be submitted for review prior to starting the project or course work to ensure the proposed work is sufficient for the objectives of the practicum. After the fact approvals are possible but not guaranteed. Appropriate practicum work is possible via Independent Study (CSE 400E or Math 400), or via project-focused classes, including (but not limited to) CSE 437S Software Engineering Workshop and CSE 454A Software Engineering for External Client. Students should contact course instructors in advance to identify the degree of agency the student will have over project selection and requirements.&＃160; Download the practicum form here and contact the Undergraduate Coordinator&＃160;in the CSE department office or the Math department office to initiate the approval process. Additional Departmental Requirements CWP 100 College Writing I 3 units Engr 310 Technical Writing 3 units Natural Sciences electives 8 units Humanities and Social Sciences electives 18 units Total Units 32 *The College Writing and Humanities and Social Sciences requirements are those required of all students in the McKelvey School of Engineering. The Natural Sciences requirement is for 8 units designated NSM (Natural Sciences and Mathematics) from any of the following departments: Anthropology; Biology; Chemistry; Earth, Environmental, and Planetary Sciences; Environmental Studies; or Physics. The College Writing and Natural Sciences courses must be completed with a grade of C- or better. All courses taken to meet any of the above requirements (with the exception of the humanities and social sciences electives) cannot be taken on a pass/fail basis. Sample Schedule starting in Year 1 Fall Spring Year 1 Math 131 CSE 131 Math 132 CSE 247 Year 2 Math 233 CSE 217A Math 309 CSE 314A Year 3 Math 3211 DS Elective 1 Ethics Course Math 4211 Math 439 DS Elective 2 Year 4 CSE 417T (or Math 4601) Practicum DS Elective 3 DS Elective 4 &＃160; Sample schedule starting in Year 2 (having credit already for Math 131 and CSE 131) Fall Spring Year 2 Math 132 CSE 247 Math 233 CSE 217A Year 3 Math 309 Math 3211 CSE 314A Math 4211 Math 439 Elective 1 Year 4 CSE 417T (or Math 460) Practicum Ethics Elective 2 Elective 3 Elective 4 Related links Bachelor of Science in Business + Computer Science Bachelor of Science in Computer Engineering Bachelor of Science in Computer Science Bachelor of Science in Computer Science + Math Bachelor of Science in Computer Science + Economics Second Majors Minors Undergraduate Research Transfer Course Credit Resources for students Academic Calendar Bulletin Frequently Asked Questions Independent Study Form Undergraduate Student Services Facebook Twitter LinkedIn Instagram YouTube Engineering Departments Biomedical Engineering Computer Science & Engineering Division of Engineering Education Electrical & Systems Engineering Energy, Environmental & Chemical Engineering Mechanical Engineering & Materials Science Sever Institute - professional degrees Technology & Leadership Center - training for industry Contact Us Washington University in St. Louis McKelvey School of Engineering MSC: 1045-213-1010J 1 Brookings Drive St. Louis, MO 63130-4899 Undergrad info: 314-935-6160 Grad info: 314-935-6132 Contact Us Resources COVID-19 Resources Canvas Directory Equity, Diversity & Inclusion Emergency Management Engineering IT Maps & Directions Make a Gift WebFAC / WebSTAC ©2024 Washington University in St. Louis. 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