Data Science Foundations (13-15)

Certificate of Undergraduate Study

Coordinator: Chair, Department of Mathematical Sciences

This certificate is designed to help students develop foundational understanding of the organization, manipulation, interpretation, and use of data.

The certificate is open to all degree-seeking undergraduates. Students must maintain good academic standing in the university, maintain at least a 2.00 grade point average in certificate courses, and complete all certificate course work within six calendar years. Up to 11 credits applied to a student’s major plan may be used to satisfy certificate requirements.

Requirements (13-15)

STAT 201 - Introduction to Software for Data Science Credits: 4

One of the following (3)

MATH 206 - Introductory Discrete Mathematics Credits: 3
MATH 239 - Introduction to Linear Algebra Credits: 3
PHIL 105 - Logic and Critical Reasoning Credits: 3

One of the following (3-4)

ISYE 335 - Probability and Statistics for Engineers Credits: 3
OR STAT 200 - Elementary Statistics Credits: 4
OR STAT 300 - Introduction to Probability and Statistics Credits: 3
OR UBUS 223 - Introduction to Business Statistics Credits: 3

One of the following (3-4)

ANTH 460 - Methods in Ethnography Credits: 3
CSCI 240 - Computer Programming in C++ Credits: 4
EAE 483 - Data Science for the Geosciences Credits: 4
ECON 290 - Economic Data Analysis Credits: 3
ECON 390 - Basic Econometrics and Economic Applications Credits: 3
AND ECON 390A - Basic Econometrics Laboratory Credits: 1
ECON 442 - Introduction to Financial Engineering Credits: 3
ECON 460X - Financial Derivatives Credits: 3
ECON 490 - Economic Statistics and Econometrics Credits: 3
ETR 340 - Survey Methods for Training and Evaluation Credits: 3
ETR 431 - Program/Performance Evaluation Credits: 3
ETR 450 - Data Analysis for Design and Evaluation Credits: 3
ISYE 335 - Probability and Statistics for Engineers Credits: 3
(if not used to fulfill requirement above)
ISYE 470 - Introduction to Data Analytics for Engineers Credits: 3
MATH 206 - Introductory Discrete Mathematics Credits: 3
(if not used to filfull requirement above)
MATH 239 - Introduction to Linear Algebra Credits: 3
(if not used to fulfill requirement above)
MATH 240 - Linear Algebra and Applications Credits: 4
MATH 445 - Mathematics of Data Science Credits: 3
MEE 483 - Deep Learning for Engineers Credits: 3
PHHE 325 - Biostatistical Applications in Public Health Credits: 3
PHHE 455 - Public Health Epidemiology Credits: 3
PHIL 105 - Logic and Critical Reasoning Credits: 3
(if not used to fulfill requirement above)
PHIL 339 - Ethics of Artificial Intelligence Credits: 3
POLS 340 - Political Analysis Credits: 3
PSYC 305 - Research Methods Credits: 3
PSYC 351 - Introduction to Psychological Tests Credits: 3
SOCI 378 - Social Science Data Visualization and Analysis Credits: 3
STAT 200 - Elementary Statistics Credits: 4
(if not used to fulfill requirement above)
STAT 300 - Introduction to Probability and Statistics Credits: 3
(if not used to fulfill requirement above)
STAT 415 - Computational Methods in Statistics Credits: 3
STAT 417 - Supervised Statistical Learning Credits: 3
STAT 418 - Unsupervised Statistical Learning Credits: 3
STAT 435 - Applied Regression Analysis Credits: 3
STAT 437 - Analysis of Discrete Data Credits: 3
UBUS 223 - Introduction to Business Statistics Credits: 3
(if not used to fulfill requirement above)