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The University of New Mexico

Data Science

Data Science Concentration

The Data Science Concentration under the BS in Mathematics provides students with a strong foundation in the mathematical, statistical, and computational principles that underlie modern data analysis and modeling. The concentration emphasizes mathematical reasoning, data literacy, programming, and applied statistical and computational techniques. Students complete lower- and upper-division core requirements, elective courses in mathematics, statistics, and computer science, and a minor in either Statistics or Computer Science. The program prepares graduates for data-driven research, professional careers, and advanced study in data science and related fields.

Suggested Schedule

This schedule is a suggestion on when to take Math and Stats courses to complete the Data Science Concentration. For more information, please refer to the UNM Catalog webpage.

SEMESTER 1

  • MATH 1300 Statistical Literacy (3) OR MATH 1350 Introduction to Statistics (3)
  • MATH 1512 Calculus I (4)
  • GEOG 2115 Information Design (3)

SEMESTER 2

SEMESTER 3

  • MATH 2531 Calculus III (4)
  • MATH 314 Linear Algebra w/ Applications (3) OR MATH 321 Linear Algebra (3)
  • STAT 345 Probability/Stats (3)

SEMESTER 4

  • MATH 327 Intro to Mathematical Thinking (3) OR MATH 401 Advanced Calculus I (4)
  • MATH 381 Math Foundations (3)

SEMESTER 5

  • MATH 481 Optimization (3)
  • STAT 411 Ethics in Data Science (3)
  • STAT 428 Adv. Data Analysis II (3)

SEMESTER 6

  • CS 464 Database Management (3)

  • Data Science Elective #1 (3)**

  • Data Science Elective #2 (3)**

SEMESTER 7

Data Science Elective #3 (3)**

 

**Data science approved electives. Select three courses from the following list, in consultation with your advisor:

  • MATH 375 — Introduction to Numerical Computing;
  • MATH 393 / 439 — Topics in Mathematics;
  • MATH 431 — Introduction to Topology (fall only);
  • MATH 441 / STAT 461 — Probability (fall only);
  • MATH 464 — Applied Matrix Theory (fall only);
  • MATH 471 — Introduction to Scientific Computing (fall only);
  • MATH 549 — Selected Topics in Probability Theory (spring only);
  • STAT 445 — Analysis of Variance and Experimental Design (spring only);
  • STAT 472 — Sampling Theory and Practice (spring only);
  • STAT 477 — Introduction to Bayesian Modeling (spring only);
  • STAT 479 — Topics in Statistics;
  • CS 361L — Data Structures & Algorithms;
  • CS 429 — Introduction to Machine Learning;
  • CS 467 — Principles and Applications of Big Data.