APC Document 25 (MATH): Add a minor in Data Science to be administered by the

Department of Mathematics and Statistics

Effective Date: Fall 2024

1. Add: On page 240, following the entry for Minor in Mathematics:

Minor in Data Science

Requirements for the Minor

The Data Science minor requires a minimum of 24 semester hours, including CSCI 183,

one course from CSCI 329, 339 or STAT 329; STAT 185 or 225; MATH 191, 295; and two

additional courses chosen from the list of elective courses below. Completion of MATH 291

and 365 may be substituted for MATH 295. Other appropriate courses with a data science

emphasis may be approved by the Chair of the Department of Mathematics and Statistics.

Students majoring in Mathematics or Computer Science may apply only 15 hours of courses

that are used for the major toward a Minor in Data Science.

University-wide minimum requirements for a minor: 1) one-half of the hours required for

a minor must be completed in residence at UNC Asheville, to include at least 6 hours at the

BUS 367 Introduction to Business Analytics (4)

CSCI 329 Big Data Analytics (if not counted above) (3)

CSCI 339 Topics in AI & Machine Learning (if not counted above) (3)

CSCI 343 Database Management (3)

ECON 365 Econometrics (4)

POLS 396 Quantitative Reasoning: Telling Stories with Numbers (4)

STAT 325 Introduction to Regression Models (3)

STAT 326 Statistics for Experimenters (3)

STAT 327 Applied Multivariate Analysis (3)

STAT 329 Big Data Analytics (if not counted above) (3)

MATH 191 are typically offered each semester. STAT/CSCI 329: Big Data Analytics is currently offered

every other year, and if the minor increases demand for this course in the future, we will submit an APC

proposal to update the course catalog to offer this course every year. While Dr. Jimin Lee has been the

only faculty member who has taught STAT/CSCI 329, there are faculty in both the Department of

Mathematics and Statistics and the Department of Computer Science who have the expertise and interest

in teaching this course, and the instructor could potentially alternate between the two departments if it is

offered annually. The Department of Mathematics and Statistics and the Department of Computer Science

have a history of collaboration with cross-listed courses and will work together to coordinate the

frequency and instructor for this course. CSCI 339, Topics in AI and Machine Learning, is a new course

that will feature rotating topics related to data science. CSCI 339 is being proposed to replace CSCI 312

(Artificial Intelligence), CSCI 412 (Computer Vision), and special topics courses such as CSCI 373 Data

Science, and CSCI 373 Natural Language Processing.

The Department of Mathematics and Statistics will work collaboratively with the Department of

Computer Science to establish a new course, MATH 295 Math for Machine Learning. MATH 295

introduces the essential tools of calculus, linear algebra, and probability used in machine learning. The

Department of Computer Science intends to require MATH 295 for the computer science major (both

Rationale: Almost every industry is being reshaped by the need to adapt to data-driven decision making

due to the rapid development of computation, data collection, and storage methods. Data storage, data

organization, statistical tests, and machine learning methods are tools used in various fields and

industries. As a result, there is an increasing number of job opportunities for students with training in data

science. According to Lightcast, the number of data science jobs in the U.S. is expected to increase by

84% between 2020 and 2030; between May 2022 and May 2023 there were 19,801 average monthly

postings for data scientists but only 5,238 average monthly hires. To meet this emerging trend and to

computational and statistical skills.

We have an existing set of courses that provide a core foundation in computational and statistical

techniques, and a number of courses that can serve as electives. The proposed courses align closely with

other data science programs (see Table 2), and utilizes faculty expertise across disciplines to offer a

cohesive multidisciplinary program in data science. This minor provides a first step to offering a data

science program, with the goal of eventually developing a data science major.

Table 2: Data Science Minor Requirements at Other Institutions

● Introduction to Data Science

● Applied Data Science

● Statistics Course (choose one from a list)

● Choose any three courses from a list

● Data Science I and II

● One course in statistics (from a list of four options)

● Introduction to computer programming

● Two electives

● Data Science and Visualization

● Computer Science I

● Computer Science II

● Database Systems

● Data Mining and Machine Learning

We plan to recruit students from Natural Sciences, Economics, Political Science, and Business.

While students in Psychology might be interested in this minor, we expect that psychology students will

more likely pursue the Certificate in Applied Social Science Research. The data science minor requires

24-26 credit hours, which is higher than most other minors on campus. However, Table 3 shows courses

required in other majors that can count toward the data science minor. We expect computer science

majors and mathematics majors in the applied mathematics concentration to be interested in pursuing a

data science minor and therefore allow up to 15 credit hours to count toward both the major and minor,

which leaves 3 additional courses for these students to obtain the minor.

Student Learning Outcomes:

Table 4 shows the student learning outcomes for the data science minor and where these objectives are

achieved.

Table 4: Student Learning Outcomes for the data science minor