BS in Data Science draft

Program Overview

The Bachelor of Science degree in Data Science emphasizes problem solving in the context of data science and analytics, and prepares students for effectively analyzing massive amounts of structured/unstructured data in various application domains. This BS program requires a minimum of 42 credit hours in computer science, about 28 credit hours in mathematics and the sciences, and the university general education program. The departmental curriculum provides required data science courses such as introduction to data science, data structures, algorithms, database systems, object-oriented software development, artificial intelligence, information security, and data mining; as well as concentration courses in computer science, statistics, or business.

All undergraduate students majoring in Data Science will meet the following student general outcomes, specified by ABET CAC, upon their graduation:

1. Analyze a problem and identify and define the computing requirements appropriate to its solution.
2. Design, implement, and evaluate a computer-based solution to meet a given set of computing requirements in the context of the program’s discipline.
3. Communicate effectively in a variety of professional contexts.
4. Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.
5. Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline.

Specifically, graduates should have an ability to

1. Apply theory, techniques, and tools throughout the data analysis lifecycle and employ the resulting knowledge to satisfy stakeholders’ needs.

Abbreviation Key – UCA Core Program

LD = Lower Division

UD = Upper Division

C = Effective Communication

D = Diversity

I = Critical Inquiry

R = Responsible Living

Z = Capstone Experience

Program Outline

The Bachelor of Science, with a major in data science, requires successful completion of 120 hours, including (1) the UCA Core: complete 39 hours to meet lower-division UCA Core requirements and complete upper-division UCA Core requirements using major, minor, or elective courses (see the UCA Core requirements); (2) degree requirements; and (3) major requirements outlined below. This program does not require a minor.

Required Computing Core (42 credit hours)

• CSCI 1470 Computer Science I
• CSCI 1480 Computer Science II
• CSCI 2310 Introduction to Data Science
• CSCI 2320 Data Structures
• CSCI 3330 Algorithms
• CSCI 3360 Database Systems [UD UCA Core: C]
• CSCI 3381 Object-Oriented Software Development with Java
• MATH 3381 Data Cleaning and Visualization
• CSCI 3385 Artificial Intelligence
• CSCI 4315 Information Security [UD UCA Core: R]
• CSCI 4321 Ethical Implications of Technology [UD UCA Core: D, R]
• CSCI 4370 Data Mining
• CSCI 4491 Applied Data Science [UD UCA Core: Z]

Required Mathematics/Statistics Core (20 credit hours)

• MATH 1496 Calculus I
• MATH 1497 Calculus II
• CSCI 2330 Discrete Mathematics for Computing
• MATH 3311 Statistical Methods
• MATH 3320 Linear Algebra [UD UCA Core: I]
• MATH 4371 Introduction to Probability [UD UCA Core: R]

Data Science Concentration (12-18 credit hours)

Students majoring in Data Science will be required to complete one of the following concentrations.

[1] Computer Science (12 credit hours)

• CSCI 3V75 Internship (with V = 3 or an upper-division course approved by the chair)
• CSCI 2335 Networking or CSCI 3345 Human-Computer Interaction or CSCI 4340 Introduction to Parallel Programming
• CSCI 4371 Machine Learning
• CSCI 4372 Data Clustering

[2] Statistics (18 credit hours)

• MATH 3392 Multivariate Analysis
• MATH 4373 Regression Analysis
• MATH 4391 Machine Learning
• MATH 4392 Time Series and Forecasting
• And 6 credit hours of electives chosen from the following MATH courses:
• MATH 3391 Nonparametric Statistics
• MATH 4381 Special Problems in Mathematics (when the subject is one of the following: Qualitative Data Analysis, Text Mining, or Bayesian Analysis)

[3] Business (12 credit hours)

• CISA 3382 Internship in Computer Information Systems and Analytics (or an upper-division course approved by the chair)
• CISA 4325 Predictive Analytics
• CISA 4330 Prescriptive Analytics
• CISA 4380 Business Intelligence and Data Visualization

General Electives (5-11 credit hours)

This program requires 5–11 credit hours of general elective coursework depending on the concentration chosen.