Data Science, BS

Program Overview

Data Science, BS

Overview

The Data Science major provides professional education to prepare the student for a career in a data-intensive field or graduate study. The BS in Data Science is a joint effort of the departments of Applied Mathematics and Statistics (AMS) and Computer Science (CSE), both housed in the College of Engineering and Applied Sciences (CEAS). The program provides a strong foundation in both the computational and mathematical sciences, as well as a substantive concentration in a related application area.

Admission Requirements

Acceptance into the Major

Qualified freshman and transfer students who have indicated their interest in the major on their applications may be admitted directly as a degree major or as a pre-major. Pre-majors are placed into the Area of Interest (AOI) program and to be eligible for the degree, they must be admitted to and declare the major.

• Completed CSE 114, CSE 214, and one of CSE 113, AMS 261, MAT 203, or MAT 307.
• Earn grades of B- or higher in the above AMS, CSE, and MAT courses, and a grade point average of 3.00 or higher in these courses.
• Repeated at most one of these courses.
• Earned a cumulative grade point average of 3.00 or higher.
• Completed course evaluations for all transferred courses that are to be used to meet requirements of the major.

Students outside of AMS, CSE, DAS, and ISE

Applications for major admission are reviewed twice per year and must be received by January 5 for Spring admission and June 5 for Fall admission. Students who do not meet the requirements for AOI admission above will not be considered. Fulfilling the requirements does not guarantee acceptance. Admission is competitive and contingent upon program capacity.

Major Requirements

The major in Data Science leads to the Bachelor of Science degree. Completion of the major requires at least 58 credits.

1. Mathematics

• AMS 151 - Applied Calculus I (3 credits)
• AMS 161 - Applied Calculus II (3 credits)
• One Linear Algebra Course:
  • AMS 210 - Applied Linear Algebra (3 credits)
  • MAT 211 - Introduction to Linear Algebra (3 credits)
  • MAT 308 - Differential Equations with Linear Algebra (4 credits)
• One Additional Calculus or Computer Science Foundations Course:
  • AMS 261 - Applied Calculus III (4 credits)
  • CSE 113 - Foundations of Computer Science I (4 credits)
  • MAT 203 - Calculus III with Applications (4 credits)
  • MAT 307 - Multivariable Calculus with Linear Algebra (4 credits)
• AMS 310 - Survey of Probability and Statistics (3 credits)

2. Computer Science

• CSE 101 - Computer Science Principles (4 credits) OR CSE 103 - Data Science Principles (4 credits)
• CSE 114 - Introduction to Object-Oriented Programming (4 credits)
• CSE 214 - Data Structures (4 credits)

3. Data Science

• CSE 351 - Introduction to Data Science (3 credits)
• One Upper-Division Data Analysis Course:
  • AMS 315 - Data Analysis (3 credits)
  • CSE 357 - Statistical Methods for Data Science (3 credits)

4. One Upper Division Writing Course

• CSE 300 - Technical Communications (3 credits)

5. Professional Ethics

• CSE 312 - Social, Legal, and Ethical Issues in Computing (3 credits)

6. Technical Electives

Each student must complete at least 9 credits of data science technical electives at the upper-division level. Electives may be drawn from any of the courses listed below. Other courses may be selected with approval of the Undergraduate Program Director.

• AMS 301 - Finite Mathematical Structures (3 credits)
• AMS 311 - Probability Theory (3 credits)
• AMS 316 - Introduction to Time Series Analysis (3 credits)
• AMS 317 - Introduction to Linear Regression Analysis (3 credits)
• AMS 318 - Financial Mathematics (3 credits)
• AMS 326 - Numerical Analysis (3 credits)
• AMS 341 - Operations Research I: Deterministic Models (3 credits)
• AMS 345 - Computational Geometry (3 credits)
• AMS 380 - Data Mining (3 credits)
• AMS 394 - Statistical Laboratory (3 credits)
• AMS 412 - Mathematical Statistics (3 credits)
• AMS 487 - Research in Applied Mathematics (0-3 credits)
• AMS 488 - Internship in Applied Mathematics and Statistics (0-3 credits)
• CSE 327 - Fundamentals of Computer Vision (3 credits)
• CSE 332 - Introduction to Visualization (3 credits)
• CSE 337 - Scripting Languages (3 credits)
• CSE 352 - Artificial Intelligence (3 credits)
• CSE 353 - Machine Learning (3 credits)
• CSE 354 - Natural Language Processing (3 credits)
• CSE 373 - Analysis of Algorithms (3 credits)
• One of:
  • ISE 305 - Database Design and Practice (3 credits)
  • CSE 305 - Principles of Database Systems (3 credits)
• CSE 487 - Research in Computer Science (0-3 credits)
• CSE 488 - Internship in Computer Science (3 credits)

7. Specialization

Each student must complete a specialization consisting of 12 or more credits. Courses taken to satisfy a specialization requirement may not be used to satisfy the technical elective requirements.

Business

• BUS 220 - Introduction to Decision Sciences (3 credits)
• At least three of the following:
  • BUS 340 - Information Systems in Management (3 credits)
  • BUS 346 - Operations Management (3 credits)
  • BUS 375 - Data Mining (3 credits)
  • BUS 376 - Risk Management & Insurance (3 credits)
  • BUS 377 - Risk Management & Insurance II (3 credits)

Computer Science

• Any combination of upper-division CSE technical lecture courses totaling 12 or more credits not taken to satisfy any other major requirement.

Applied Mathematics and Statistics

• Any combination of upper-division AMS technical lecture courses totaling 12 or more credits not taken to satisfy any other major requirement.

Grading

All courses taken to satisfy requirements for the major must be taken for a letter grade and passed with a grade of C or higher.

SBC Courses

This table illustrates major courses that can also be used to fulfill SBC requirements.

Sample Course Sequence

Freshman

Sophomore

Junior

Senior