Students who have completed the data science and analytics major should be able to:
- Demonstrate a well-developed understanding of the mathematics that are foundational to techniques in data science and analytics, such as set theory, symbolic logic, calculus, linear algebra, probability, and combinatorics.
- Collect, clean, validate and filter data sources.
- Demonstrate proficiency in an appropriate scripting language (e.g. Python), statistical software (e.g. R), and programming language for managing relational databases (e.g. SQL) and unstructured data repositories (e.g. No-SQL).
- Fit statistical models (e.g. linear regressions) and assess the efficacy of those models.
- Communicate statistical results in written and oral forms to technical and nontechnical audiences.
- Understand what can and cannot be inferred from a set of data and the limits of statistical techniques used in order to recognize errors that can be made in carrying out analyses and interpreting results.
- Apply data science and analytics in the investigation of real-world problems.
- Understand and appropriately respond to the many ethical considerations that arise in the field of data science and analytics.
Major requirements include:
- CS 115 - Discrete Mathematics
- CS 111 - Computing I
- CS 112 - Computing II
- MA 170 - Calculus I
- MA 180 - Calculus II
- MA 310 - Linear Algebra
- MA 330 - Statistics I
- MA 340 - Statistics II
- MA 345 - Foundations of Data Science and Analytics /CS 345
- CS 370 - Database Management
- MA 390 - Graph Theory and Combinatorics
- MA 455 - Research /CS 455
- 1 elective from the following: CS 210 - Introduction to Geographic Information Systems, CS 211 - Remote Sensing, CS 213 - Data Structures and Algorithms, CS 310 - Advanced Algorithmic Analysis, CS 360 - Artificial Intelligence
Course Sequence Outline
- HU 103 - Conversatio I
- MA 110 - Concepts of Mathematics or CS 115 Discrete Math
- MA 170 - Calculus I
In the fall, students should take CS 111 - Computing I and MA 330 - Statistics I. In the spring, students should take CS 112 - Computing II and MA 340 - Statistics II. Their remaining courses will be core requirements, electives or courses for a minor or double major
Students should take MA 310 - Linear Algebra, CS/MA 3XX - Foundations of Data Science and Analytics, and MA 370 - Database Management. If the schedule permits, students may consider taking additional courses for the major. Their remaining courses will be core requirements, electives or courses for a minor or double major. One semester or a full year of study abroad is an option for this year.
Majors will complete the required courses for the major and the mathematics. Their remaining courses will be core requirements, electives, or courses for a minor or double major.