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Data Science

Data Science course descriptions

Faculty

Wendy Weber (chair), Erik Insko, Stephen Fyfe, Russ Goodman, Mark Mills, Alexey Pronin, Michael Thompson.

Statement of Philosophy

Data is everywhere in today’s society. A major or minor in data science is a valuable way for students to learn a variety of concepts, tools, and modern technologies connected to data analysis. Further, students studying data science will have enhanced abilities to visualize, interpret, and communicate the results of analyses involving data. These are all essential skills for students to apply in the context of a variety of disciplines, including their chosen field of study.

 

Minor Restriction

Students declaring a computer science major or mathematics major may not also declare a data science minor. Students declaring an economics major or an actuarial science major must take an elective outside of their major.

 

Data Science Major Requirements (minimum 48 credits) 

  1. Complete all of the following:
    DATA   110       Data-Driven Decisions (3)
    DATA   210       Introduction to Data Science (3)
    DATA   310       Data Visualization (3)
    DATA   340       Applied Machine Learning (3)
    DATA   420       Senior Project (4)
    COSC   110      Introduction to Computer Science (3)
    COSC   130      Data Structures (3)
    COSC   250      Databases and Big Data (3)
    COSC   330      Algorithms (3)
    MATH   131      Calculus I (4)
    MATH   215      Applied Statistics (4)
  1. Take four related elective courses (minimum 12 credits) with at least one course at the 300-level or higher, in consultation with the advisor.

 

Data Science Minor Requirements (23-24 credits)

  1. Complete all of the following:
    COSC   110     Introduction to Computer Science (3)
    MATH   131     Calculus I (4)
    MATH   105     Introduction to Statistics (4) or MATH 215 Applied Statistics or approved disciplinary statistics course
    DATA    110     Data- Driven Decisions (3)
    DATA    210     Introduction to Data Science (3)
    DATA    310     Data Visualization (3)
  1. Complete one of the following:
    COSC   250     Databases and Big Data (4)
    DATA     340     Applied Machine Learning (3)
    ECON   381     Research Methods in Economics (4)
    ECON   382     Economic Forecasting (3)
    ECON   485     Economics Research Seminar (3)
    GEOG  320     Principles of GIS with Lab (3)
    MATH   240     Linear Algebra (4)
    POLS   489     Capstone Seminar (4)
    SOC    350      Methods of Social Research (4)

Notes regarding prerequisites:
COSC 110 is a prerequisite for DATA 210
DATA 310 requires second-year standing
COSC 110 and MATH 131 are prerequisites for DATA 340