Degree Requirements

Data Science Major Requirements checklist

Data Science Computational Analytics 
Data Science Economic Analytics
Data Science Mathematical Analytics

Base Curriculum (56 units):

  • CS 110 (Intro to Programming I)
  • CS 112 (Intro to Programming II)
  • CS 245 (Data Structures) 
  • CS 360 (Data Visualization)
  • CS 451 (Data Mining)
  • Math 109 (Calc I)
  • Math 110 (Calc II)
  • Math 230 (Linear Algebra)
  • Math 201 (Discrete Math) or Math 235 (Formal Methods)
  • Math 345 (Mathematical Modeling)
  • Math 370 (Probability with Applications)
  • Math 371 (Statistics with Applications)
  • Econ 111 (Microeconomics) or Econ 112 (Macroeconomics)
  • One of: CS 490 (Senior Project) or Math 394 (Applied Math Laboratory). This will serve as a capstone experience for the students.

In addition to the core, students must choose one of the concentrations listed below.

Concentration in computational analytics (68 total units):

  • Add 3 out of the 4 from: CS 212 (Software Development), CS 333 (Databases), CS 451 (Data Mining), Math 430 (Applied Numerical Linear Algebra)
Concentration in economic analytics (68 total units):
  • Substitute Econ 320 (Econometrics) for Math 371 (Statistics with Applications)
  • Add one of: Econ 311, 312 (Intermediate micro/macro)
  • Add two from: Econ 350 (Money and Banking), Econ 425 (Economics of Financial Markets), Econ 451 (Monetary Economics)
Concentration in mathematical analytics (68 units):
  • Add Math 211 (Calculus III)
  • Add two of: Math 340 (Differential Equations), Math 422 (Combinatorics), Math 453 (Real Analysis)

New Courses

CS 451: Data Mining

This course provides an overview of generative and discriminative classifiers, including decision trees, rule learners, regression models, Bayesian models, and support vector machines. Also, clustering methods such as k-means, kNN and EM. Discussion of data cleaning and data preparation issues, including noise models, missing and unbalanced data, discrete versus continuous features, and feature selection, including PCA and mutual information. Some techniques are implemented from scratch – in other cases, real-world tools such as R, Weka, or CN5.0 are used on large-scale data sets. Prerequisite: CS 245.

Math 371: Statistics with applications
This course introduces students to theoretical issues and data-driven applications in statistics. Topics include descriptive statistics and data analysis; confidence intervals and hypothesis tests; estimation theory, linear regression, goodness-of-fit tests, and nonparametric tests. Prerequisite: Math 370, or permission of instructor.

 

Math 430. Applied Numerical Linear Algebra (To be added in coming years)
Advanced treatment of numerical algebra. The first 2/3 of the course will be devoted to important topics in applied numerical linear algebra including: Stability of Algorithms and Conditioning of Problems, Gaussian Elimination and LU Factorization, QR Factorization, Singular Value Decomposition, Least-Squares Solutions to Linear Systems, Numerical Matrix Eigenvalue Problems, Iterative Methods for Large and Sparse Problems. The last 1/3 of the course will be devoted to selected applications including spectral data clustering, Pagerank and other modern iterative methods, optimization, and linear programming. Prerequisite: Math 230.