Data Science

Develops differential calculus and its applications, with an introduction to integral calculus.

Develops integral calculus and its applications, then introduces infinite sequences and series.

Develops mathematical reasoning through discrete structures such as sets, functions, relations, graphs and trees.

Applies linear algebra to systems of equations, matrices, determinants and vectors in Euclidean spaces.

Introduces the data science workflow, including data exploration, visualization, statistical analysis and machine learning.

Introduces data summaries, probability distributions, sampling variability, estimation and statistical tests.

Studies multivariable differentiation and integration, vector fields and optimization for engineering and the physical sciences.

Introduces probability, random variables and expected values through mathematical models and applications.

Builds data science skills through research design, data collection and cleaning, visualization, regression and machine learning.

Develops probability tools for inference, including joint and conditional distributions, expectation, covariance and correlation.

Studies linear regression for a quantitative response, with matrix methods and practical data analysis.

Uses R and case studies to apply data analytics methods such as dimension reduction and classification.

Applies statistical principles to machine learning and introduces widely used neural network models.

Applies data science methods in a capstone project using problems from health care, industry or other organizations.