Data Science Major and Minor

Introduction to programming with the Python programming language. Basic data types: int, float, str, bool. Programming constructs: sequential, conditional, iterative, nested conditional, nested iterative. Runtime analysis. User-defined functions, function libraries. Data structures: one- and two dimensional lists, tuples, ranges, dictionaries, sets.

Introduction to object-oriented programming with the Python programming language. Recursion: basic algorithms, array searching and sorting. Dynamic storage allocation: pointer types, linked lists and binary search trees as abstract data types. Classes: objects, abstract classes, inheritance and polymorphism, linked lists and binary trees as classes. Prerequisite: C- or better in CS 110.

Formal logic as a tool for mathematical proofs. Propositional calculus: boolean expressions, logic connectives, axioms, and theorems. Predicate calculus: universal and existential quantification. Modeling English propositions. Applications of formal methods to sets. Prerequisite: C- or better in MATH 103, equivalent, or appropriate score on math placement exam. (MR)

Application of formal methods to discrete analysis: mathematical induction, the correctness of loops, relations and functions, combinatorics, analysis of algorithms. Application of formal methods to the modeling of discrete structures of computer science: binary trees, graphs. Prerequisite: C- or better in CS 120.

Signal and color/pixel/voxel image representation, filtering and convolution, low- and high-pass filters, bilateral filtering. Local feature detection and description. Morphological image processing, radon transform, geometric image transformation, interpolation. Data compression. Image comparison/ categorization. Prerequisites: CS 111 and either MATH 316, MATH 350, PSYC 250, SOC 250, POSC 250, ECON 212, BA 216, or COM 240.

Linear regression/classification. Decision trees. Support vector machines. Spectral methods, k-means clustering. Neural networks. Deep learning, gradient descent, backpropagation, error evaluation/cross-validation. Prerequisites: CS 111 and either MATH 316, MATH 141, MATH 350, PSYC 250, SOC 250, or ECON 212.

Project-based class using data science on a real-world problem from start (design, data-preparation/pre-processing) to finish (report of the results). This culminating practicum course for the data science major and applied data science minor builds on one or more of the prerequisites. Topics include, but are not limited, to data mining, text mining, and applied machine learning. Prerequisite: DS 210.

This course explores the ethical challenges and societal impacts of various computational technologies, including artificial intelligence, machine learning, and data analysis. Students will critically examine topics such as algorithmic bias, the ethical implications of automated decision-making, the impact of technology on employment, and the responsibilities of data scientists in handling sensitive information. This critical reflection on the broader implications of the computational work will prepare students to be ethical leaders in the field of data science. Prerequisites: PHIL 320 or CS 320. (DS 321 is equivalent to CS 321 and PHIL 321)

Graph theory. Probabilistic graphical models. Bayes theorem. Parametric discrete and Gaussian message passing. Non-parametric message passing. Prerequisites: CS 121, DS 320, and MATH 316 or MATH 350.

Statistics for the biological sciences. Random sampling; measures of central tendency; dispersion and variability; probability; normal distribution; hypothesis testing (one-sample, two-sample, and paired-sample) and confidence intervals; multi-sample hypotheses and the one- and two-factor analysis of variance; linear and multiple regression and correlation; other chisquare tests; nonparametric statistics. Prerequisite: C- or better in MATH 150 or permission of instructor. (MR)

The theory of probability from counting and from axioms, conditional probability, independence, random variables, important discrete and continuous distributions, properties of expected value and variance, moment generating functions, law of large numbers, and central limit theorem. Other topics may include stochastic processes, random walks, hazard functions, Shannon entropy and information theory, game theory, expected time complexity of algorithms, probabilistic proofs, empirical versus Bayesian interpretations of probability, risk analysis, and applications to genetics, statistics, economics, and queuing theory. Prerequisites: C- or better in MATH 250 and either CS 121 or MATH 320.

A critical examination and evaluation of major ethical thinkers and theories including virtue theory, deontology, and utilitarianism.