Courses

A scientific examination of the relationships of digital technology and big data to the individual and society. Topics include issues of privacy and ethics, human machine interaction artificial intelligence, interpersonal communication, law and crime, healthcare, education, business, effects on American and global social structure, media, national security and politics, and science and technology the scientific community.

An introduction to the fundamentals of Structured Query Language (SQL), relational database management systems, and data visualization. Topics include SQL syntax including data definition language vs data manipulation language, basic and advanced join patterns, subqueries, common table expressions, user defined functions, query optimization and data quality pipelines.

A continuation of CSCI 80. Students will learn to program databases using SQL. Microsoft Access integrated with Visual Basic. In addition, object-oriented database programming such as Oracle and Jasmine will be covered

ER modeling; functional dependencies and relational design; file organization and indexing; relational algebra and calculi as query languages; SQL; transactions, concurrency and recovery; query processing. Programming projects.

Computer communications and networks; carriers, media, interfaces (RS 232, RS 422, CCITT); circuit types, data codes, synchronous and asynchronous transmission; protocols (OSI, TCP/IP); modems, multiplexors, and other network hardware; error correction and encryption; voice and data switching: local area networks, ISDN, packet switching; issues in the architecture, design, and management of networks.

Introduction to statistical programs and programming languages to assist with conducting research; data gathering, processing, analysis, interpretation and report writing. Not open to students who have taken SOC 333.

Data Mining and data warehousing: data warehouse basics; concept of patterns and visualization; information theory, information and statistics linkage; temporal- spatial data; change point detection; statistical association patterns; pattern inference and model discovery; Bayesian networks; pattern ordering inference; selected case study.

Philosophy of modeling with data. Prediction via linear models and machine learning including support vector machines and random forests. Probability estimation and asymmetric costs. Underfitting vs. overfitting and model validation. Formal instruction of data manipulation, visualization and statistical computing in a modern language. Writing Intensive (W). Recommended corequisites include ECON 382, 387, MATH 341, MATH 343 or their equivalents.

Philosophy of modeling with data. Prediction via linear models and machine learning including support vector machines and random forests. Probability estimation and asymmetric costs. Underfitting vs. overfitting and model validation. Formal instruction of data manipulation, visualization and statistical computing in a modern language. Not open to students who are taking or who have received credit for MATH 642. Writing Intensive (W).

Fundamentals data structures and their implementations: stacks, queues, trees (binary and AVL), heaps, graphs, hash tables. Searching and sorting algorithms. Runtime analysis. Examples of problem-solving using greedy-algorithm, divide- and-conquer, and backtracking.