Introduction to algorithmic problem solving and computer programming. Designed to provide a foundation for further studies in computer science.

Basic techniques of data harvesting and cleaning; association rules, classification and clustering; analyze, manipulate, and visualize data using programming languages. Basic principles of probability and statistical modeling/inference to make meaning out of large datasets. Credit not awarded after STAT 3000 or greater. Cross-listed with: STAT 1870.

Intermediate programming concepts including common data structures, algorithms, style, design, documentation, testing and debugging techniques, and an introduction to object-oriented programming. Prerequisite: CS 1210 with a grade of C- or better.

An introduction to artificial intelligence including logic and rule-based approaches, heuristic search, A*, IDA*, minimax, alpha/beta pruning, expectiminimax, Markov models and MDPs, decision tree, ensemble learning / random forest, the neural model and simple multi-layer perceptrons. Other topics, if any may vary. Prerequisite: C- or better in CS 2240.

Basic data science techniques, from import to cleaning to visualizing and modeling, using the R language. Machine learning methods include regression, classification and clustering algorithms. Programming methods include user-defined functions. Prerequisite: STAT 1110, STAT 1410, or STAT 2430. Cross-listed with: STAT 2870.

Research for the Doctoral Dissertation.