INTRODUCTION TO DESKTOP DATABASES
This course will introduce students to the design, implementation and use of desktop databases. Major topics include: modeling using ER diagrams, creating and maintaining a database using a PC based application, compose and use queries in Structured Query Language, create and customize forms and reports, and integrate databases with other sources of data and applications. PREREQUISITE(S): NONE
(FORMERLY CSC 323) Application of statistical concepts and techniques to a variety of problems in IT areas and other disciplines, using a statistical package for simple data analysis. Course topics include descriptive statistics, elementary probability rules, sampling, distributions, confidence intervals, correlation, regression and hypothesis testing. PREREQUISITE(S): MAT 130 or placement
DATA ANALYSIS & STATISTICAL SOFTWARE II
Continuation of IT223. Multiple regression and correlation, residual analysis, analysis of variance, and robustness. PREREQUISITE(S): IT 223 or MAT 351.
INTRODUCTION TO DATA MINING
The course is an introduction to the Data Mining (DM) stages and its methodologies. The course provides students with an overview of the relationship between data warehousing and DM, and also covers the differences between database query tools and DM. Possible DM methodologies to be covered in the course include: multiple linear regression, clustering, k-nearest neighbor, decision trees, and multidimensional scaling. These methodologies will be augmented with real world examples from different domains such as marketing, e-commerce, and information systems. If time permits, additional topics may include privacy and security issues in data mining. The emphasis of this course is on methodologies and applications, not on their mathematical foundations. PREREQUISITE(S): IT 223
ADVANCED DATA ANALYSIS
The course will teach advanced statistical techniques to discover information from large sets of data. The course topics include visualization techniques to summarize and display high dimensional data, dimensional reduction techniques such as principal component analysis and factor analysis, clustering techniques for discovering patterns from large datasets, and classification techniques for decision making. The methods will be implemented using standard computer packages. PREREQUISITE(S): CSC 324 or consent of instructor.