Stat 315 Mathematical Statistics I  

Theory and applications in statistical analysis. Combinations, permutations, probability, distributions of discrete and continuous random variables, expectation and common distributions (including normal).
        Prerequisite: Math 218 Discrete Math and Math 221 Calculus II. 

 

Stat 321 Elementary Data Mining  
Introduction to basic concepts behind data mining. Survey of data mining applications, techniques and models. Discussion of ethics and privacy issues with respect to invasive use. Introduction to data mining software suite.
        Prerequisite: Stat 104, 200, 215, or 315 (a first semester course in statistics).

 

Stat 322: Data Mining Techniques  

Exploration of data mining methodologies.  Topics may include decision tables, decision trees, classification rules, association rules, clustering, statistical modeling, and linear models.  More extensive use of SPSS' Clementine data mining suite.  Visit http://www.spss.com/clementine/ for more information.
        Prerequisite: Stat 321, plus one of Stat 201, 216, 416, or 453 (a second semester course in statistics).

Stat 323: Applications of Data Mining  

Capstone course for Certificate in Data Mining.  Case studies using large data sets taken from real-life applications.  Problems encountered when dealing with large data sets.  How much data is enough?  Extensive use of data mining software. 
        Prerequisites: Stat 322; Math 122; Math 218

Stat 416: Mathematical Statistics II  
Continuation of theory and applications of statistical inference. Elements of sampling, point and interval estimation of population parameters, tests of hypotheses and the study of multivariate distributions.
        Prerequisite: STAT 315 

 

Stat 455 Experimental Design  
Introduction to experimental designs in statistics. Topics include complete randomized blocks, Latin square and factorial experiments.
        Prerequisite: STAT 201 or 216 or 416

 

Stat 521 Introduction to Data Mining  
Introduction to the fundamental concepts of data mining.  Motivation for and applications of data mining.  Survey of techniques and models.  Potential pitfalls of machine learning.  Introduction to SPSS' Clementine data mining suite.  Visit http://www.spss.com/clementine/ for more information.
        Prerequisite: Stat 104, 200, 215 or Stat 315 (i.e., a first semester course in Stats) or permission of instructor.

 

Stat 522: Data Mining Methods  

Intensive investigation of data mining methodologies.  Topics may include decision trees, classification, association, clustering, attributes, statistical modeling, Bayesian classification, k-nearest neighbors, CART.  Extensive use of SPSS' Clementine data mining suite.
        Prerequisites: Stat 521 and Stat 315 plus one of Stat 201, 216, 416, or 453 (a second semester course in statistics), or permission of Department Chair.  

 

Stat 523 Applied Data Mining   

Graduate level application of student’s data mining expertise.  Course based on case studies using large data sets taken from real-life applications.  Statistical model building and deployment.  Model choice.  Visualization, report writing, graphical presentation.  Extensive use of data mining software.
        Prerequisites: Stat 416 and Stat 522.

 

Stat 524 Advanced Methods in Data Mining

Advanced techniques in data mining. Text data mining, text classification, naive Bayes, the EM algorithm, optimization, visualization, genetic algorithms, data augmentation, Markov-chain Monte Carlo techniques, knowledge extraction.  Extensive use of data mining software. 
        Prerequisite: Stat 523.

 

Stat 525 Web Mining

Introduction to techniques of mining information from the web.  Web basics and HTTP, data sources on the web, personalization, working with logs, forms, and cookies, user identification and path analysis.  E-Metrics.  Use of web mining software.
        Prerequisites: Stat 521; Stat 201 or 216 or 416 or 453.

Stat 551 Applied Stochastic Processes

IAn introduction to stochastic processes. Topics include Markov, Poisson, birth and death, renewal and stationary processes. Statistical inferences of Markov processes are discussed. 
        Prerequisites: Stat 315 and Math 228.

 

Stat 567: Linear Models  

Introduction to the methods of least squares. 

Topics include general linear models, least squares estimators, inference and hypothesis testing.
        Prerequisite: STAT 416 and MATH 228.

Stat 570: Applied Multivariate Analysis  
Introduction to analysis of multivariate data with examples from economics, education, psychology and health care. Topics include multivariate normal distribution, Hotelling’s T2, multivariate regression, analysis of variance, discriminant analysis, factor analysis and cluster analysis. Computer packages assist in the design and interpretation of multivariate data.
       Prereq.: Stat 216 or 201 or 453 with permission of instructor, MATH 228 or STAT 416.  

Stat 575: Mathematical Statistics III  

Continuation of theory and applications of statistical inference.  Advanced topics in the estimation of population parameters and the testing of hypotheses.  Introduction to Bayesian methods, regression, correlation, and the analysis of variance.
        Prerequisite: STAT 416.

CS 290: Topics in Computer Science: Data Mining

See Dr. Joan Calvert calvert@ccsu.edu for this course description.

 

 

CS 501: Foundations of Computer Science  
 Software design for structuring and manipulating data. Topics include tree structures, graphs, data abstraction and external sorting.
        Prerequisites: MAT 122 or 125 AND CS 500 or CS 151+152.

 

CS 570: Topics in Artificial Intelligence: Machine Learning

See Dr. Zdravko Markov markovz@ccsu.edu for this course description.

 

CS 580: Topics in Computer Science: Data Mining   
Data Mining studies algorithms and computational paradigms that allow computers to find patterns and regularities in databases, perform prediction and forecasting, and generally improve their performance through interaction with data. It is currently regarded as the key element of a more general process called Knowledge Discovery that deals with extracting useful knowledge from raw data. The knowledge discovery process includes data selection, cleaning, coding, using different statistical, pattern recognition and machine learning techniques, and reporting and visualization of the generated structures. The course will cover all these issues and will illustrate the whole process by examples of practical applications. Important related technologies as Data Warehousing and  On-line Analytical Processing (OLAP) will be also discussed. The students will use recent Data Mining software.

Prerequisites: CS 501 and CS 502, basic knowledge of algebra, discrete math and statistics: CS 501 and CS 502, basic knowledge of algebra, discrete math and statistics.

 

Stat 200: Business Statistics I  
Application of statistical methods used for a description of analysis of business problems. Topics include frequency distributions, graphical presentations, measures of relative position, measures of central tendency and variability, probability distributions, including binomial and normal, confidence intervals and hypothesis testing. 

Prerequisite: Math 101

Math 122 Calculus I

Limits and continuity, derivatives, applications of derivatives, anti-derivatives, definite integrals and applications of definite integrals. Mode 2 CCSU General Education credit.
        Prerequisite: MATH 115 (C- or higher), MATH 121 (C- or higher) or Placement Exam.
      

 

Math 218 Discrete Mathematics
Topics include logic, induction, recursion, combinatorics, matrices, graph theory, set theory and number theory.
        Prerequisite: MATH 122 (C- or higher).

 

Math 221 Calculus II
Continuation of MATH 122. Differentiation and integration of transcendental functions, techniques of integration, indeterminate forms, improper integrals, sequences and infinite series. Mode 2.
        Prerequisite: MATH 122 (C-or higher)

Register at

for Data Mining Online