Ph.D., M.S., University of Minnesota
B.A., Carleton College
Mark Werness
Associate Professor and Chair
Degree
Office
OSS 403
Phone
(651)9625471
Toll Free
(800) 3286819 ext. 5471
Email
Mail
University of St. Thomas
Mail Number OSS 402
2115 Summit Avenue
St. Paul, MN 55105
Mail Number OSS 402
2115 Summit Avenue
St. Paul, MN 55105
Professional Interests
My current academic interests are applied statistics, computer applications in experimental science, and STEM teacher preparation. In addition, I was the first Program Director for the new Statistics major at UST, fall 2009 to summer 2011.
Spring 2016 Courses
Course  Section  Title  Days  Time  Location  

CISC 230  02  Object Oriented Design & Prog  See Details  *  *  
Description of course Genetics B/ Lab: 
CRN: 21643
4 Credit Hours
Instructor: Mark E. Werness
(Formerly QMCS 281) Programming and problem solving using an objectoriented approach. Builds on the procedural language foundation developed in CISC 130 or 131. Topics include: how procedural design differs from objectoriented design, algorithms, modeling, design requirements and representation, Uniform Modeling Language specification, implementation of objectoriented models, testing, and verification, and elementary design patterns. Lab included
Prerequisites: A minimum grade of C in CISC 130 or 131
Schedule Details


STAT 220  04  Statistics I  M  W  F    0935  1040  OSS 329  
Description of course Genetics B/ Lab: 
CRN: 20841
4 Credit Hours
Instructor: Mark E. Werness
Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chisquare, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
Schedule Details

Summer 2016 Courses
Course  Section  Title  Days  Time  Location 

Fall 2016 Courses
Course  Section  Title  Days  Time  Location  

IDTH 400  01  Data Mining & Machine Learning   T  R     1330  1510  OSS 333  
Description of course Genetics B/ Lab: 
CRN: 41884
4 Credit Hours
Instructor: Mark E. Werness
Introduction to statistical learning methods, from a statistical and computational perspective, to deal with massive and complex data. Topics include: Introduction; creating a project and diagram. Data preparation; defining and exploring data sources. Pattern discovery; cluster analysis, market basket analysis. Decision trees; cultivating and pruning decision trees, autonomous tree growth. Regression; transforming inputs, categorical inputs, polynomial regression. Neural Networks; input selection, stopped training. Model assessment; fit statistics, graphs, separate sampling. Model implementation; scored data sets, score code models. Applications. This course will give the basic ideas and intuition behind these methods, and special emphasis will be placed on their application through statistical software. Prerequisites: IDTH 360, MATH 113, and one of MATH 128 or MATH 240, and one of STAT 320 or STAT 333.
Schedule Details


STAT 220  04  Statistics I  M  W  F    0935  1040  OSS 329  
Description of course Genetics B/ Lab: 
CRN: 40946
4 Credit Hours
Instructor: Mark E. Werness
Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chisquare, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
Schedule Details


STAT 220  05  Statistics I  M  W  F    1055  1200  OSS 329  
Description of course Genetics B/ Lab: 
CRN: 40947
4 Credit Hours
Instructor: Mark E. Werness
Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chisquare, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
Schedule Details
