Ph.D., M.S., University of Minnesota
B.A., Carleton College
Mark Werness
Associate Professor
Degree
Office
OSS 424
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
Social
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.
Summer 2017 Courses
Course  Section  Title  Days  Time  Location  

CISC 476  01  Experiential Learning            
Description of course Genetics B/ Lab: 
CRN: 30685
2 Credit Hours
Instructor: Mark E. Werness
Schedule Details


IDTH 393  I1  Individual Study            
Description of course Genetics B/ Lab: 
CRN: 30660
4 Credit Hours
Instructor: Mark E. Werness
Schedule Details

Fall 2017 Courses
Course  Section  Title  Days  Time  Location  

STAT 220  06  Statistics I  M  W  F    1055  1200  OSS 431  
Description of course Genetics B/ Lab: 
CRN: 40846
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  07  Statistics I  M  W  F    1215  1320  OSS 432  
Description of course Genetics B/ Lab: 
CRN: 40847
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 400  01  Data Mining & Machine Learning   T  R     1330  1510  OSS 431  
Description of course Genetics B/ Lab: 
CRN: 42577
4 Credit Hours
Instructor: Mark E. Werness
(Formerly IDTH 400) 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: CISC 130 or 131, and MATH 113, and STAT 320 or 333.
Schedule Details


STAT 460  03  Statistical Research/Practicum            
Description of course Genetics B/ Lab: 
CRN: 43340
4 Credit Hours
Instructor: Mark E. Werness
Students will work individually with the instructor to identify a statistical research topic of current interest or to identify a real practical problem, for which statistics can be used to produce a feasible solution. State and local governments, companies, businesses, TV channels, or even faculty doing research should be the natural source of real practical problems to be solved. For either the research or the practical problem, the final outcome should be a report with publication potential.
Schedule Details

JTerm 2018 Courses
Course  Section  Title  Days  Time  Location 
