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      • Baylor Data Scientists Partner on National Science Foundation Grant to Use Sports Analytics to Prepare Next Generation of Data Science Professionals
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Baylor BU Statistical Science Graduate Prof. M.S. and Elective Courses
  • Doctor of Philosophy (Ph.D.)
  • Master of Science (M.S.)
  • Professional Master of Science (M.S.)
  • Minor in Statistics
  • Graduate Student Handbook and Orientation
  • M.S./Ph.D. Statistics Courses
  • Prof. M.S. and Elective Courses
  • How to Apply
  • FAQ

4+1 B.S./M.S., Professional M.S. and Elective Courses

These courses may be taken for the Professional M.S. in Statistics or for the graduate Minor in Statistics.  They may not be taken for credit for the M.S. in Statistics or the Ph.D. in Statistics.

5300  Statistical Methods

Introduction to descriptive and inferential statistics. Topics may be selected from the following: descriptive statistics and graphs, probability, regression, correlation, tests of hypotheses, interval estimation, measurement, reliability, experimental design, analysis of variance, nonparametric methods, and multivariate methods.

5301  Introduction to Experimental Design

Prerequisite(s): Graduate standing.

Simple and complex analysis of variance and analysis of covariance designs. The general linear model approach, including full-rank and less than full-rank models, will be emphasized.

5303  Applied Regression Analysis

Pre-requisite(s):  STA 5300 or equivalent

Regression modeling, estimation, and diagnostics with emphasis on applications. Topics include simple linear regression, multiple regression, logistic regression, and Poisson regression. The statistical programming language R is used.

5320 Predictive Analytics

Pre-requisite(s): STA 5303 Concepts, methods, and tools used for predictive modeling and data analytics with applications are considered

The focus of this course is on advanced tools using various multivariate regression techniques, statistical modeling, machine learning, and simulation for forecasting. Practical applications are emphasized.

5350 Statistical Machine Learning

Pre-requisite(s): STA 5303

Fundamental topics of machine learning including supervised/unsupervised learning, cost function optimization, feature selection and engineering, and bias/variance trade-off. Learning algorithms including classification methods, support vector machines, decision trees, neural networks, and deep learning are covered.

5360  Introduction to Bayesian Data Analysis

Prerequisite(s): STA 3381 or consent of instructor

An overview of analytic and computational methods in Bayesian inference beginning with two-sample t-inference procedures and extending through regression, focusing on state-of-the art software for Bayesian computation.

5361  Applied Time Series Analysis

Prerequisite(s): STA 3386 or STA 5303 or equivalent or concurrent enrollment or consent of instructor

Statistical methods of analyzing time series. Model identification, estimation, forecasting, and spectral analysis will be discussed. Applications in a variety of areas including economics and environmental science will be considered. The R statistical programming language will be used.

5370  Applied Sampling Techniques

Pre-requisite(s):  Grade of C or better in one of STA 2381 or STA 5300 or equivalent course in statistical methods

Planning, execution, and analysis of sampling from finite populations.  Simple random, stratified random, ratio, systematic, cluster, subsampling, regression estimates and multi-frame techniques are covered.  Using computer software for analyzing data collected from designs covered in class.

5371  Methods in Data Mining and Management

Pre-requisite(s):  STA 3386, STA 5303 or equivalent course, or consent of instructor

This course introduces the methods and practice of data mining.

5372 Statistical Process Control

Pre-requisite(s): STA 3381 or equivalent; STA 2381 or equivalent

Development of statistical concepts and theory underlying procedures used in statistical process control applications. Topics include sampling inspection procedures, continuous sampling procedures, theory of process control procedures, and experimental design and response surface analysis to design and analyze process experiments.

5373 Computational Statistical Methods

Pre-requisite(s):  STA 2381 or STA 3381 or consent of the instructor.

Computational methods using statistical packages and programming.

5374 Applied Sampling Techniques

Pre-requisite(s): A grade of C or better in any one of STA 2381 or STA 5300 or an equivalent course in statistical methods

Planning, execution, and analysis of sampling from finite populations. Simple random, stratified random, ratio, systematic, cluster, subsampling, regression estimates, and multi-frame techniques are covered. Use of computer software for analyzing data collected from designs covered in class.

5376  Methods in Biostatistics

Pre-requisite(s):  STA 2381 or STA 5300 or an equivalent course in statistical methods

A survey of methods of data analysis for biostatisticians in the biomedical and pharmaceutical fields.  Regression analysis, experimental design, categorical data analysis, clinical trials, longitudinal data, and survival analysis.

5384   Multivariate Statistical Methods

Discriminant analysis, canonical correlation analysis, and multivariate analysis of variance.

Department of Statistical Science

Department of Statistical Science
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Waco, TX 76798

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Baylor BU Statistical Science Graduate Prof. M.S. and Elective Courses
  • About
    Back
    • Events
    • Paper Club
    • News
      Back
      • Congratulations to Dr. Jason Maddox
      • Congratulations to Dr. Luke Durell
      • JSM 2022 Baylor Statistics Alumni
      • JSM 2022
      • "Outstanding 1st year Ph.D. student for 2021-2022 academic year".
      • A $1.1 million NSF grant is helping educators use sports analytics to prepare the next generation of data scientists.
      • Baylor Data Scientists Partner on National Science Foundation Grant to Use Sports Analytics to Prepare Next Generation of Data Science Professionals
      • Congratulations to Dr. Yuhan Ma
      • Congratulations to James Otto who received the "Outstanding Overall Student Award" for the 2021-2022 academic year. 
      • Outstanding Teaching Assistant Award
      • SRCoS
      • Section for Statistical Programmers and Analysts Webinar
    • Visit Information
    • Graduate Program Information
    • Newsletters
    • Testimonials
      Back
      • Sarah Stanley
      • Chris Casement
      • Monica Bennett
  • Faculty Directory
    Back
    • Adjunct Faculty Directory
  • Graduate
    Back
    • Doctor of Philosophy (Ph.D.)
      Back
      • Biostatistics Concentration
      • Data Science Concentration
    • Master of Science (M.S.)
      Back
      • Admission prior to Summer 2019
      • Admission Summer 2019 or after
    • Professional Master of Science (M.S.)
    • Minor in Statistics
    • Graduate Student Handbook and Orientation
    • M.S./Ph.D. Statistics Courses
    • Prof. M.S. and Elective Courses
    • How to Apply
    • FAQ
  • Undergraduate
    Back
    • Minor in Statistics
    • Bachelor of Science (B.S.)
      Back
      • Actuarial Science Concentration
      • Sports Analytics
    • Departmental Policy for Course Replacement, Waivers, and Permits
    • Undergraduate Course Descriptions
  • Research
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