Rich Lab:Relevant OSU Courses

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In-development list of OSU courses relevant to our work


  • STAT 6550 The Statistical Analysis of Time Series
    • Description: to develop knowledge of time series processes, modeling (identification, estimation, and diagnostics), and forecasting methods. Experience is gained in the statistical theory so as to be able to analyze time series data in practice.
    • Prereq: 6201, 6302 (623), or 6802 (622), and 6450 (645) or 6950; or permission of instructor. Not open to students with credit for 635.
    • Syllabi 2019
  • STAT 6620 Environmental Statistics
    • Description: survey of statistical methods for environmental data, with a focus on applications. Topics include sampling, regression, censoring, risk analysis, bioassay, time series, spatial statistics, and environmental extremes.
    • Prerequisite: 5302 (529) or 6450 (645) or 6910 or Geog 683.xx or 833.01; prereq or concur: Stat 6910; or permission of instructor. Not open to students with credit for 662.
    • Syllabi 2016
  • PUBHBIO 6210 Design and Analysis of Studies in the Health Sciences I
    • Description: Theory and application of basic statistical concepts for design of studies in health sciences, integrated with statistical software applications.
    • Prerequisite: Grad standing in PubHlth, or enrollment in MS Pharmacology program, or permission of instructor.
    • “I have taken PUBHBIO 6210 and 6211 Design and Analysis of Studies in Health Sciences I and II. 6210 is the prerequisite for 6211 and is more basic stats. I still learned a lot in that class once you get past the first month or so but 6211 was very useful in my opinion. You learn to use STATA in both which I know can be useful for some and not for others. In 6211 we learned about most of the parametric and non-parametric statistical methods and how to choose the best method based on your data and study design. I would recommend either class, especially taught by Lemeshow/Archer.” (recommended by Karen's student)
  • PUBHBIO 6211 Design & Analysis of Studies in the Health Sciences II
    • Description: A second course in applied biostatistical methods with an emphasis on regression methods commonly used in the health sciences. The focus is on linear regression and ANOVA. Integrated with use of computer statistical packages.
    • Prerequisite: Grade of B‐ or above in 6210 (701), or permission of instructor. Not open to student with credit for 702.
  • MOLGEN 5650 Analysis and Interpretation of Biological Data
    • Description: Methods of analyzing biological data including: sampling, descriptive statistics, distributions, analysis of variance, inference, regression, and correlation. Emphasizes practical applications of statistics in the biological sciences.
    • Prerequisite: Math 1149 or 1150 (150) or equiv, and 10 semester cr hrs at the 3000-level (or 300 level in the quarter system) or above in Agricultural or Biological Sciences. Not open to students with credit for 650.
  • Foundation of Statistics (a CPDA course)
    • One of four non-credit courses in the Certification in Practice of Data Analytics program, delivered in a 100% distance learning format. Introduction to statistics includes; a short discussion of where data comes from; data exploration; probability and random variables; the basics of statistical inference (e.g., sampling and inferring upon population parameters using statistics); testing statistical hypotheses and building confidence intervals; and an introduction to regression. Students will use the R software package in this course. 4 CEUs are granted upon successful completion of the course.
    • Start Date: May 10, 2017 / End Date: June 28, 2017
    • Start Date: August 22, 2017 / End Date: October 10, 2017



  • 4511 Introduction to Biological Chemistry: An introductory course in biochemistry dealing with the molecular basis of structure,metabolism, genetic replication, transcription, and translation in plants, animals, andmicroorganisms.
  • MVIMG 8040: Mass Spectrometry and Proteomics.
    • Instructor: Dr. Michael A. Freitas, Ph.D. (email:
    • Topics include: Introduction to Mass Spectrometry, Mass Spectrometry Instrumentation, Tandem Mass Spectrometry, Peptide Fragmentation, Interpretation of Mass Spectra, Peptide Mass Fingerprinting, Protein & Peptide Separations, Liquid Chromatography Tandem Mass Spectrometry, Multiple Reaction Monitoring, Bioinformatics and Computations Proteomics, Quantitative Proteomics, and Student Suggested Applications (as time permits).

Biomedical Informatics:

  • 5710 Introduction to Biomedical Informatics
    • A survey of biomedical informatics theories and methods employed in the design, implementation and management of information systems supporting basic science, clinical and translational research, clinical care, and public health. Recommended course work in computer science, statistics, anatomy, physiology, and medical terminology.
  • 5730 Introduction to Bioinformatics
    • Introduces students to basic topics of bioinformatics including sequence analyses, proteomics, microarrays, regulatory networks, sequence and protein databases. Recommended background in molecular biology and computer science.
  • 5750 Methods in Biomedical Informatics and Data Science
    • Students will gain a familiarity with methods used during the course of the design, implementation, and evaluation of Biomedical Informatics platforms, and be able to appropriately select and combine such approaches on a project-specific basis. This course will establish an application-oriented understanding of how to appropriately use such methods in order to satisfy project-specific needs and deliverables. The following items will be discussed during the summer-long course: Intro to UML Modeling, Modeling Activities & User Needs, Modeling Driven Architecture, Procedural Programming, Defining Data Types, Managing Collections of Data, Advanced Procedural Programming, Designing and Executing Database Queries, Advanced Query Operations, Web Application Design, and Accessing Databases Using Procedural Languages.
  • Biomedical informatics, BMI7830
  • 8194 Microbiome Informatics
    • This is an approach based course that teaches how to interpret and analyze genomic datasets with an aim to develop skills in processing and organizing datasets, extracting the function, structure, and evolutionary history of genes in these datasets, and discerning community structure and ecological drivers in metagenomic data. It also introduces other ‘omics data types including: viral metagenomes, metatranscriptomes, metaproteomes, metabolomes, and more.
    • Credit: 2 Units
    • Instructor: Sullivan, Kumar, Dabdoub, Bolduc
    • Offered: Autumn


  • Data Science for Scientists Club
    • Data Science for Scientists Club is recruiting new members!
    • Open to any individual (students, postdocs or faculty) interested in developing skills necessary to analyze any type of data
    • No programming experience necessary! Club is a great place for absolute beginners to get introduced to the basics of data analysis and programming
    • Develop programming skills in Python, R and associated libraries
    • Share tips and tricks for data analysis and visualization
    • Email or for more information
    • Meets every Tuesday 11:30am-1pm on Zoom ID
    • Attend our Zoom information session on Tuesday, March 23rd, Zoom ID at 11:30 am - 1 pm

Earth Science:

  • 2203 Environmental Geoscience
  • 2155 Energy and the Environment
  • 5719 Environmental Organic Chemistry

Environment and Natural Resources:

Evolution, Ecology and Organismal Biology

  • 7210 Methods in Evolution and Ecology: Essential tools for computational biology
    • Instructors: Bryan Carstens, and Ariadna Morales,
    • Students will be introduced to the command line, remote computing, basic shell scripting, the R statistical language, programming in Python, statistical tests and modeling in R, and Graphics in R. Students are required to bring laptop running either Ubuntu 16.04 LTS or macOS 10.10.5 or higher. The term assignment will be to design and conduct a novel analysis of data from your dissertation research.

Molecular Genetics:

  • 4500 General Genetics
    • The principles of genetics, including molecular genetics, transmission genetics of prokaryotes and eukaryotes, developmental and non-chromosomal genetics, recombinant DNA and genomics, and the genetics and evolution of populations.
  • 4606 Molecular Genetics
    • A comprehensive genetics course for majors covering transmission and molecular genetics; DNA replication, repair and mutation; transcription and translation; analysis and manipulation of genes at the molecular level.
  • 5701 DNA Transactions and Gene Regulation
    • Understanding mechanisms of DNA replication, DNA repair and recombination, transcription, translation, regulation of gene expression, and the experimental approaches to these topics.

Plant Pathology: