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BIOL 343

Data Analysis for Biologists

3.00 units · Undergraduate

Fall 2025

Topics

  • Introduction to data science and data management
  • Central moments and other summary statistics
  • Generalized Linear Models (GLM) and experimental design
  • Language
  • Development
  • Learning
  • Problem solving
  • Decision making

Readings

  • Listed material: Before Lecture:
  • Listed material: During Lecture Session:
  • Listed material: During Tutorial/Help Sessions:
  • Listed material: Review feedback and prepare for next week. Assessments
  • Listed material: 25% Weekly Assignments – usually due 24 to 48 hours after lecture
  • Listed material: 20% Participation and Peer-Review – peer evaluations of group work
  • Listed material: 45% Final Exam – a very simple, hand-written exam to Support & Flexibility
  • Required: Course Textbooks Edition(s) Publisher
Fall 2024 · Syllabus

Topics

  • Introduction to data science and data management
  • Central moments and other summary statistics
  • Generalized Linear Models (GLM) and experimental design
  • Development

Readings

  • Listed material: This introductory textbook is available for purchase (print) with a free (online) version on the course website.
Fall 2023 · Advanced Data Analysis for Biologists

Topics

  • Statistical inference
  • Regression
  • Analysis of variance
  • Learning

Readings

  • Required: Motulsky H 2016 Essential Biostatistics. A Nonmathematical Approach. Oxford University Press
2022 · Syllabus

Topics

  • Intro to statistical thinking and data management in R
  • Statistical Inference: the population, the sample and statistical distributions
  • Basic linear models with QA/QC
  • In-class Test #1
  • Generalized Linear Models (GLM) and experimental design
  • Linear Mixed Models (LMM)
  • In-class Test #2
  • Intro to machine learning and Principal Components Analysis (PCA)

Readings

  • Listed material: The following texts are required for this course: Essential Biostatistics. A Nonmathematical Approach by Harvey Motulsky (Oxford University Press). Suggested readings are assigned each week and the content is tested on two in-class
Fall 2021

Topics

  • Readings combined with lectures/tutorials over all fundamental statistics, including frequency
  • summary measures of variation, probability theory, normal distribution, sampling theory, confidence
  • Correlation
  • Regression
  • Development
  • Problem-solving

Readings

  • Required: Essential Biostatistics. A Nonmathematical Approach by Motulsky (Oxford University Press) Suggested: R for Data Science by Hadley Wickham
2017-2018

Topics

  • application of the general linear model to the analysis of biological data using R
  • practical application of the general linear model (anova, regression, categorical
  • w3 descriptive statistics and statistical literacy
  • w5 general linear models (LMs): regression and correlation
  • w7 LM: multiple regression, diagnostics and graphs
  • w8 LM: one way ANOVA
  • w9 LM: two way ANOVA and ANCOVA, interactions
  • REQUIRED: Motulsky H (2016) Essential Biostatistics. Oxford University
  • Learning
2016-2017

Topics

  • biological
  • LEARNING
  • regression
  • correlation)
  • Multivariate
  • Statistical inference
  • Biostatistics
2014-2015

Topics

  • GLM: regression and correlation
  • GLM: logistic regression
  • intro to multivariates: regression, PCA
  • ANOVA
Sources