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BDS Courses

BDS Course Sheet

BDS 003

Undergraduate Research

Engage in research activities that involve generating, processing, analyzing, and/or drawing conclusions from large biological datasets. Through the research experience, acquire skills, techniques, and knowledge relevant to this field of study. In consultation with a faculty mentor, engage in research activity, and make and execute a plan for a project.

Completion of this course provides students a transcript-visible notation for a non-credit undergraduate research experience. Download the course syllabus and experiential learning activities documents for more information. Participation in this course can be used to satisfy, completely or partially, depending on hours engaged, the required Biological Data Sciences Experiential Learning Activity (see BDS 420).

BDS 004

Internship

Provides basic personal and professional skills that can be used within and outside of a work setting. Through practice, this experience guides students in building and maintaining positive professional relationships, networking/mentoring relationships, and enhances students’ understanding of the connection between theory and practice in the use of large datasets in scientific investigation.

Completion of this course provides students a transcript-visible notation for a non-credit undergraduate internship experience. Download the course syllabus and experiential learning activities documents for more information. Participation in this course can be used to satisfy, completely or partially, depending on hours engaged, the required Biological Data Sciences Experiential Learning Activity (see BDS 420).

BDS 211

Use and Abuse of Data: Critical Thinking in Science

Critically examine how data analysis can support legitimate conclusions from biological datasets and also how biased sampling, misleading comparisons, and spurious reasoning can lead to false conclusions. Analyze data to break down the logical flow of an argument and identify key assumptions, even when they are not stated explicitly.

BDS 310

Foundations of Biological Data Sciences

Develops competency in scientific computing and data analysis with broad applications to the life sciences. Introduces the Python programming language as a versatile, powerful tool for visualizing and analyzing data and for performing reproducible research. Focuses on real-world datasets originating across the life sciences. Provides a foundation for future work in data-intensive disciplines.

  • Prerequisites: MTH 251 or MTH 227
  • Co-requisites: None
  • Instructor: Dr. Tim Warren

BDS 311

Computational Approaches for Biological Data

The theory and practice behind widely used computational methods for biological data analysis. Covers principles of programming for reproducible research as well as computational techniques for testing hypotheses, inferring dataset parameters, and making predictions from biological data.

  • Prerequisites: BDS 310 OR CS 161 OR CS 162
  • Co-requisites: None
  • Instructor: Dr. Tim Warren
  • Course advertisement: Click here for a course flyer or click here to access the course website.

BDS 401

Research

Engage in research activities that involve generating, processing, analyzing, and/or drawing conclusions from large biological datasets. Through the research experience, acquire skills, techniques, and knowledge relevant to this field of study. In consultation with a faculty mentor, engage in research activity, and make and execute a plan for a project.

Completion of this course provides students a transcript-visible notation and credit for undergraduate research experience. This course is available for A/F and S/U grading options. If the research mentor does not have an assigned section, please contact the director of the BDS program and/or the BPP office for assistance. Download the course syllabus and experiential learning activities documents for more information. Participation in this course can be used to satisfy, completely or partially, depending on hours engaged, the required Biological Data Sciences Experiential Learning Activity (see BDS 420).

BDS 403

Thesis

Engage in research activities that involve generating, processing, analyzing, and/or drawing conclusions from large biological datasets. Through the research experience, acquire skills, techniques, and knowledge relevant to this field of study. In consultation with a faculty mentor, engage in research activity, and make and execute a plan for a project. Report and reflect on activities in a written thesis.

If the research mentor does not have an assigned section, please contact the director of the BDS program and/or the BPP offices for assistance. Download the course syllabus and experiential learning activities documents for more information. Participation in this course can be used to satisfy, completely or partially, depending on hours engaged, the required Biological Data Sciences Experiential Learning Activity (see BDS 420).

BDS 411

Analysis of Biological Data: Case Studies

Case studies; synthesize previously acquired knowledge and skills in biology, mathematics, statistics, and computer science to implement, in writing, an analysis strategy. This course fulfills the Baccalaureate Core requirement for the Writing Intensive Curriculum (WIC) category for students majoring in Biological Data Sciences majors.

  • Prerequisites: ((BI 311 OR BI 311H) OR (BB 314 OR BB 314H) OR MB 310) AND ((MTH 252 OR MTH 252H) OR MTH 228) AND CS 261 AND (ST 352 OR ST 412)
  • Co-requisites: None
  • Instructor: Dr. Sam Leiboff
  • Course advertisement: Click here for a course flyer.

BDS 420

Reflect on Experiential Learning Activities

Reflect upon experiential learning projects and build professional skills, including oral and a hard-copy written presentations, building a curriculum vitae or resumé, preparing job or graduate school application, listening and responding to other student presentations.

  • Prerequisites: completion of Experiential Learning project
  • Co-requisites: None
  • Instructor: Dr. Jeff Chang

BDS 472/572

Advanced Computing for Biological Data Analysis

Examines machine learning or pattern recognition applications in analyses of biological data. Applies supervised learning techniques for recognizing useful patterns in genome-scale datasets, with emphasis on carefully considered scientific interpretation of machine learning model outcomes. Explores Python Scikit-Learn libraries for implementing model-based analyses.

  • Prerequisites:BDS 472: (BDS 311 or CS 161 or CS 162) and (BI 221/221H or BI 205)
    BDS 572: BDS 570
  • Recommended: BDS 474/574 AND MTH 341
  • Instructor: Dr. Molly Megraw

BDS 474/574

Introduction to Genome Biology

Explores how genomes underlie and influence biological phenomena, across the diversity of life, from prokaryotic microbes to eukaryotic multicellular organisms. Examines genome organization in the first part of the course: the structure of chromosomes and chromatin; genes and gene families; and mechanisms that remodel genomes, such as mutation, recombination and transposable elements. Summarizes models of genome expression and regulation in the second part of the course: transcriptional and post-transcriptional regulatory mechanisms and genotype-to-phenotype relationships. Illustrates how recent technological advances and genome-wide assays enable investigation of these topics.

  • Prerequisites: (BI 311 or BI311H with C- or better) or (BB 314 or BI 314H with C- or better)
  • Co-requisites: None
  • Instructor: Dr. Jeff Chang

BDS 475/575

Comparative Genomics

Explores principles of comparative genomics. Examines methods for genome assembly and annotation. Discusses genomic approaches for the study of structural change, whole genome duplication, gene family evolution, gene networks, gene regulation and epigenetics. Lab topics include the analysis of next generation sequencing data and conducting comparative genomic analyses.

 

BDS 477/577

Population Genomics

Translate fundamental knowledge on genetics and genomics to study evolution and functional genes in populations. Apply skills in computer science to process, analyze, and draw conclusions from microbial populations at the ecosystem level.

  • Prerequisites:BDS 310 OR CS 161 OR CS 162
  • Suggested: BI 454 
  • Recommended:
    • BDS 477: BI 311 OR BDS 474
    • BDS 577: BDS 570 AND BDS 574
  • Instructor: Dr. Jessie Uehling
  • Course advertisement: Click here for a course flyer.

BDS 478/578

Functional Genomics

Introduces conceptual approaches and associated laboratory techniques that rely on genome-scale datasets to investigate the function of, and interactions between, genes as well as their RNA/protein products. Examples include: predicting protein function based on nucleotide and amino acid sequence analysis; large-scale genetic approaches to identifying novel genotype-phenotype associations; and analysis of transcriptomic, proteomic and metabolomic datasets, which measure changes in RNA transcripts, proteins and metabolites, respectively, to explore gene function and cellular/organismal networks. Provides a conceptual framework for understanding how the wide range of available large-scale technologies can be applied to solve biological problems.

  • Prerequisites: BI 314 [C-] or BI 314H [C-]
  • Co-requisites: None
  • Instructor: Dr. Jeff Anderson

BDS 491

Capstone Projects in Biological Data Science I

Quantitative skills and biological thinking will be used to analyze and draw conclusions from real-world biological datasets. Projects will be completed in the context of small groups. This is a synthesis course that draws skills in mathematics, statistics, computer science, and biology.

  • Prerequisites: (ST 352 OR ST 412) AND (CS 162 OR BOT 476 OR BB 485 OR MTH 427) OR instructor consent
  • Corequisites: none
  • Instructor: Dr. Maude David
  • Course advertisement: BDS 491 flyer

BDS 570

Introduction to Computing in Life Sciences

Covers the basics of writing a well-organized computer program to perform tasks that are commonly needed for effective data analysis in the life sciences. Incorporates reading data from a variety of file formats, parsing relevant information from data which comes in as text, putting this information into storage structures that make sense for the task at hand, applying basic mathematical functions to the data, and writing results to an output file. Provides students with the foundation to rapidly expand their knowledge of Python and other programming languages as needed in the future.

 BDS 599

These are a series of courses offered by the Center for Quantitative Life Sciences.

Topics include:

  • Command-Line Data Analysis
  • Environmental Sequence Analysis
  • GBS (Genotyping by sequencing)
  • Introduction to Unix/Linux
  • Python I
  • Python II
  • RNA Sequencing

Please note that courses are offered in different terms. Learn more about course workshops offered through CQLS.