Biological Data Sciences (BDS)
BDS 003, UNDERGRADUATE RESEARCH, 0 Credits
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.
BDS 004, INTERNSHIP, 0 Credits
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.
BDS 211, USE AND ABUSE OF DATA: CRITICAL THINKING IN SCIENCE, 3 Credits
Critically examine how data analysis can support legitimate conclusions from biological datasets and also how deceptive visualizations, 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.
Prerequisite: (MTH 251 (may be taken concurrently) with C- or better or MTH 251H (may be taken concurrently) with C- or better) or MTH 227 with C- or better or MTH 241 with C- or better or MTH 245 with C- or better
BDS 310, FOUNDATIONS OF BIOLOGICAL DATA SCIENCES, 4 Credits
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.
Prerequisite: MTH 251 (may be taken concurrently) with C- or better or MTH 251H (may be taken concurrently) with C- or better
Equivalent to: BDS 470, BOT 470
BDS 311, COMPUTATIONAL APPROACHES FOR BIOLOGICAL DATA, 3 Credits
Explores 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.
Prerequisite: BDS 310 with C- or better or CS 161 with C- or better or CS 162 with C- or better
BDS 401, RESEARCH, 1-16 Credits
This course is repeatable for 16 credits.
BDS 403, THESIS, 1-16 Credits
This course is repeatable for 16 credits.
BDS 411, ^ANALYSIS OF BIOLOGICAL DATA: CASE STUDIES, 3 Credits
Synthesizes knowledge and skills in biology, mathematics, statistics, and computer science to implement, in writing, an analysis strategy.
Attributes: CWIC – Bacc Core, Skills, Writing Intensive Course
Prerequisite: (BI 311 with C- or better or BI 311H with C- or better or BB 314 with C- or better or BB 314H with C- or better) and BDS 311 [C-] and (ST 352 [C-] or ST 412 [C-])
BDS 420, REFLECT ON EXPERIENTIAL LEARNING ACTIVITIES, 1 Credit
Reflect upon experiential learning projects and build professional skills, including oral and a hard-copy written presentations, a curriculum vitae or resumé, and job or graduate school application. Listen and respond to other student presentations.
BDS 446, NETWORKS IN COMPUTATIONAL BIOLOGY, 3 Credits
Emphasizes computational and applied mathematical methods for modeling and analyzing biological networks. Covers various network centralities, topological measures, clustering algorithms, probabilistic annotation models and inference methods. Introduces those concepts in the context of protein interaction, gene regulatory, and metabolic networks. Uses graph frameworks, data frames (and related data structures for data science), and programming in Python or R. CROSSLISTED as BDS 446/CS 446 and BDS 546/CS 546.
Prerequisite: CS 161 with C or better or BDS 310 with C or better or BDS 470 with C or better or BOT 470 with C or better or BOT 476 with C or better or ENGR 103 with C or better or ENGR 103H with C or better
Equivalent to: CS 446
Recommended: Completion or concurrent enrollment in CS 325
BDS 472, ADVANCED COMPUTING FOR BIOLOGICAL DATA ANALYSIS, 3 Credits
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.
Prerequisite: (BDS 311 with C- or better or CS 162 with C- or better) and (BI 221 [C-] or BI 221H [C-] or BI 205 [C-])
Equivalent to: BDS 472X, BOT 472X
BDS 474, INTRODUCTION TO GENOME BIOLOGY, 3 Credits
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.
Prerequisite: BI 311 (may be taken concurrently) with C- or better or BI 311H (may be taken concurrently) with C- or better or BB 314 (may be taken concurrently) with C- or better or BB 314H (may be taken concurrently) with C- or better or PBG 430 (may be taken concurrently) with C- or better
Equivalent to: BDS 474X, BOT 474, BOT 474X
BDS 475, COMPARATIVE GENOMICS, 4 Credits
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.
Prerequisite: (BB 314 with D- or better or BB 314H with D- or better) and (BI 311 [D-] or BI 311H [D-] or PBG 430 [D-])
Equivalent to: BOT 475
Recommended: Basic working knowledge of cell and molecular biology and genetics
BDS 477, POPULATION GENOMICS, 3 Credits
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. CROSSLISTED as BDS 477/BOT 477 and BDS 577/BOT 577.
Prerequisite: BDS 310 with C- or better or CS 161 with C- or better or CS 162 with C- or better
Equivalent to: BDS 477X, BOT 477, BOT 477X
BDS 478, FUNCTIONAL GENOMICS, 3 Credits
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.
Prerequisite: BB 314 with C- or better or BB 314H with C- or better
Equivalent to: BOT 460, BOT 478
BDS 491, CAPSTONE PROJECTS IN BIOLOGICAL DATA SCIENCE I, 3 Credits
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. Draws on skills in mathematics, statistics, computer science, and biology.
Prerequisite: (ST 352 with C- or better or ST 412 with C- or better) and (CS 162 [C-] or BDS 310 [C-] or BDS 470 [C-] or BOT 470 [C-] or BOT 476 [C-] or BB 485 [C-] or MTH 427 [C-])
BDS 492, CAPSTONE PROJECTS IN BIOLOGICAL DATA SCIENCE II, 3 Credits
Quantitative skills and biological thinking will be used to analyze and draw conclusions from biological datasets retrieved in BDS 412. This is a synthesis course that draws skills in mathematics, statistics, computer science, and biology, in which the students will process their curated datasets and draw conclusions.
Prerequisite: BDS 491 with C- or better
BDS 546, NETWORKS IN COMPUTATIONAL BIOLOGY, 3 Credits
Emphasizes computational and applied mathematical methods for modeling and analyzing biological networks. Covers various network centralities, topological measures, clustering algorithms, probabilistic annotation models and inference methods. Introduces those concepts in the context of protein interaction, gene regulatory, and metabolic networks. Uses graph frameworks, data frames (and related data structures for data science), and programming in Python or R. CROSSLISTED as BDS 446/CS 446 and BDS 546/CS 546.
Equivalent to: CS 546
BDS 570, INTRODUCTION TO COMPUTING IN THE LIFE SCIENCES, 3 Credits
Examines the basics of writing a well-organized computer program to perform tasks commonly needed for effective data analysis in the life sciences. Emphasizes reading data from various file formats, parsing relevant text-based information from data, putting information into storage structures, applying basic mathematical functions to data, and writing results to output files. Builds the foundation to apply programming to life science research.
BDS 572, ADVANCED COMPUTING FOR BIOLOGICAL DATA ANALYSIS, 3 Credits
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.
Equivalent to: BDS 572X, BOT 572X
BDS 574, INTRODUCTION TO GENOME BIOLOGY, 3 Credits
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.
Equivalent to: BDS 574X, BOT 574, BOT 574X
BDS 575, COMPARATIVE GENOMICS, 4 Credits
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.
Equivalent to: BOT 575, MCB 575
Recommended: BB 314 and (BI 311 or PBG 430) and basic working knowledge of cell and molecular biology and genetics
BDS 577, POPULATION GENOMICS, 3 Credits
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. CROSSLISTED as BDS 477/BOT 477 and BDS 577/BOT 577.
Equivalent to: BDS 577X, BOT 577, BOT 577X
BDS 578, FUNCTIONAL GENOMICS, 3 Credits
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.
Equivalent to: BOT 560, BOT 578
BDS 599, SPECIAL TOPICS, 1-16 Credits
This course is repeatable for 99 credits.