Data Science Courses
CRS  SEC  CRN  Title  CRD  Time  Building  Instructor  Notes  Max  CUR  REM  SEM 

BIO 240  0  20053  Biostats for Life Scientists Instructors Probability, descriptive statistics, and proper application, interpretation, and reporting of inferential statistics for biological research. Instruction in experimental design and use of statistical and graphics software. Recommended for premed and preveterinary students as well as those who plan to enroll in Biology group investigation or independent study courses. Satisfies Mathematical & Quantitative Thought distribution requirement. Prerequisites

1  T R 0950  1105am  WALL106  Kevin Smith  PRQ MQRQ 
20  0  20  Spring 2022 
BIO 309  0  20058  Genomics Instructor Students use published resources to understand how genomescale information (e.g., DNA sequences, genome variations, transcriptomes, proteomes, and clinical studies) can provide a systems biology perspective. Students also use databases and bioinformatics tools to analyze data and post their analyses online. May be taken simultaneously with BIO 343. Counts as an elective in the Data Science interdisciplinary minor. Prerequisites

1  M W F 1200  1250pm  WALL243  Debbie ThurtleSchmidt  PRQ,R, PRQ  32  0  32  Spring 2022 
BIO 343  0  20063  Laboratory Methods in Genomics Instructor In this labonly course, students participate in a real genome sequencing project (sequencing performed offsite). Students analyze sequences and annotate genes in the genome. This original research is computer intensive and contributes to the growing body of knowledge in genomics. Students participate in collaborative research projects and generate reports of their findings. May be taken simultaneously with BIO 309. Counts as an elective in the Data Science interdisciplinary minor. Prerequisites

1  T R 0950  1105am  WALL380  Debbie ThurtleSchmidt  Footnote 05,FEE, R, Note 123, Note 123+, FEE, PRQ,4+, R,B1, Footnote 123,FEE, Footnote 03, PRQ, B1  18  0  18  Spring 2022 
CSC 110  0  20120  Data Science & Society Instructor An introduction to methods of data science, including computer programming, data visualization, and statistical analysis. Students will collect, process, analyze, and present data in order to expose and help each other understand issues of social and economic justice. All work will be done in R, a freely available data analysis software package. Satisfies a Mathematical & Quantitative Thought requirement. Prerequisites 
1  T R 0950  1105am  WATSON132  Laurie Heyer  4,PRM,PRQ, PRQ MQRQ, JEC 
24  0  24  Spring 2022 
CSC 120  0  20121  Programming in the Humanities Instructor Computational methods have significantly broadened and deepened the possibilities of inquiry in the Humanities. Programming skills have allowed textual scholars, in particular, to take advantage of enormous digitized corpora of historical documents, newspapers, novels, books, and social network data like Twitter feeds to pose new questions to the written word. We can now trace the changing semantics of words and phrases across millions of documents and hundreds of years, visualize centuriesold plot structures in new ways through sentiment analysis and character networks, and solve longstanding riddles of authorship attributionamong many other exciting feats. This course offers an introduction to computer science through applications in the Humanities. Students will learn to program in the Wolfram Language, aka Mathematica. The Wolfram Language is especially well suited for humanists: its rich documentation and natural language processing capabilities ensure a gentle introduction for firsttime programmers, its symbolic computation structure allows us to work with texts written in any language and any alphabet, while its Notebook environment provides an interactive medium for publishing and sharing our results with peers. Mathematica also provides a great springboard for further work in computer science, physical computing, and Digital Studies more broadly. Satisfies a minor requirement in Computer Science.
Prerequisites (Spring) 
1  T R 0245  0400pm  LIBB110  Jakub Kabala  XLST, PRQ MQRQ 
12  0  12  Spring 2022 
CSC 121  A  20122  Programming & Problem Solving Instructor An introduction to computer science and structured programming, including algorithmic thinking, using control structures, essential data structures, creating functions, recursion, and objectoriented programming. Satisfies the Mathematical and Quantitative Thought requirement. Prerequisites 
1  M W F 1050  1140am  WATSON132  Tabitha Peck  Footnote 23, PRQ MQRQ 
24  0  24  Spring 2022 
CSC 121  B  20123  Programming & Problem Solving Instructor An introduction to computer science and structured programming, including algorithmic thinking, using control structures, essential data structures, creating functions, recursion, and objectoriented programming. Satisfies the Mathematical and Quantitative Thought requirement. Prerequisites 
1  M W F 1200  1250pm  WATSON132  Raghu Ramanujan  XLST, Footnote 02 MQRQ 
24  0  24  Spring 2022 
CSC 121  C  20124  Programming & Problem Solving Instructor An introduction to computer science and structured programming, including algorithmic thinking, using control structures, essential data structures, creating functions, recursion, and objectoriented programming. Satisfies the Mathematical and Quantitative Thought requirement. Prerequisites 
1  T R 0110  0225pm  WALLB05  Catherine Nemitz  PRQ, Footnote 24 MQRQ 
24  0  24  Spring 2022 
CSC 221  A  20129  Data Structures Instructor A study of abstract data types, including lists, stacks, queues, and search tables, and their supporting data structures, including arrays, linked lists, binary search trees, and hash tables. Implications of the choice of data structure on the efficiency of the implementation of an algorithm. Efficient methods of sorting and searching. Counts towards the Mathematics major and minor. Prerequisites 
1  M W F 0110  0200pm  WATSON132  Hammurabi Mendes  PRQ MQRQ 
24  0  24  Spring 2022 
CSC 221  B  20130  Data Structures Instructor A study of abstract data types, including lists, stacks, queues, and search tables, and their supporting data structures, including arrays, linked lists, binary search trees, and hash tables. Implications of the choice of data structure on the efficiency of the implementation of an algorithm. Efficient methods of sorting and searching. Counts towards the Mathematics major and minor. Prerequisites 
1  M W F 0220  0310pm  WATSON132  Hammurabi Mendes  Footnote XP, XP, PRQ MQRQ 
24  0  24  Spring 2022 
CSC 362  0  20136  Data Visualization Instructor An introduction to the theory and application of graphical representations of data. Topics include: the human visual system, lowlevel vision processing, attentive vs. preattentive processes, color vision and color map design, interaction, space perception, and visualization design. Counts as an elective in the Data Science interdisciplinary minor. Prerequisites Does not carry Mathematics major credit. 
1  M W 0730  0845pm  WATSON132  Tabitha Peck  PRQ,LL, PRQ, Footnote 02  24  0  24  Spring 2022 
CSC 371  0  20137  Machine Learning Instructor A survey of the field of machine learning, with an introduction to the fundamental algorithms in the field and the theory underpinning them. Topics include techniques for regression, classification, ensemble methods, and dimensionality reduction. Counts towards the Mathematics major and minor. Prerequisites Offered Spring of evennumbered years. 
1  T R 0245  0400pm  CHAMLRC  Michelle Kuchera  XLST, PRQ  24  0  24  Spring 2022 
DIG 120  0  20147  Programming in the Humanities Instructor Computational methods have significantly broadened and deepened the possibilities of inquiry in the Humanities. Programming skills have allowed textual scholars, in particular, to take advantage of enormous digitized corpora of historical documents, newspapers, novels, books, and social network data like Twitter feeds to pose new questions to the written word. We can now trace the changing semantics of words and phrases across millions of documents and hundreds of years, visualize centuriesold plot structures in new ways through sentiment analysis and character networks, and solve longstanding riddles of authorship attributionamong many other exciting feats. This course offers an introduction to computer science through applications in the Humanities. Students will learn to program in the Wolfram Language, aka Mathematica. The Wolfram Language is especially well suited for humanists: its rich documentation and natural language processing capabilities ensure a gentle introduction for firsttime programmers, its symbolic computation structure allows us to work with texts written in any language and any alphabet, while its Notebook environment provides an interactive medium for publishing and sharing our results with peers. Mathematica also provides a great springboard for further work in computer science, physical computing, and Digital Studies more broadly. Satisfies a minor requirement in Computer Science. Prerequisites (Spring)

1  T R 0245  0400pm  LIBB110  Jakub Kabala  Footnote 05, Footnote 06, XLST, PRQ MQRQ 
12  0  12  Spring 2022 
ECO 105  A  20156  Stats & Basic Econometrics Instructor Application of probability and statistics to economic analysis. Topics include: probability rules, discrete and continuous random variables, confidence intervals, hypothesis tests, correlation, and regression. Spreadsheet software is utilized. An economics research paper is a major component of the course. One laboratory session per week may be required (see specific section times for details). Satisfies the Mathematical and Quantitative Thought requirement. Prerequisites

1  T R 0950  1105am  CHAM1046  Caleb Stroup  R, Footnote 04, PRQ MQRQ 
24  0  24  Spring 2022 
ECO 105  B  20157  Stats & Basic Econometrics Instructor Application of probability and statistics to economic analysis. Topics include: probability rules, discrete and continuous random variables, confidence intervals, hypothesis tests, correlation, and regression. Spreadsheet software is utilized. An economics research paper is a major component of the course. One laboratory session per week may be required (see specific section times for details). Satisfies the Mathematical and Quantitative Thought requirement. Prerequisites

1  T R 1135  1250pm  CHAM2164  Caleb Stroup  34+,R, Footnote 04, PRQ MQRQ 
24  0  24  Spring 2022 
ECO 205  A  20162  Econometrics Instructor Applications of linear regression analysis to economic analysis. Topics include model specification, parameter estimation, inference, and problems relating to data issues, statistical concerns, and model diagnostics. Statistical software is utilized. An economics research paper is a major component of the course. Counts as an elective in the Data Science interdisciplinary minor. Prerequisites 
1  T R 0815  0930am  CHAM2164  Mark Foley  34, Footnote 18, Footnote 23, PRQ MQRQ 
26  0  26  Spring 2022 
ECO 205  B  20163  Econometrics Instructor Applications of linear regression analysis to economic analysis. Topics include model specification, parameter estimation, inference, and problems relating to data issues, statistical concerns, and model diagnostics. Statistical software is utilized. An economics research paper is a major component of the course. Counts as an elective in the Data Science interdisciplinary minor. Prerequisites 
1  T R 0950  1105am  CHAM2164  Mark Foley  Footnote 12, PRQ MQRQ 
26  0  26  Spring 2022 
EDU 291  0  20181  Data in Education Instructor Educational data and quantitative data analyses have come to play a powerful role in the way we govern our schools. In this course, students will learn to be critical consumers and skilled producers of such analyses. In the applied portion of this class, students will learn data management, analysis, and visualization strategies by working with real data gathered in educational settings to answer research questions of policy and practical interest.
Satisfies a requirement in the Educational Studies minor. Prerequisites 
0  T R 0950  1105am  LIBB110  Brittany Murray  12, Footnote 17, PREQ, Footnote 22 MQRQ 
30  0  30  Spring 2022 
HIS 207  0  20264  Digital Medieval History Instructor An introduction to reading, writing and research in history with the help of digital methods. Students will study the primary sources and historiography of Medieval Europe (5001500 C.E.) using digital methods of text mining, map making, sentiment analysis, network analysis and/or topic modeling. No prior experience expected. Satisfies an Historical Thought requirement. Prerequisites 
1  T R 1135  1250pm  CHAM3106  Jakub Kabala  34, Footnote 17, Footnote 22, 123+ HTRQ 
20  0  20  Spring 2022 
MAT 210  0  20309  Mathematical Modeling Instructor A survey of discrete mathematical and computational modeling techniques and their application to the natural and social sciences. Mathematical tools are selected from such topics as Monte Carlo simulation, queuing theory, Markov chains, optimization, discrete dynamical systems, computational geometry, agentbased modeling, and cellular automata. Emphasis is on formulating models, investigating them analytically and computationally, and communicating the results. Counts as an elective in the Mathematics major. Prerequisites 
1  M W F 0110  0200pm  WALLB05  Tim Chartier  ATII, XLST,PRQ, PRM MQRQ 
24  0  24  Spring 2022 
MAT 341  0  20316  Mathematical Statistics Instructor A mathematical approach to statistical theory. Includes a study of distribution theory, important properties of estimators, interval estimation and hypothesis testing, regression and correlation, and selected topics from nonparametric statistics. Satisfies a requirement in the Data Science interdisciplinary minor. Prerequisites

1  T R 0815  0930am  CHAM1046  Carl Yerger  PRM, Footnote 17, PREQ, XLST, Footnote 22, PRQ  30  0  30  Spring 2022 
PHY 200  0  20352  Computational Physics Instructor (Crosslisted as CSC 200) This course is an introduction to computer programming and computational physics using Python. No prior programming experience is necessary. This course will provide studetns with the skills required to write code to solve physics problems in areas including quantum physics, electromagnetism, and mechanics. Structured programming methods will be covered as well as algorithms for numerical integration, solving differential equations, and more. Satisfies a requirement in the Data Science interdisciplinary minor. Prerequisites 
1  T R 0245  0400pm  WALL380  Michelle Kuchera  PREQ, XLST, Footnote 22, PRQ MQRQ 
20  0  20  Spring 2022 
POL 182  A  20361  Intro to Research Methods Instructors The framework of social science analysis, and the use of statistics for studying political problems. Topics range from research design and hypothesis testing to correlation and multiple regression. Satisfies the Mathematical and Quantitative Thought requirement. Prerequisites 
0  W 0220  0335pm  WALLB05  Melody CrowderMeyer  ATII, XP, 12+, Footnote 23 MQRQ 
30  0  30  Spring 2022 
POL 182  A  20361  Intro to Research Methods Instructors The framework of social science analysis, and the use of statistics for studying political problems. Topics range from research design and hypothesis testing to correlation and multiple regression. Satisfies the Mathematical and Quantitative Thought requirement. Prerequisites 
1  M W F 1050  1140am  WALLB05  Melody CrowderMeyer  ATII, XP, 12+, Footnote 23 MQRQ 
30  0  30  Spring 2022 
POL 182  B  20362  Intro to Research Methods Instructors The framework of social science analysis, and the use of statistics for studying political problems. Topics range from research design and hypothesis testing to correlation and multiple regression. Satisfies the Mathematical and Quantitative Thought requirement. Prerequisites 
1  M W F 1200  1250pm  WALLB05  Melody CrowderMeyer  AT, XP, 12+, Footnote 23 MQRQ 
30  0  30  Spring 2022 
POL 182  B  20362  Intro to Research Methods Instructors The framework of social science analysis, and the use of statistics for studying political problems. Topics range from research design and hypothesis testing to correlation and multiple regression. Satisfies the Mathematical and Quantitative Thought requirement. Prerequisites 
0  R 0245  0400pm  WALLB05  Melody CrowderMeyer  AT, XP, 12+, Footnote 23 MQRQ 
30  0  30  Spring 2022 
SOC 392  0  20466  Quantitative Data Analysis Instructor The purpose of this class is to prepare you as a future producer and evaluator of highquality quantitative research  whether as a social scientist, as a decisionmaker in a corporate setting, or as a designer and evaluator of social policy. Extending theoretical concepts from introductory Social Statistics coursework, this class provides students with handson quantitative analysis experience using existing quantitative research. We survey, and learn to replicate and evaluate, various types of regressions, structural equation models, and longitudinal analyses. Additionally, students learn to critically engage with and evaluate social network analyses, geospatial analyses and mixed method research methodologies. Students will complete a capstone project that builds on their existing research, ending the semester with a manuscript able to be presented at a formal conference. Counts as an elective in the Data Science interdisciplinary minor. Prerequisites 
1  M W 0220  0335pm  CHAM2146  Joseph Marchia  Footnote 18, Footnote 23, PRQ MQRQ 
20  0  20  Spring 2022 