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

BIO 240  C  20025  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 0815  0930am  WALLB05  Chris Thawley  PRQ MQRQ 
20  20  0  Spring 2020 
BIO 240  A  20026  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 0940  1055am  WALLB05  Chris Thawley  PRQ MQRQ 
20  20  0  Spring 2020 
BIO 240  B  20027  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 1215  0130pm  WALL380  Chris Thawley  PRQ MQRQ 
20  20  0  Spring 2020 
BIO 309  0  20036  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 1130  1220pm  CHAM2198  Debbie ThurtleSchmidt  BR, PRQ  32  21  11  Spring 2020 
BIO 343  0  20269  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  M W 0230  0345pm  WALL380  Debbie ThurtleSchmidt  1,AT,R,XP, PRQ  18  17  1  Spring 2020 
CSC 110  0  20032  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  M W F 1130  1220pm  CHAMLRC  Che Smith  PRQ MQRQ, JEC 
24  24  0  Spring 2020 
CSC 121  C  20001  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 0940  1055am  LIBB110  Jonad Pulaj  XLST MQRQ 
24  24  0  Spring 2020 
CSC 121  A  20007  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 1130  1220pm  WATSON132  Hammurabi Mendes  Footnote 22 MQRQ 
24  26  2  Spring 2020 
CSC 121  B  20008  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 1030  1120am  WALLB05  Michelle Kuchera  PREQ, Footnote 17, Footnote 22 MQRQ 
24  24  0  Spring 2020 
CSC 221  B  20003  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 1030  1120am  WATSON132  Tabitha Peck  XLST, PRQ MQRQ 
24  24  0  Spring 2020 
CSC 221  A  20009  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 0930  1020am  WATSON132  Tabitha Peck  PRQ MQRQ 
24  25  1  Spring 2020 
CSC 362  0  20011  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  T R 0940  1055am  WATSON132  Tabitha Peck  PRQ  24  26  2  Spring 2020 
ECO 105  B  20049  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. Satisfies the Mathematical and Quantitative Thought requirement. Prerequisites

1  M W F 1030  1120am  CHAM2187  Caleb Stroup  B2, PRQ, Footnote 02 MQRQ 
13  14  1  Spring 2020 
ECO 105  B  20049  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. Satisfies the Mathematical and Quantitative Thought requirement. Prerequisites

0  R 0815  0930am  CHAM3146  Caleb Stroup  B2, PRQ, Footnote 02 MQRQ 
13  14  1  Spring 2020 
ECO 105  A  20050  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. Satisfies the Mathematical and Quantitative Thought requirement. Prerequisites

0  T 0815  0930am  CHAM3146  Caleb Stroup  B2, Footnote 01,18, Footnote 23, PRQ MQRQ 
13  13  0  Spring 2020 
ECO 105  A  20050  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. Satisfies the Mathematical and Quantitative Thought requirement. Prerequisites

1  M W F 1030  1120am  CHAM2187  Caleb Stroup  B2, Footnote 01,18, Footnote 23, PRQ MQRQ 
13  13  0  Spring 2020 
ECO 105  C  20051  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. Satisfies the Mathematical and Quantitative Thought requirement. Prerequisites

1  M W F 1230  0120pm  CHAM2198  Caleb Stroup  B2, PRQ MQRQ 
13  13  0  Spring 2020 
ECO 105  C  20051  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. Satisfies the Mathematical and Quantitative Thought requirement. Prerequisites

0  T 0305  0420pm  CHAM3146  Caleb Stroup  B2, PRQ MQRQ 
13  13  0  Spring 2020 
ECO 105  D  20052  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. Satisfies the Mathematical and Quantitative Thought requirement. Prerequisites

0  R 0305  0420pm  CHAM3146  Caleb Stroup  PREQ, Footnote 01, PRQ MQRQ 
13  13  0  Spring 2020 
ECO 105  D  20052  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. Satisfies the Mathematical and Quantitative Thought requirement. Prerequisites

1  M W F 1230  0120pm  CHAM2198  Caleb Stroup  PREQ, Footnote 01, PRQ MQRQ 
13  13  0  Spring 2020 
ECO 205  C  20606  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  CHAM2234  Mark Foley  Footnote 01,11,18, Footnote 01,12,23, PRQ MQRQ 
13  12  1  Spring 2020 
ECO 205  C  20606  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 
0  T 0140  0255pm  CHAM3146  Mark Foley  Footnote 01,11,18, Footnote 01,12,23, PRQ MQRQ 
13  12  1  Spring 2020 
ECO 205  D  20607  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  CHAM2234  Mark Foley  Footnote 01,12,23, PRQ MQRQ 
13  8  5  Spring 2020 
ECO 205  D  20607  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 
0  R 0140  0255pm  CHAM3146  Mark Foley  Footnote 01,12,23, PRQ MQRQ 
13  8  5  Spring 2020 
ECO 205  B  20057  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 
0  R 0305  0420pm  CHAM3130  Mark Foley  PREQ, Footnote 01, PRQ MQRQ 
13  13  0  Spring 2020 
ECO 205  B  20057  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 0140  0255pm  CHAM2068  Mark Foley  PREQ, Footnote 01, PRQ MQRQ 
13  13  0  Spring 2020 
ECO 205  A  20066  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 0140  0255pm  CHAM2068  Mark Foley  Footnote 17, Footnote 22, PRQ MQRQ 
13  13  0  Spring 2020 
ECO 205  A  20066  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 
0  T 0305  0420pm  CHAMLRC  Mark Foley  Footnote 17, Footnote 22, PRQ MQRQ 
13  13  0  Spring 2020 
MAT 105  0  20613  Introduction to Statistics Instructor An introduction to statistics as a science of understanding and analyzing data. Students will learn how to effectively make use of data in the face of uncertainty: how to collect data, how to analyze data, and how to use data to make inferences and conclusions about real world phenomena. Students will practice their analytical and statistical skills while exploring topics related to social justice, equality, and community. Satisfies Data Science minor requirement Prerequisites 
1  T R 0140  0255pm  WATSON132  Che Smith  FEE,PERM, Footnote 18, Footnote 23 MQRQ, JEC 
26  24  2  Spring 2020 
MAT 210  0  20154  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 1230  0120pm  WALL380  Tim Chartier  PRQ,XLST, PRQ MQRQ 
24  30  6  Spring 2020 
PHY 200  0  20274  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 0815  0930am  WATSON132  Michelle Kuchera  PERM,R, PERM, Footnote 01,23, Footnote 01,18, PRM, PRM,R, XLST, Footnote 01,PERM MQRQ 
20  18  2  Spring 2020 
POL 182  0  20235  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 0930  1020am  WALLB05  Andrew O'Geen  Footnote 10, Footnote 21, 234,XP, Footnote 28, Footnote 11 MQRQ 
30  31  1  Spring 2020 
POL 182  0  20235  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  T 0305  0420pm  LIBB110  Andrew O'Geen  Footnote 10, Footnote 21, 234,XP, Footnote 28, Footnote 11 MQRQ 
30  31  1  Spring 2020 
PSY 367  0  20549  Psychological Modeling Instructor In this seminar we will explore modeling techniques used in psychological science. Example analytic techniques that may be covered include: linear and nonlinear models, agentbased modeling, network analysis, natural language processing, machine learning, systems modeling, dynamical/complex systems, or other computational/representative models. We will focus broadly on psychological science, meaning models will be applied to diverse areas (e.g., clinical, personality, social, health, I/O, behavioral neuroscience) but may have arisen in other fields (e.g., economics, mathematics, physics, computer science). Major assignments will include written papers, mathematical modeling, and a group based digital learning project. This course will use a variety of coding environments (e.g., NetLogo, R) so a willingness to learn how to program is expected but experience with coding is not required. Satisfies Psychology major requirement. Prerequisites 
1  T R 0940  1055am  LIBSTUDIOD  Brian Eiler  PRQ,PRM, PERM, Footnote 23 MQRQ 
11  13  2  Spring 2020 
SOC 201  0  20105  Social Statistics Instructor Sociologists and other social scientists must describe and interpret social facts in order to make sense of the world around them. To do this, they often rely on the analysis of quantitative data using statistical methods. This course acts as a primer to sociological statistical analysis and students will learn to find and access social data, summarize patterns in that data, represent these patterns graphically, and explore relationships between different variables. Topics include descriptive measures, hypothesis testing, analysis of variance, chisquare, correlation, and regression. This course is designed as a gateway to quantitative sociological research, and emphasis is on practice and implementation, with students also learning to use SPSS software. Satisfies a major or interdisciplinary minor requirement in Communication Studies. Prerequisites 
1  T R 0815  0930am  CHAM2198  Gayle Kaufman  AT, Footnote 23 MQRQ 
20  20  0  Spring 2020 
SOC 391  0  20559  Survey Research Methods Instructor This course introduces students to survey research methods. Sociology is based on empirical data. Sociologists are trained to collect data in order to answer questions. One of the most commonly used forms of data collection within sociology is the survey. In this course, students will gain experience in designing a survey, sampling, administering a survey, and analyzing survey data. Counts as an elective in the Data Science interdisciplinary minor.
Prerequisites 
0  T R 0815  0930am  CHAMLRC  Alessandra Bazo Vienrich  Footnote 01,23, Footnote 01,18  20  17  3  Spring 2020 