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

ANT 377  0  50018  Imaging the Earth Instructor The use of geographical information systems (GIS) to analyze, model, and present spatial relationships in the biological and social sciences, supplemented by other packages such as Google Earth. Field collection of spatial data with GPS units. Course is computerbased and emphasizes individual research projects. Satisfies the Methods requirement for the major and minor in Anthropology. Prerequisites 
1  TBA  Maxime LamoureuxStHilaire  O  11  11  0  Fall 2020  
BIO 240  0  50071  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 Fall 2020 
1  T R 0110  0225pm  Susana Wadgymar  PRQ,O MQRQ 
20  20  0  Fall 2020  
CSC 110  0  50135  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 1135  1250pm  Laurie Heyer  O MQRQ, JEC 
24  23  1  Fall 2020  
CSC 121  A  50136  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  TBA  Tabitha Peck  O MQRQ 
24  27  3  Fall 2020  
CSC 121  B  50137  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  TBA  Raghu Ramanujan  O MQRQ 
24  30  6  Fall 2020  
CSC 121  C  50138  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 1135  1250pm  WATSON109  Carlos Seminario  H MQRQ 
24  23  1  Fall 2020 
CSC 221  A  50141  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  T R 0815  0930am  Sakib Miazi  PRQ,O MQRQ 
24  18  6  Fall 2020  
CSC 221  B  50142  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  T R 0950  1105am  Sakib Miazi  PRQ,O MQRQ 
24  22  2  Fall 2020  
DIG 270  0  50155  Digital Maps, Space & Place Instructor A course in the theories and practices of digital mapping as applied to the humanities and social sciences. The course brings together readings in the digital spatial humanities as well as handson mapping and spatial analysis through programming in the Wolfram Language (Mathematica). Students will learn how to choose geographical projections; work with points, lines and polygons; find, extract and analyze spatial data from humanistic materials; and tell stories (and lies) with maps. They will also read, think and write about real and imagined geographies, the meaning of place and memory, as well as the creation of space. The course will conclude with independent student projects on topics of their choosing. No background experience required. Satisfies a requirement in the Digital Studies interdisciplinary minor. Prerequisites 
1  M W 0730  0845pm  Jakub Kabala  O SSRQ 
25  26  1  Fall 2020  
ECO 105  A  50161  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 1200  1250pm  Caleb Stroup  PRQ,H MQRQ 
13  13  0  Fall 2020  
ECO 105  A  50161  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 0245  0400pm  LIBB110  Caleb Stroup  PRQ,H MQRQ 
13  13  0  Fall 2020 
ECO 105  B  50162  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 1200  1250pm  Caleb Stroup  PRQ,H MQRQ 
13  14  1  Fall 2020  
ECO 105  B  50162  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 0245  0400pm  LIBB110  Caleb Stroup  PRQ,H MQRQ 
13  14  1  Fall 2020 
ECO 105  C  50163  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  LIBB110  Caleb Stroup  PRQ,H MQRQ 
13  13  0  Fall 2020 
ECO 105  C  50163  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 0110  0200pm  Caleb Stroup  PRQ,H MQRQ 
13  13  0  Fall 2020  
ECO 105  D  50164  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 0110  0200pm  Caleb Stroup  PRQ,H MQRQ 
13  13  0  Fall 2020  
ECO 105  D  50164  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  LIBB110  Caleb Stroup  PRQ,H MQRQ 
13  13  0  Fall 2020 
ECO 205  D  50172  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 0815  0930am  Mark Foley  PRQ,O MQRQ 
16  20  4  Fall 2020  
ECO 205  D  50172  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  Mark Foley  PRQ,O MQRQ 
16  20  4  Fall 2020  
ECO 205  A  50169  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 0245  0400pm  Mark Foley  PRQ,4+,O MQRQ 
13  13  0  Fall 2020  
ECO 205  A  50169  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  Mark Foley  PRQ,4+,O MQRQ 
13  13  0  Fall 2020  
ECO 205  B  50170  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  Mark Foley  PRQ,4+,O MQRQ 
13  13  0  Fall 2020  
ECO 205  B  50170  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 0245  0400pm  Mark Foley  PRQ,4+,O MQRQ 
13  13  0  Fall 2020  
ECO 205  C  50171  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 0815  0930am  Mark Foley  PRQ,4+,O MQRQ 
10  10  0  Fall 2020  
ECO 205  C  50171  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  Mark Foley  PRQ,4+,O MQRQ 
10  10  0  Fall 2020  
ECO 316  0  50179  Computational Economics Instructor Computational methods for building and solving models in the context of economics topics. Methods discussed include agentbased simulations to analyze complex adaptive systems, value function iteration to solve dynamic structural models, and miscellaneous estimation and optimizing techniques. Satisfies an interdisciplinary minor requirement for applied mathematics. Prerequisites 
1  M W F 1050  1140am  CHAMLRC  Shyam Gouri Suresh  PRQ,O SSRQ 
16  15  1  Fall 2020 
EDU 291  0  50188  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 
1  T R 0110  0225pm  WATSON109  Chris Marsicano  H MQRQ 
30  27  3  Fall 2020 
POL 182  0  50376  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  Besir Ceka  234,O,HL MQRQ 
30  35  5  Fall 2020  
POL 182  0  50376  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  Besir Ceka  234,O,HL MQRQ 
30  35  5  Fall 2020 
SOC 201  0  50460  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 
0  T R 0815  0930am  TOMCONF  Aarushi Bhandari  H MQRQ 
20  18  2  Fall 2020 