Courses

This page displays the schedule of Bryn Mawr courses in this department for this academic year. It also displays descriptions of courses offered by the department during the last four academic years.

For information about courses offered by other Bryn Mawr departments and programs or about courses offered by Haverford and Swarthmore Colleges, please consult the Course Guides page.

For information about the Academic Calendar, including the dates of first and second quarter courses, please visit the College's calendars page.

Students must choose a major subject and may choose a minor subject. Students may also select from one of seven concentrations, which are offered to enhance a student's work in the major or minor and to focus work on a specific area of interest.

Concentrations are an intentional cluster of courses already offered by various academic departments or through general programs. These courses may also be cross-listed in several academic departments. Therefore, when registering for a course that counts toward a concentration, a student should register for the course listed in her major or minor department. If the concentration course is not listed in her major or minor department, the student may enroll in any listing of that course.

Fall 2022 DSCI

Course Title Schedule/Units Meeting Type Times/Days Location / Instruction Mode Instr(s)
DSCI B100-001 Introduction to Data Science 1Semester / 1 LEC: 2:25 PM- 3:45 PM TTH Dalton Hall 300
In Person
Thapar,A.
DSCI B201-001 Ethics in Data Sciences 1Semester / 1 LEC: 11:40 AM- 1:00 PM MW Park 264
In Person
Hausmann-Stabile,C.
DSCI B314-001 Advanced Data Science:Regression & Multivariate Statistics 1Semester / 1 LEC: 9:10 AM-12:00 PM TH Bettws Y Coed 127
In Person
Schulz,M.
BIOL B215-001 Biostatistics with R 1Semester / 1 Lecture: 9:55 AM-11:15 AM TTH Park 229
In Person
Bitarello,B., Bitarello,B.
Laboratory: 1:10 PM- 4:00 PM W Park 25
In Person
CITY B201-001 Introduction to GIS for Social and Environmental Analysis 1Semester / 1 Lecture: 9:55 AM-11:15 AM TTH Canaday Computer Lab
In Person
Hurley,J.
Breakout Disussion: 2:15 PM- 2:45 PM TTH Canaday Computer Lab
In Person
CITY B201-002 Introduction to GIS for Social and Environmental Analysis 1Semester / 1 Lecture: 12:55 PM- 2:15 PM TTH Canaday Computer Lab
In Person
Hurley,J.
CMSC B109-001 Introduction to Computing 1Semester / 1 Lecture: 1:10 PM- 2:30 PM MW Park 245
In Person
Kumar,D.
CMSC B109-00A Introduction to Computing 1Semester / 1 Laboratory: 2:40 PM- 4:00 PM M Park 231
In Person
Kumar,D.
CMSC B113-001 Computer Science I 1Semester / 1 Lecture: 1:10 PM- 2:30 PM MW Park 338
In Person
Normoyle,A.
CMSC B113-002 Computer Science I 1Semester / 1 Lecture: 12:55 PM- 2:15 PM TTH Park 300
In Person
Poliak,A.
CMSC B113-00A Computer Science I 1Semester / 1 Laboratory: 2:25 PM- 3:15 PM T Park 230
In Person
Poliak,A.
CMSC B113-00B Computer Science I 1Semester / 1 Laboratory: 11:55 AM-12:45 PM TH Park 230
In Person
Poliak,A.
CMSC B113-00C Computer Science I 1Semester / 1 Laboratory: 2:40 PM- 4:00 PM W Park 231
In Person
Normoyle,A.
CMSC B151-001 Introduction to Data Structures 1Semester / 1 Lecture: 9:55 AM-11:15 AM TTH Park 337
In Person
Towell,G.
CMSC B151-00A Introduction to Data Structures 1Semester / 1 Laboratory: 11:25 AM-12:45 PM TH Park 231
In Person
Towell,G.
CMSC B283-001 Topics in Computer Science 1Semester / 1 Lecture: 12:55 PM- 2:15 PM TTH Park 245
In Person
Murphy,C.
CMSC B283-00A Topics in Computer Science 1Semester / 1 Laboratory: 2:25 PM- 3:45 PM TH Park 230
In Person
Murphy,C.
ECON B253-001 Introduction to Econometrics 1Semester / 1 Lecture: 1:10 PM- 2:30 PM MW Carpenter Library 21
In Person
Anti,S.
GEOL B210-001 Cataloging Collections: Minerals, Museums/Wstrn Colnsm 1Semester / 1 LEC: 1:10 PM- 4:00 PM TH PK 373
In Person
Robbins,C., Weldon,M.
GNST B425-001 Praxis III - Independent Study 1Semester / 1 Dept. staff, TBA
HLTH B302-001 Survey Methods for Health Research 1Semester / 1 LEC: 1:10 PM- 4:00 PM W Park 252
In Person
Olson,H.
MATH B195-001 Select Topics in Mathematics: Intro to Math & Sustainability 1Semester / 1 LEC: 2:25 PM- 3:45 PM TTH Park 336
In Person
Donnay,V.
PSYC B205-001 Research Methods and Statistics 1Semester / 1 Lecture: 11:25 AM-12:45 PM TTH Park 338
In Person
Hazan,B., Peterson,L.
PSYC B205-00A Research Methods and Statistics 1Semester / 1 Laboratory: 10:10 AM-11:30 AM F Canaday Computer Lab
In Person
Peterson,L., Tian,J.
PSYC B205-00B Research Methods and Statistics 1Semester / 1 Laboratory: 1:10 PM- 2:30 PM F Canaday Computer Lab
In Person
Peterson,L., Tian,J.
PSYC B205-00Z Research Methods and Statistics 1Semester / 1 In Person Peterson,L., Tian,J.
SOCL B265-001 Quantitative Methods 1Semester / 1 Lecture: 11:40 AM- 1:00 PM MW Dalton Hall 119
In Person
Wright,N.
SOCL B327-001 Capital & Connections:A Network Approach to Social Structure 1Semester / 1 Lecture: 10:10 AM-11:30 AM MW Dalton Hall 300
In Person
Cox,A.

Spring 2023 DSCI

Course Title Schedule/Units Meeting Type Times/Days Location / Instruction Mode Instr(s)
CITY B217-001 Topics in Research Methods: Qualitative Methods 1Semester / 1 LEC: 1:10 PM- 2:30 PM MW In Person Restrepo,L.
CMSC B113-001 Computer Science I 1Semester / 1 Lecture: 12:55 PM- 2:15 PM TTH Park 338
In Person
Poliak,A.
CMSC B113-00A Computer Science I 1Semester / 1 Laboratory: 2:25 PM- 3:15 PM T Park 230
In Person
Poliak,A.
CMSC B113-00B Computer Science I 1Semester / 1 Laboratory: 11:55 AM-12:45 PM TH Park 230
In Person
Poliak,A.
CMSC B113-00Z Computer Science I 1Semester / 1 In Person Poliak,A.
CMSC B151-001 Introduction to Data Structures 1Semester / 1 Lecture: 12:55 PM- 2:15 PM TTH Park 245
In Person
Towell,G., Xu,D.
Laboratory: 2:25 PM- 3:45 PM TH In Person
CMSC B383-001 Recent Advances in Computer Science: Applications of Natural Language Processing 1Semester / 1 Lecture: 10:10 AM-11:30 AM MW Park 338
In Person
Poliak,A., Poliak,A.
Lecture: 11:40 AM- 1:00 PM W In Person
ECON B304-001 Econometrics 1Semester / 1 Lecture: 9:55 AM-11:15 AM TTH Dalton Hall 119
In Person
Kim,J.
GEOL B104-001 The Science of Climate Change 1Semester / 1 In Person Hearth,S.
PSYC B205-001 Research Methods and Statistics 1Semester / 1 Lecture: 11:25 AM-12:45 PM TTH Park 338
In Person
Thapar,A.
PSYC B205-00A Research Methods and Statistics 1Semester / 1 Laboratory: 10:10 AM-11:30 AM F Canaday Computer Lab
In Person
Thapar,A.
PSYC B205-00B Research Methods and Statistics 1Semester / 1 Laboratory: 1:10 PM- 2:30 PM F Canaday Computer Lab
In Person
Thapar,A.
PSYC B205-00Z Research Methods and Statistics 1Semester / 1 In Person
PSYC B318-001 Data Science with R 0.5First Half / 0.5 LEC: 1:10 PM- 4:00 PM F In Person Smedley,E.

Fall 2023 DSCI

(Class schedules for this semester will be posted at a later date.)

New Data Science Courses for 2022-2023

MUSC H255: Encoding Music 

HLTH B302: Survey Methods for Health Research

2022-23 Catalog Data: DSCI

DSCI B100 Introduction to Data Science

Fall 2022

"Data science" is a catch-all term used to describe the practice of working with and analyzing messy data sources to draw meaningful conclusions. This course provides a broad introduction to the field of data science via the statistical programming language, R. Over the semester, students will learn how to manipulate, manage, summarize and visualize large data sets. No previous exposure to programming or statistics is expected.

Course does not meet an Approach

Quantitative Readiness Required (QR)

Counts Toward Data Science

Counts Toward Neuroscience

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DSCI B201 Ethics in Data Sciences

Fall 2022

This course offers a critical perspective of diverse ethical challenges associated with data sciences. The goal of this course is to prepare the students to become ethical data citizens. For that, we integrate ethical perspectives with a discussion of the impact of data on the human condition and experience. This course responds to the vacuum of ethics guidelines in data sciences. As a field of inquiry and practice, data sciences do not have an ethical code despite that data and algorithmic processes and computational techniques impact everyday life. Our thinking is grounded in that the development and use of data technologies carries consequences for citizens, organizations, governments, the environment, and society at large. Because of the vacuum in critical ethical thinking in data sciences, the consequences of data technologies sometimes carry unintended implications. This class allows students to apply ethical lenses to examine data sciences developments and applications to understand the ethical issues emerging from these, as well as how to mitigate them. The course is not required for DS minors.

Course does not meet an Approach

Counts Toward Data Science

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DSCI B210 Quantifying Happiness: Efforts to study and alter happiness

Not offered 2022-23

This course is designed to introduce students to the scientific study of happiness and psychological well-being. We begin with readings that will allow us to critically consider what is meant by happiness and well-being and then move on to evaluating approaches to measuring these constructs. We will examine studies that have tracked happiness and attempted to identify contributors to happiness. We will also look at efforts to increase happiness. We will ponder the ways in which culture and historical factors influence the study of happiness. Students will work directly with data sets measuring aspects of happiness. Part of the class meeting time will be used to support study work with data. Prerequisite: Intro to Data Science or a statistics class; coursework in the social sciences recommended but not required; Quantitative Readiness Required (QR)

Course does not meet an Approach

Counts Toward Data Science

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DSCI B314 Advanced Data Science:Regression & Multivariate Statistics

Fall 2022

This course is designed to improve your data science skills by introducing you to advanced statistical techniques that have become increasingly important in psychology and a variety of fields. The focus will be on understanding the advantages and limitations of regression approaches and multivariate analytic techniques that permit simultaneous prediction of multiple outcomes. Topics covered will include basic regression approaches, advanced regression strategies, structural equation modeling, factor analysis, measurement models, path modeling, modeling of longitudinal data sets, multilevel modeling approaches and growth curve modeling. Students will gain familiarity with these techniques by working with actual data sets. The last part of each class will be reserved for lab time to apply lessons from class to an assignment due the following week. Students are welcome to stay beyond the noon ending time to complete the assignment. Prerequisites: Required: PSYC Research Methods and Statistics 205 (BMC), Psych 200 (HC) Experimental Methods and Statistics, or BIOL B215 Experimental Design and Statistics. Students with good statistical preparation in math or other disciplines and some knowledge of core methods used in social science or health-related research should consult with the instructor to gain permission to take the class.This course was formerly numbered PSYC B314; students who previously completed PSYC B314 may not repeat this course.

Counts Toward Data Science

Counts Toward Health Studies

Back to top

DSCI B100 Introduction to Data Science

Fall 2022

"Data science" is a catch-all term used to describe the practice of working with and analyzing messy data sources to draw meaningful conclusions. This course provides a broad introduction to the field of data science via the statistical programming language, R. Over the semester, students will learn how to manipulate, manage, summarize and visualize large data sets. No previous exposure to programming or statistics is expected.

Course does not meet an Approach

Quantitative Readiness Required (QR)

Counts Toward Counts toward Data Science

Counts Toward Counts toward Neuroscience

Back to top

DSCI B314 Advanced Data Science:Regression & Multivariate Statistics

Fall 2022

This course is designed to improve your data science skills by introducing you to advanced statistical techniques that have become increasingly important in psychology and a variety of fields. The focus will be on understanding the advantages and limitations of regression approaches and multivariate analytic techniques that permit simultaneous prediction of multiple outcomes. Topics covered will include basic regression approaches, advanced regression strategies, structural equation modeling, factor analysis, measurement models, path modeling, modeling of longitudinal data sets, multilevel modeling approaches and growth curve modeling. Students will gain familiarity with these techniques by working with actual data sets. The last part of each class will be reserved for lab time to apply lessons from class to an assignment due the following week. Students are welcome to stay beyond the noon ending time to complete the assignment. Prerequisites: Required: PSYC Research Methods and Statistics 205 (BMC), Psych 200 (HC) Experimental Methods and Statistics, or BIOL B215 Experimental Design and Statistics. Students with good statistical preparation in math or other disciplines and some knowledge of core methods used in social science or health-related research should consult with the instructor to gain permission to take the class.This course was formerly numbered PSYC B314; students who previously completed PSYC B314 may not repeat this course.

Counts Toward Counts toward Data Science

Counts Toward Counts toward Health Studies

Back to top

CITY B201 Introduction to GIS for Social and Environmental Analysis

Fall 2022

This course is designed to introduce the foundations of GIS with emphasis on applications for social and environmental analysis. It deals with basic principles of GIS and its use in spatial analysis and information management. Ultimately, students will design and carry out research projects on topics of their own choosing. Prerequisite: At least sophomore standing and Quantitative Readiness are required (i.e.the quantitative readiness assessment or Quan B001).

Quantitative Readiness Required (QR)

Counts Toward Counts toward Data Science

Counts Toward Counts toward Environmental Studies

Back to top

CMSC B109 Introduction to Computing

Fall 2022

The course is an introduction to computing: how we can describe and solve problems using a computer. Students will learn how to write algorithms, manipulate data, and design programs to make computers useful tools as well as mediums of creativity. Contemporary, diverse examples of computing in a modern context will be used, with particular focus on graphics and visual media. The Processing/Java programming language will be used in lectures, class examples and weekly programming projects, where students will learn and master fundamental computer programming principles. Students are required to register for the weekly lab. Prerequisites: Must pass either the Quantitative Readiness Assessment or the Quantitative Seminar (QUAN B001).

Quantitative Methods (QM)

Quantitative Readiness Required (QR)

Scientific Investigation (SI)

Counts Toward Counts toward Data Science

Back to top

CMSC B151 Introduction to Data Structures

Fall 2022, Spring 2023

Introduction to the fundamental algorithms and data structures using Java. Topics include: Object-Oriented programming, program design, fundamental data structures and complexity analysis. In particular, searching, sorting, the design and implementation of linked lists, stacks, queues, trees and hash maps and all corresponding complexity analysis. In addition, students will also become familiar with Java's built-in data structures and how to use them, and acquire competency using a debugger. Students must also register for the weekly lab. Prerequisites: CMSC B109 or CMSC B113 or CMSC H105, or permission of instructor.

Quantitative Methods (QM)

Scientific Investigation (SI)

Counts Toward Counts toward Data Science

Back to top

BIOL B215 Biostatistics with R

Fall 2022

An introductory course in designing experiments and analyzing biological data. This course is structured to develop students' understanding of when to apply different quantitative methods, and how to implement those methods using the R statistics environment. Topics include summary statistics, distributions, randomization, replication, parametric and nonparametric tests, and introductory topics in multivariate and Bayesian statistics. The course is geared around weekly problem sets and interactive learning. Suggested Preparation: BIOL B110 or B111 is highly recommended. Students who have taken PSYC B205/H200 or SOCL B265 are not eligible to take this course.

Quantitative Methods (QM)

Quantitative Readiness Required (QR)

Scientific Investigation (SI)

Counts Toward Counts toward Biochemistry and Molecular Biology

Counts Toward Counts toward Data Science

Counts Toward Counts toward Health Studies

Back to top

BIOL B115 Computing Through Biology: An Introduction

Not offered 2022-23

This course is an introduction to biology through computer science, or an introduction to computer science through biology. The course will This course is an introduction to biology through computer science, or an introduction to computer science through biology. The course will examine biological systems through the use of computer science, exploring concepts and solving problems from bioinformatics, evolution, ecology, and molecular biology through the practice of writing and modifying code in the Python programming language. The course will introduce students to the subject matter and branches of computer science as an academic discipline, and the nature, development, coding, testing, documenting and analysis of the efficiency and limitations of algorithms. Three hours of lecture, three hours of lab per week.

Quantitative Methods (QM)

Scientific Investigation (SI)

Counts Toward Data Science

Back to top

BIOL B215 Biostatistics with R

Fall 2022

An introductory course in designing experiments and analyzing biological data. This course is structured to develop students' understanding of when to apply different quantitative methods, and how to implement those methods using the R statistics environment. Topics include summary statistics, distributions, randomization, replication, parametric and nonparametric tests, and introductory topics in multivariate and Bayesian statistics. The course is geared around weekly problem sets and interactive learning. Suggested Preparation: BIOL B110 or B111 is highly recommended. Students who have taken PSYC B205/H200 or SOCL B265 are not eligible to take this course.

Quantitative Methods (QM)

Quantitative Readiness Required (QR)

Scientific Investigation (SI)

Counts Toward Biochemistry and Molecular Biology

Counts Toward Data Science

Counts Toward Health Studies

Back to top

BIOL B330 Ecological Modeling

Not offered 2022-23

The survival of humanity depends upon natural resources and ecosystem services. To make important decisions about environmental problems, society needs to understand ecological systems. However, ecological systems are inherently complex. Statistical models coupled with empirical data and simulations provide a means of exploring the complexity of ecological systems to better inform environmental decisions. This class will introduce students to a variety of ecological models while instilling an appreciation for the types of uncertainties that may shroud models to better understand inferences made from them. The course will be taught as a hands-on integrated lab/lecture where students will be expected to program regularly, primarily in R. Prerequisite: BIOL B215 or BIOL B250.

Quantitative Readiness Required (QR)

Counts Toward Data Science

Back to top

CITY B201 Introduction to GIS for Social and Environmental Analysis

Fall 2022

This course is designed to introduce the foundations of GIS with emphasis on applications for social and environmental analysis. It deals with basic principles of GIS and its use in spatial analysis and information management. Ultimately, students will design and carry out research projects on topics of their own choosing. Prerequisite: At least sophomore standing and Quantitative Readiness are required (i.e.the quantitative readiness assessment or Quan B001).

Quantitative Readiness Required (QR)

Counts Toward Data Science

Counts Toward Environmental Studies

Back to top

CITY B217 Topics in Research Methods

Section 001 (Spring 2023): Qualitative Methods
Section 001 (Fall 2021): Research Mthds/Social Sciences

Spring 2023

This is a topics course. Course content varies.

Current topic description: This course builds competency in both qualitative methods and qualitative research design for those who seek to engage in original research into the administration and planning of cities, as well as the community efforts and social movements to effect these processes from below. Students will gain hands-on experience conducting semi-structured interviews, ethnographic and systematic social observation, and both old-school and new-school techniques for gathering plans, policy statements, news coverage, and social-media data. From project design to data gathering, thematic analysis, and presentation, careful attention will be given to the ethical considerations of engaged urban research.

Quantitative Methods (QM)

Counts Toward Data Science

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ECON B253 Introduction to Econometrics

Fall 2022

An introduction to econometric terminology and reasoning. Topics include descriptive statistics, probability, and statistical inference. Particular emphasis is placed on regression analysis and on the use of data to address economic issues. The required computational techniques are developed as part of the course. Class cannot be taken if you have taken H203 or H204. Prerequisites: ECON B105 and a 200-level elective. ECON H201 does not count as an elective.

Quantitative Methods (QM)

Counts Toward Counts toward Data Science

Back to top

CMSC B109 Introduction to Computing

Fall 2022

The course is an introduction to computing: how we can describe and solve problems using a computer. Students will learn how to write algorithms, manipulate data, and design programs to make computers useful tools as well as mediums of creativity. Contemporary, diverse examples of computing in a modern context will be used, with particular focus on graphics and visual media. The Processing/Java programming language will be used in lectures, class examples and weekly programming projects, where students will learn and master fundamental computer programming principles. Students are required to register for the weekly lab. Prerequisites: Must pass either the Quantitative Readiness Assessment or the Quantitative Seminar (QUAN B001).

Quantitative Methods (QM)

Quantitative Readiness Required (QR)

Scientific Investigation (SI)

Counts Toward Data Science

Back to top

CMSC B113 Computer Science I

Fall 2022, Spring 2023

This is an introduction to the discipline of computer science, suitable for those students with a mature quantitative ability. This fast-paced course covers the basics of computer programming, with an emphasis on program design, problem decomposition, and object-oriented programming in Java. Graduates of this course will be able to write small computer programs independently; examples include data processing for a data-based science course, small games, or estimating likelihood of probabilistic events, etc.. No computer programming experience is necessary or expected. Students are required to register for a weekly lab. Prerequisites: Students must have completed AP level Calculus, Statistics, Physics, Chemistry, Economics, or Computer Science; or IB Mathematics HL; or have a SAT score of 650 or higher in Mathematics or Physics; or ACT score of 28 or higher in Mathematics.

Course does not meet an Approach

Quantitative Methods (QM)

Quantitative Readiness Required (QR)

Counts Toward Data Science

Back to top

BIOL B115 Computing Through Biology: An Introduction

Not offered 2022-23

This course is an introduction to biology through computer science, or an introduction to computer science through biology. The course will This course is an introduction to biology through computer science, or an introduction to computer science through biology. The course will examine biological systems through the use of computer science, exploring concepts and solving problems from bioinformatics, evolution, ecology, and molecular biology through the practice of writing and modifying code in the Python programming language. The course will introduce students to the subject matter and branches of computer science as an academic discipline, and the nature, development, coding, testing, documenting and analysis of the efficiency and limitations of algorithms. Three hours of lecture, three hours of lab per week.

Quantitative Methods (QM)

Scientific Investigation (SI)

Counts Toward Counts toward Data Science

Back to top

CMSC B151 Introduction to Data Structures

Fall 2022, Spring 2023

Introduction to the fundamental algorithms and data structures using Java. Topics include: Object-Oriented programming, program design, fundamental data structures and complexity analysis. In particular, searching, sorting, the design and implementation of linked lists, stacks, queues, trees and hash maps and all corresponding complexity analysis. In addition, students will also become familiar with Java's built-in data structures and how to use them, and acquire competency using a debugger. Students must also register for the weekly lab. Prerequisites: CMSC B109 or CMSC B113 or CMSC H105, or permission of instructor.

Quantitative Methods (QM)

Scientific Investigation (SI)

Counts Toward Data Science

Back to top

CMSC B283 Topics in Computer Science

Fall 2022

This is an intermediate-level topics course. Course content varies. Fall 2022 offering: Computer Science in Society.

Current topic description: Software is prevalent in all aspects of modern life, and has tremendous impact not just on society, but also on the world in which we live. This sophomore/junior-level course explores the relationship between computer software, society, and our world through investigation of the ethical, legal, and policy concerns that must be considered by computing professionals and organizations. Topics may include: privacy, anonymity, and freedom of speech in online spaces; hacking and computer security; viruses, worms, spyware, and spamming; licensing and intellectual property; effects on wellness and mental health; accessibility; ethics and bias in ML and AI; and environmental concerns. In addition to reading assignments, in-class discussions, and reflective writing, this course will include weekly programming labs in Python and a group project to investigate a selected topic in greater depth.

Course does not meet an Approach

Counts Toward Data Science

Back to top

CMSC B383 Recent Advances in Computer Science

Section 001 (Spring 2023): Applications of Natural Language Processing
Section 001 (Spring 2022): Database Systems in Practice

Spring 2023

This is a topics course. Course content varies.

Counts Toward Data Science

Back to top

BIOL B115 Computing Through Biology: An Introduction

Not offered 2022-23

This course is an introduction to biology through computer science, or an introduction to computer science through biology. The course will This course is an introduction to biology through computer science, or an introduction to computer science through biology. The course will examine biological systems through the use of computer science, exploring concepts and solving problems from bioinformatics, evolution, ecology, and molecular biology through the practice of writing and modifying code in the Python programming language. The course will introduce students to the subject matter and branches of computer science as an academic discipline, and the nature, development, coding, testing, documenting and analysis of the efficiency and limitations of algorithms. Three hours of lecture, three hours of lab per week.

Quantitative Methods (QM)

Scientific Investigation (SI)

Counts Toward Counts toward Data Science

Back to top

BIOL B330 Ecological Modeling

Not offered 2022-23

The survival of humanity depends upon natural resources and ecosystem services. To make important decisions about environmental problems, society needs to understand ecological systems. However, ecological systems are inherently complex. Statistical models coupled with empirical data and simulations provide a means of exploring the complexity of ecological systems to better inform environmental decisions. This class will introduce students to a variety of ecological models while instilling an appreciation for the types of uncertainties that may shroud models to better understand inferences made from them. The course will be taught as a hands-on integrated lab/lecture where students will be expected to program regularly, primarily in R. Prerequisite: BIOL B215 or BIOL B250.

Quantitative Readiness Required (QR)

Counts Toward Counts toward Data Science

Back to top

ECON B253 Introduction to Econometrics

Fall 2022

An introduction to econometric terminology and reasoning. Topics include descriptive statistics, probability, and statistical inference. Particular emphasis is placed on regression analysis and on the use of data to address economic issues. The required computational techniques are developed as part of the course. Class cannot be taken if you have taken H203 or H204. Prerequisites: ECON B105 and a 200-level elective. ECON H201 does not count as an elective.

Quantitative Methods (QM)

Counts Toward Data Science

Back to top

ECON B304 Econometrics

Spring 2023

The econometric theory presented in ECON 253 is further developed and its most important empirical applications are considered. Each student does an empirical research project using multiple regression and other statistical techniques. Prerequisites:ECON B253 or ECON H203 or ECON H204 and ECON B200 or ECON B202 and MATH B201 or permission of instructor.

Counts Toward Data Science

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GEOL B104 The Science of Climate Change

Spring 2023

A survey of the science behind climate change. Students will analyze climate data, read primary scientific literature, examine the drivers of climate change, and investigate the fundamental Earth processes that are affected. We will also examine deep-time climate change and the geologic proxies that Earth scientists use to understand climate change on many different time scales. This course is appropriate for students with little to no scientific background but is geared toward students who are considering a science major. Two 90-minute lectures per week.

Quantitative Methods (QM)

Quantitative Readiness Required (QR)

Scientific Investigation (SI)

Counts Toward Counts toward Data Science

Back to top

CITY B201 Introduction to GIS for Social and Environmental Analysis

Fall 2022

This course is designed to introduce the foundations of GIS with emphasis on applications for social and environmental analysis. It deals with basic principles of GIS and its use in spatial analysis and information management. Ultimately, students will design and carry out research projects on topics of their own choosing. Prerequisite: At least sophomore standing and Quantitative Readiness are required (i.e.the quantitative readiness assessment or Quan B001).

Quantitative Readiness Required (QR)

Counts Toward Counts toward Data Science

Counts Toward Counts toward Environmental Studies

Back to top

GEOL B104 The Science of Climate Change

Spring 2023

A survey of the science behind climate change. Students will analyze climate data, read primary scientific literature, examine the drivers of climate change, and investigate the fundamental Earth processes that are affected. We will also examine deep-time climate change and the geologic proxies that Earth scientists use to understand climate change on many different time scales. This course is appropriate for students with little to no scientific background but is geared toward students who are considering a science major. Two 90-minute lectures per week.

Quantitative Methods (QM)

Quantitative Readiness Required (QR)

Scientific Investigation (SI)

Counts Toward Data Science

Back to top

GEOL B210 Cataloging Collections

Section 001 (Fall 2022): Minerals, Museums/Wstrn Colnsm

Fall 2022

This course is an introduction to cataloguing as an integral component of museum collections management. Students will consider the history, theories, and practices of cataloguing as a museum practice as it relates to the different objectives of various types of museums (art, natural history, science, history, zoological). Students will explore how cultural attitudes, institutional policies, and social expectations have historically influenced, and continue to shape, the development of collections management policies and procedures, while undertaking projects related to collections research and cataloguing. They will evaluate and recommend standardized vocabularies to build a collections database that accommodates more complex histories while optimizing searchability. They will engage with instructors who are actively involved in the professional operations of and calls to "decolonize" collections, becoming trained in the fundamentals of cataloguing collections as they actively rethink these structures and contribute to object records.

Course does not meet an Approach

Counts Toward Data Science

Counts Toward Museum Studies

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GNST B425 Praxis III - Independent Study

Counts Toward Data Science

Counts Toward Praxis Program

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BIOL B215 Biostatistics with R

Fall 2022

An introductory course in designing experiments and analyzing biological data. This course is structured to develop students' understanding of when to apply different quantitative methods, and how to implement those methods using the R statistics environment. Topics include summary statistics, distributions, randomization, replication, parametric and nonparametric tests, and introductory topics in multivariate and Bayesian statistics. The course is geared around weekly problem sets and interactive learning. Suggested Preparation: BIOL B110 or B111 is highly recommended. Students who have taken PSYC B205/H200 or SOCL B265 are not eligible to take this course.

Quantitative Methods (QM)

Quantitative Readiness Required (QR)

Scientific Investigation (SI)

Counts Toward Counts toward Biochemistry and Molecular Biology

Counts Toward Counts toward Data Science

Counts Toward Counts toward Health Studies

Back to top

SOCL B265 Quantitative Methods

Fall 2022

An introduction to the conduct of empirical, especially quantitative, social science inquiry. In consultation with the instructor, students may select research problems to which they apply the research procedures and statistical techniques introduced during the course. Using SPSS, a statistical computer package, students learn techniques such as cross-tabular analysis, ANOVA, and multiple regression. Required of Bryn Mawr Sociology majors and minors. Non-sociology majors and minors with permission of instructor.

Quantitative Methods (QM)

Quantitative Readiness Required (QR)

Counts Toward Counts toward Data Science

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HLTH B302 Survey Methods for Health Research

Fall 2022

Surveys are widely used to measure the population prevalence of various health conditions; to better understand the scope and impact of exposure to social and economic stressors on population health; to monitor health-related knowledge, attitudes and practices; and to inform health systems strengthening efforts. Through course material and hands-on experience, students will master the basic elements of survey design, including, operationalizing constructs and formulating research questions, choosing a mode of survey implementation, pretesting the survey instrument, designing a sampling plan, managing field operations, and analyzing and interpreting survey data. Prerequisites: Completion of a 200-level course in the social sciences or permission of the instructor.

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MATH B195 Select Topics in Mathematics

Section 001 (Fall 2022): Intro to Math & Sustainability
Section 001 (Fall 2021): Statistics for Data Science

Fall 2022

This is a topics course. Course content varies.

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Quantitative Methods (QM)

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MATH B205 Theory of Probability with Applications

Not offered 2022-23

The course analyzes repeatable experiments in which short-term outcomes are uncertain, but long-run behavior is predictable. Topics include: random variables, discrete distributions, continuous densities, conditional probability, expected value, variance, the Law of Large Numbers, and the Central Limit Theorem. Prerequisite: Math 201.

Quantitative Methods (QM)

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ECON B304 Econometrics

Spring 2023

The econometric theory presented in ECON 253 is further developed and its most important empirical applications are considered. Each student does an empirical research project using multiple regression and other statistical techniques. Prerequisites:ECON B253 or ECON H203 or ECON H204 and ECON B200 or ECON B202 and MATH B201 or permission of instructor.

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PSYC B205 Research Methods and Statistics

Fall 2022, Spring 2023

An introduction to research design, general research methodology, and the analysis and interpretation of data. Emphasis will be placed on issues involved with conducting psychological research. Topics include descriptive and inferential statistics, research design and validity, analysis of variance, and correlation and regression. Each statistical method will also be executed using computers. Lecture three hours, laboratory 90 minutes a week.

Quantitative Methods (QM)

Scientific Investigation (SI)

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ECON B253 Introduction to Econometrics

Fall 2022

An introduction to econometric terminology and reasoning. Topics include descriptive statistics, probability, and statistical inference. Particular emphasis is placed on regression analysis and on the use of data to address economic issues. The required computational techniques are developed as part of the course. Class cannot be taken if you have taken H203 or H204. Prerequisites: ECON B105 and a 200-level elective. ECON H201 does not count as an elective.

Quantitative Methods (QM)

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PSYC B205 Research Methods and Statistics

Fall 2022, Spring 2023

An introduction to research design, general research methodology, and the analysis and interpretation of data. Emphasis will be placed on issues involved with conducting psychological research. Topics include descriptive and inferential statistics, research design and validity, analysis of variance, and correlation and regression. Each statistical method will also be executed using computers. Lecture three hours, laboratory 90 minutes a week.

Quantitative Methods (QM)

Scientific Investigation (SI)

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PSYC B318 Data Science with R

Spring 2023

TIn this course, students will build and practice data science skills to tidy up disorganized real-world data sets, generate eye catching visualizations, and craft easy to interpret, polished end-products in the R programming environment. Topics include data management and organization, data manipulation and cleaning, quality control and data evaluation, graphing and story-telling with data. Students will learn how to respond to coding challenges with a puzzle-solving, growth-oriented mindset. First-time programmers are welcome.?If you have questions or concerns about preparation and prerequisites, please reach out to the professor.. Prerequsites: Required PSYC B205 (Bryn Mawr - Research Methods and Statistics), OR PSYC H200 (Haverford - Research Methods and Statistics), OR SOCLB265 (Bryn Mawr - Quantitative Methods).

Quantitative Readiness Required (QR)

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PSYC B330 Reproducible Research in Psychology

Not offered 2022-23

How do we know what we know and what we don't know in empirical science? Can we trust the peer review process to filter out invalid claims and identify the claims with enough evidentiary support to merit inclusion in The Literature? This course has two primary aims. The first is to introduce students to the recent history and major conclusions of the "Open Science" reform movement in psychology and related sciences. Students will learn about the structural and methodological factors that are potentially responsible for the high proportion of false positive findings in psychology. The second aim is to introduce modern best practices in research design and statistical computing, which prioritize error control, transparency, and reproducibility. The course will provide a very gentle introduction to the R programming language, which students will use to produce a simple but fully reproducible statistical analysis in the format of a scientific report. Prerequisites: PSYC B205 or PSYC H200 or similar introduction to Research Methods and Statistics.

Quantitative Readiness Required (QR)

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SOCL B265 Quantitative Methods

Fall 2022

An introduction to the conduct of empirical, especially quantitative, social science inquiry. In consultation with the instructor, students may select research problems to which they apply the research procedures and statistical techniques introduced during the course. Using SPSS, a statistical computer package, students learn techniques such as cross-tabular analysis, ANOVA, and multiple regression. Required of Bryn Mawr Sociology majors and minors. Non-sociology majors and minors with permission of instructor.

Quantitative Methods (QM)

Quantitative Readiness Required (QR)

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SOCL B327 Capital & Connections:A Network Approach to Social Structure

Fall 2022

Is it better to have a tightly knit circle of friends or several compartmentalized groups? And better for what--social support, academic achievement, finding a job, coming up with a new idea, sparking a social movement? How might we study questions like these? In this course, we will explore the various ways of understanding social connections as a resource--as a form of capital--and we will learn how to collect and analyze data about networks to investigate the structure of social networks. In particular, we will learn how to think about advantages and disadvantages as resulting from the structure and composition of our social networks. Prerequisite: At least one social science course or permission of instructor.

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Contact Us

Data Science

Marc Schulz
Director of Data Science
Professor of Psychology
mschulz@brynmawr.edu
610-526-5039

Nina Fichera
Administrative Support Staff
nfichera@brynmawr.edu