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.

## Spring 2021

COURSE | TITLE | SCHEDULE/ UNITS |
MEETING TYPE TIMES/DAYS | LOCATION / INSTRUCTION MODE | INSTR(S) |
---|---|---|---|---|---|

CITY B201-001 | Introduction to GIS for Social and Environmental Analysis | Second Half / 1 | Lecture: 9:40 AM-11:00 AM MTH | Taylor Hall FHybrid: In-Person & Remote | Hurley,J. |

CMSC B113-001 | Computer Science I | Second Half / 1 | Lecture: 11:10 AM-12:30 PM TF | Remote Instruction | Normoyle,A. |

CMSC B113-00A | Computer Science I | Second Half / 1 | Laboratory: 12:40 PM- 2:00 PM T | Remote Instruction | Normoyle,A. |

CMSC B113-00B | Computer Science I | Second Half / 1 | Laboratory: 10:10 AM-11:00 AM F | Remote Instruction | Normoyle,A. |

CMSC B113-00Z | Computer Science I | Second Half / 1 | In Person | ||

CMSC B151-001 | Introduction to Data Structures | Second Half / 1 | Lecture: 11:10 AM-12:30 PM TF | Park 25In Person | Towell,G., Towell,G. |

Laboratory: 12:40 PM- 2:00 PM F | Park 231In Person | ||||

ECON B253-001 | Introduction to Econometrics | Second Half / 1 | Lecture: 11:10 AM-12:30 PM MTH | Goodhart Hall AuditoriumHybrid: In-Person & Remote | Anti,S. |

ECON B304-001 | Econometrics | Second Half / 1 | Lecture: 9:40 AM-11:00 AM MTH | Remote Instruction | Lambie-Hanson,T. |

GEOL B104-001 | The Science of Climate Change | Second Half / 1 | Lecture: 11:10 AM-12:30 PM TF | Park 200Hybrid: In-Person & Remote | Hearth,S. |

GNST B425-001 | Praxis III - Independent Study | Second Half / 1 | Dept. staff, TBA | ||

PSYC B205-001 | Research Methods and Statistics | Second Half / 1 | Lecture: 9:40 AM-11:00 AM MTH | Remote Instruction | Thapar,A. |

PSYC B205-00A | Research Methods and Statistics | Second Half / 1 | Laboratory: 9:40 AM-11:00 AM F | Remote Instruction | Thapar,A. |

PSYC B205-00B | Research Methods and Statistics | Second Half / 1 | Laboratory: 1:10 PM- 2:30 PM F | Remote Instruction | Thapar,A. |

PSYC B205-00Z | Research Methods and Statistics | Second Half / 1 | In Person | ||

PSYC B330-001 | Reproducible Research in Psychology | Second Half / 1 | Lecture: 11:10 AM-12:30 PM MTH | Remote Instruction | Albert,D. |

## Fall 2021

COURSE | TITLE | SCHEDULE/ UNITS |
MEETING TYPE TIMES/DAYS | LOCATION / INSTRUCTION MODE | INSTR(S) |
---|---|---|---|---|---|

DSCI B100-001 | Introduction to Data Science | Semester / 1 | LEC: 2:25 PM- 3:45 PM TTH | Park 338In Person | Thapar,A. |

DSCI B201-001 | Ethics in Data Sciences | Semester / 1 | LEC: 2:40 PM- 4:00 PM MW | In Person | |

BIOL B250-001 | Computational Methods in the Sciences | Semester / 1 | Lecture: 10:10 AM-11:30 AM WF | Park 245In Person | Record,S., Record,S. |

Laboratory: 1:10 PM- 4:00 PM W | Canaday Computer LabIn Person | ||||

BIOL B330-001 | Ecological Modeling | Semester / 1 | LEC: 1:10 PM- 4:00 PM TH | Park 246In Person | Record,S. |

CITY B217-001 | Topics in Research Methods: Research Mthds/Social Sciences | Semester / 1 | LEC: 2:40 PM- 4:00 PM MW | In Person | Hurley,J. |

CMSC B109-001 | Introduction to Computing | Semester / 1 | Lecture: 1:10 PM- 2:30 PM MW | Park 338In Person | Interim,R. |

CMSC B109-00A | Introduction to Computing | Semester / 1 | Laboratory: 2:40 PM- 3:30 PM M | Park 231In Person | Interim,R. |

CMSC B109-00B | Introduction to Computing | Semester / 1 | Laboratory: 12:10 PM- 1:00 PM W | Park 231In Person | Interim,R. |

CMSC B113-001 | Computer Science I | Semester / 1 | Lecture: 12:55 PM- 2:15 PM TTH | Park 338In Person | Murphy,C. |

CMSC B113-002 | Computer Science I | Semester / 1 | Lecture: 1:10 PM- 2:30 PM MW | Park 338In Person | Murphy,C. |

CMSC B113-00A | Computer Science I | Semester / 1 | Laboratory: 2:25 PM- 3:15 PM T | Park 231In Person | Murphy,C. |

CMSC B113-00B | Computer Science I | Semester / 1 | Laboratory: 11:55 AM-12:45 PM TH | Park 231In Person | Murphy,C. |

CMSC B113-00C | Computer Science I | Semester / 1 | Laboratory: 2:40 PM- 4:00 PM W | Park 231In Person | Murphy,C. |

CMSC B151-001 | Introduction to Data Structures | Semester / 1 | Lecture: 12:55 PM- 2:15 PM TTH | Park 338In Person | Interim,R., Interim,R. |

Laboratory: 2:25 PM- 3:45 PM T | Park 230In Person | ||||

CMSC B151-002 | Introduction to Data Structures | Semester / 1 | Lecture: 9:55 AM-11:15 AM TTH | Park 338In Person | Towell,G., Towell,G. |

Laboratory: 11:25 AM-12:45 PM TH | Park 230In Person | ||||

MATH B195-001 | Select Topics in Mathematics: Statistics for Data Science | Semester / 1 | LEC: 2:40 PM- 4:00 PM MW | Park 336In Person | Myers,A. |

PSYC B205-001 | Research Methods and Statistics | Semester / 1 | Lecture: 1:10 PM- 2:30 PM MW | In Person | Albert,D. |

PSYC B205-00A | Research Methods and Statistics | Semester / 1 | Laboratory: 10:40 AM-12:00 PM F | Canaday Computer LabIn Person | Albert,D. |

PSYC B205-00B | Research Methods and Statistics | Semester / 1 | Laboratory: 1:10 PM- 2:30 PM F | Canaday Computer LabIn Person | Albert,D. |

PSYC B205-00Z | Research Methods and Statistics | Semester / 1 | In Person | ||

PSYC B314-001 | Advanced Data Science:Regression & Multivariate Statistics | Semester / 1 | Lecture: 9:10 AM-12:00 PM TH | Bettws Y Coed 239In Person | Schulz,M. |

SOCL B265-001 | Quantitative Methods | Semester / 1 | Lecture: 11:40 AM- 1:00 PM MW | In Person | Wright,N. |

SOCL B317-001 | Comparative Social Policy: Cuba, China, US, Scandinavia | Semester / 1 | Lecture: 1:10 PM- 4:00 PM M | Dalton Hall 1In Person | Karen,D. |

## Spring 2022

COURSE | TITLE | SCHEDULE/ UNITS |
MEETING TYPE TIMES/DAYS | LOCATION / INSTRUCTION MODE | INSTR(S) |
---|---|---|---|---|---|

CITY B201-001 | Introduction to GIS for Social and Environmental Analysis | Semester / 1 | Lecture: 2:25 PM- 3:45 PM TTH | Canaday Computer LabIn Person | Hurley,J. |

CMSC B113-001 | Computer Science I | Semester / 1 | Lecture: 12:55 PM- 2:15 PM TTH | In Person | Kumar,D. |

CMSC B113-00A | Computer Science I | Semester / 1 | Laboratory: 2:25 PM- 3:15 PM T | In Person | Kumar,D. |

CMSC B113-00B | Computer Science I | Semester / 1 | Laboratory: 11:55 AM-12:45 PM TH | In Person | Kumar,D. |

CMSC B151-001 | Introduction to Data Structures | Semester / 1 | Lecture: 12:55 PM- 2:15 PM TTH | In Person | Towell,G., Towell,G. |

Laboratory: 2:25 PM- 3:45 PM TH | In Person | ||||

CMSC B383-001 | Recent Advances in Computer Science | Semester / 1 | Lecture: 11:40 AM- 1:00 PM MW | In Person | Towell,G., Towell,G. |

Laboratory: 1:10 PM- 2:30 PM W | In Person | ||||

ECON B253-001 | Introduction to Econometrics | Semester / 1 | Lecture: 2:25 PM- 3:45 PM TTH | Dalton Hall 300In Person | Anti,S. |

ECON B304-001 | Econometrics | Semester / 1 | Lecture: 10:10 AM-11:30 AM MW | In Person | Lambie-Hanson,T. |

GEOL B104-001 | The Science of Climate Change | Semester / 1 | Lecture: 11:25 AM-12:45 PM TTH | Park 180In Person | Hearth,S. |

MATH B205-001 | Theory of Probability with Applications | Semester / 1 | Lecture: 1:10 PM- 2:30 PM MW | Park 336In Person | Myers,A. |

PSYC B205-001 | Research Methods and Statistics | Semester / 1 | Lecture: 11:25 AM-12:45 PM TTH | In Person | Thapar,A. |

PSYC B205-00A | Research Methods and Statistics | Semester / 1 | Laboratory: 10:10 AM-11:30 AM F | Canaday Computer LabIn Person | Thapar,A. |

PSYC B205-00B | Research Methods and Statistics | Semester / 1 | Laboratory: 1:10 PM- 2:30 PM F | Canaday Computer LabIn Person | Thapar,A. |

PSYC B330-001 | Reproducible Research in Psychology | Semester / 1 | Lecture: 9:10 AM-12:00 PM F | In Person | Albert,D. |

### 2021-22 Catalog Data

DSCI B100 Introduction to Data ScienceFall 2021

"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 Introduction to Data Science

Counts toward NeuroscienceDSCI B201 Ethics in Data Sciences

Fall 2021

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 Introduction to Data ScienceDSCI B100 Introduction to Data Science

Fall 2021

"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 Introduction to Data Science

Counts toward Counts toward NeuroscienceCMSC B109 Introduction to Computing

Fall 2021

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 principals.

Quantitative Methods (QM)

Quantitative Readiness Required (QR)

Scientific Investigation (SI)

Counts toward Counts toward Introduction to Data ScienceCMSC B151 Introduction to Data Structures

Fall 2021, Spring 2022

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. Required: 2 hour lab. Prerequisites: CMSC B110 or CMSC B113 or H105, or permission of instructor.

Quantitative Methods (QM)

Scientific Investigation (SI)

Counts toward Counts toward Introduction to Data ScienceBIOL B250 Computational Methods in the Sciences

Fall 2021

A study of how and why modern computation methods are used in scientific inquiry. Students will learn basic principles of visualizing and analyzing scientific data through hands-on programming exercises. The majority of the course will use the R programming language and corresponding open source statistical software. Content will focus on data sets from across the sciences. Six hours of combined lecture/lab per week.

Quantitative Methods (QM)

Quantitative Readiness Required (QR)

Scientific Investigation (SI)

Counts toward Counts toward Biochemistry and Molecular Biology

Counts toward Counts toward Introduction to Data Science

Counts toward Counts toward Environmental Studies

Counts toward Counts toward NeuroscienceBIOL B115 Computing Through Biology: An Introduction

Not offered 2021-22

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 Introduction to Data ScienceBIOL B250 Computational Methods in the Sciences

Fall 2021

A study of how and why modern computation methods are used in scientific inquiry. Students will learn basic principles of visualizing and analyzing scientific data through hands-on programming exercises. The majority of the course will use the R programming language and corresponding open source statistical software. Content will focus on data sets from across the sciences. Six hours of combined lecture/lab per week.

Quantitative Methods (QM)

Quantitative Readiness Required (QR)

Scientific Investigation (SI)

Counts toward Biochemistry and Molecular Biology

Counts toward Introduction to Data Science

Counts toward Environmental Studies

Counts toward NeuroscienceBIOL B330 Ecological Modeling

Fall 2021

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 Introduction to Data ScienceCITY B201 Introduction to GIS for Social and Environmental Analysis

Spring 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 Introduction to Data Science

Counts toward Environmental StudiesCITY B217 Topics in Research Methods

Section 001 (Fall 2020): Research Mthds/Social Sciences

Section 001 (Fall 2021): Research Mthds/Social Sciences

Fall 2021

This is a topics course. Course content varies.

__Current topic description:__Research Methods in the Social Sciences: This course is a hands-on introduction to the research process. It will provide students with the practical skills needed to design, conduct, and analyze original research of the complexity of a thesis-length project. Specifically, students will build knowledge and experience in research design (how to craft a good research question and match methods to the question), research methods (quantitative methods involving analysis of pre-existing large-n survey data and the qualitative methods of case study, content analysis, and interviewing), and data analysis (basic descriptive and inferential statistical analysis using Excel and SPSS along with qualitative data analysis using computer-assisted qualitative data analysis). No computer programming is required or taught.

Quantitative Methods (QM)

Counts toward Introduction to Data ScienceECON B253 Introduction to Econometrics

Spring 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 Introduction to Data ScienceCITY B328 Analysis of Geospatial Data Using GIS

Not offered 2021-22

An advanced course for students with prior GIS experience involving individual projects and collaboration with faculty. Completion of GIS (City 201) or equivalent with 3.7 or above. Instructor permission required after discussion of project.

Quantitative Readiness Required (QR)

Counts toward Introduction to Data ScienceCMSC B109 Introduction to Computing

Fall 2021

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 principals.

Quantitative Methods (QM)

Quantitative Readiness Required (QR)

Scientific Investigation (SI)

Counts toward Introduction to Data ScienceCMSC B113 Computer Science I

Fall 2021, Spring 2022

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 and problem decomposition. 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 basic communications programs (such as a chat client). No computer programming experience is necessary or expected. Prerequisite: Must pass either the Quantitative Readiness Assessment or the Quantitative Seminar (QUAN B001)

Course does not meet an Approach

Quantitative Methods (QM)

Quantitative Readiness Required (QR)

Counts toward Introduction to Data ScienceBIOL B115 Computing Through Biology: An Introduction

Not offered 2021-22

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 Introduction to Data ScienceCMSC B151 Introduction to Data Structures

Fall 2021, Spring 2022

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. Required: 2 hour lab. Prerequisites: CMSC B110 or CMSC B113 or H105, or permission of instructor.

Quantitative Methods (QM)

Scientific Investigation (SI)

Counts toward Introduction to Data ScienceCMSC B383 Recent Advances in Computer Science

Spring 2022

This is a topics course. Course content varies. Fall 2020 Topic: Web Development for Data Science. Spring 2021 Topic: Software Development. Please contact instructor for more details.

Counts toward Introduction to Data ScienceBIOL B115 Computing Through Biology: An Introduction

Not offered 2021-22

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 Introduction to Data ScienceBIOL B330 Ecological Modeling

Fall 2021

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 Introduction to Data ScienceECON B253 Introduction to Econometrics

Spring 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 Introduction to Data ScienceECON B304 Econometrics

Spring 2022

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 Introduction to Data ScienceCITY B201 Introduction to GIS for Social and Environmental Analysis

Spring 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 Introduction to Data Science

Counts toward Counts toward Environmental StudiesGEOL B104 The Science of Climate Change

Spring 2022

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. One required all-day field trip on a weekend.

Quantitative Methods (QM)

Quantitative Readiness Required (QR)

Scientific Investigation (SI)

Counts toward Introduction to Data ScienceBIOL B250 Computational Methods in the Sciences

Fall 2021

A study of how and why modern computation methods are used in scientific inquiry. Students will learn basic principles of visualizing and analyzing scientific data through hands-on programming exercises. The majority of the course will use the R programming language and corresponding open source statistical software. Content will focus on data sets from across the sciences. Six hours of combined lecture/lab per week.

Quantitative Methods (QM)

Quantitative Readiness Required (QR)

Scientific Investigation (SI)

Counts toward Counts toward Biochemistry and Molecular Biology

Counts toward Counts toward Introduction to Data Science

Counts toward Counts toward Environmental Studies

Counts toward Counts toward NeuroscienceGNST B425 Praxis III - Independent Study

Counts toward Introduction to Data Science

Counts toward Praxis ProgramSOCL B265 Quantitative Methods

Fall 2021

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 Introduction to Data ScienceSOCL B317 Comparative Social Policy: Cuba, China, US, Scandinavia

Fall 2021

This course will examine different countries' policy choices to address different societal challenges. Four societal types - socialist (Cuba), post-socialist (China), capitalist (US), and social-democratic (Scandinavia) - will be studies to help us understand how these different kinds of societies conceive of social problems and propose and implement attempted solutions. We will examine particular problems/solutions in four domains: health/sports; education; environment; technological development. As we explore these domains, we will attend to methodological issues involved in making historical and institutional comparisons

Counts toward Counts toward Introduction to Data Science

Counts toward Counts toward Education

Counts toward Counts toward Health StudiesMATH B195 Select Topics in Mathematics

Section 001 (Fall 2021): Statistics for Data Science

Fall 2021

This is a topics course. Course content varies. For Fall 2021 the course introduces basic descriptive and inferential statistics through work with real data and programming in R (statistical computing and graphics software). This course is open to students of all majors. Although it does not count towards the math major, math majors may choose to take it as a College elective. Satisfies the Data Analytics Foundational requirement for the Data Science minor.

Course does not meet an Approach

Counts toward Introduction to Data ScienceMATH B205 Theory of Probability with Applications

Spring 2022

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)

Counts toward Introduction to Data ScienceECON B304 Econometrics

Spring 2022

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 Counts toward Introduction to Data SciencePSYC B205 Research Methods and Statistics

Fall 2021, Spring 2022

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)

Counts toward Counts toward Introduction to Data ScienceECON B253 Introduction to Econometrics

Spring 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 Introduction to Data ScienceSOCL B317 Comparative Social Policy: Cuba, China, US, Scandinavia

Fall 2021

This course will examine different countries' policy choices to address different societal challenges. Four societal types - socialist (Cuba), post-socialist (China), capitalist (US), and social-democratic (Scandinavia) - will be studies to help us understand how these different kinds of societies conceive of social problems and propose and implement attempted solutions. We will examine particular problems/solutions in four domains: health/sports; education; environment; technological development. As we explore these domains, we will attend to methodological issues involved in making historical and institutional comparisons

Counts toward Counts toward Introduction to Data Science

Counts toward Counts toward Education

Counts toward Counts toward Health StudiesPSYC B205 Research Methods and Statistics

Fall 2021, Spring 2022

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)

Counts toward Introduction to Data SciencePSYC B314 Advanced Data Science:Regression & Multivariate Statistics

Fall 2021

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.

Counts toward Introduction to Data Science

Counts toward Health StudiesPSYC B318 Data Science with R

Not offered 2021-22

This course provides a broad introduction to the field of data science via the statistical programming language, R. The course focuses on using computational methods and statistical techniques to analyze massive amounts of data and to extract knowledge. It provides an overview of tools for data acquisition and cleaning, data manipulation, data analysis and evaluation, visualization and communication of results, data management and big data systems. The course surveys the complete data science process from data to knowledge and gives students hands-on experience with tools and methods. Prerequisites: PSYC B205, PSYC H200, or SOCL B265. Students with good statistical preparation in math or other disciplines should consult with the instructor to gain permission to take the class.

Quantitative Readiness Required (QR)

Counts toward Introduction to Data Science

Counts toward NeurosciencePSYC B330 Reproducible Research in Psychology

Spring 2022

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)

Counts toward Introduction to Data ScienceSOCL B265 Quantitative Methods

Fall 2021

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 Introduction to Data ScienceSOCL B317 Comparative Social Policy: Cuba, China, US, Scandinavia

Fall 2021

This course will examine different countries' policy choices to address different societal challenges. Four societal types - socialist (Cuba), post-socialist (China), capitalist (US), and social-democratic (Scandinavia) - will be studies to help us understand how these different kinds of societies conceive of social problems and propose and implement attempted solutions. We will examine particular problems/solutions in four domains: health/sports; education; environment; technological development. As we explore these domains, we will attend to methodological issues involved in making historical and institutional comparisons

Counts toward Introduction to Data Science

Counts toward Education

Counts toward Health Studies