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.

Spring 2024 DSCI

Course Title Schedule/Units Meeting Type Times/Days Location Instr(s)
BIOL B216-001 Genomics Semester / 1 Lecture: 9:55 AM-11:15 AM TTH Park 264
Bitarello,B., Bitarello,B.
Laboratory: 1:10 PM-4:00 PM W Park 264
CITY B201-001 Introduction to GIS for Social and Environmental Analysis Semester / 1 Lecture: 11:25 AM-12:45 PM TTH Canaday Computer Lab
Kinsey,D.
CITY B328-001 Analysis of Geospatial Data Using GIS Semester / 1 Lecture: 1:10 PM-3:30 PM F Dalton Hall 20
Hurley,J.
CMSC B113-001 Computer Science I Semester / 1 Lecture: 12:55 PM-2:15 PM TTH Park 231
Dinella,E., Dinella,E.
Laboratory: 2:25 PM-3:45 PM TH Park 231
CMSC B151-001 Introduction to Data Structures Semester / 1 Lecture: 1:10 PM-2:30 PM MW Park 245
Dinella,E., Dinella,E.
Laboratory: 2:40 PM-4:00 PM M Park 231
CMSC B383-001 Recent Advances in Computer Science: Database Systems in Practice Semester / 1 LEC: 9:55 AM-11:15 AM TTH Park 231
Towell,G., Towell,G.
Laboratory: 12:55 PM-2:15 PM TH Park 230
CMSC B383-002 Recent Advances in Computer Science: Deep Learning/Lrg Lang Models Semester / 1 LEC: 7:10 PM-8:30 PM T Park 231
Gandhi,R., Gandhi,R.
LEC: 6:10 PM-7:30 PM TH Park 231
Laboratory: 7:40 PM-9:00 PM TH Park 231
DSCI B210-001 Quantifying Happiness: Efforts to study and alter happiness Semester / 1 LEC: 2:25 PM-3:45 PM TTH Dalton Hall 25
Schulz,M.
DSCI B315-001 Bayesian and Frequentist Statistical Inference Semester / 1 Lecture: 12:55 PM-2:15 PM TTH Dalton Hall 1
Kuelz,A.
ECON B253-001 Introduction to Econometrics Semester / 1 Lecture: 12:55 PM-2:15 PM TTH Dalton Hall 2
Anti,S.
ECON B304-001 Econometrics Semester / 1 Lecture: 12:55 PM-2:15 PM TTH Dalton Hall 10
Kim,J.
GEOL B104-001 The Science of Climate Change Semester / 1 Lecture: 11:25 AM-12:45 PM TTH Park 25
Hearth,S.
GNST B425-001 Praxis III - Independent Study 1 Dept. staff, TBA
MATH B104-001 Basic Probability and Statistics Semester / 1 Lecture: 2:40 PM-4:00 PM MW Park 25
Kasius,P.
MATH B104-002 Basic Probability and Statistics Semester / 1 LEC: 2:40 PM-4:00 PM MW Carpenter Library 21
Cheng,L.
MATH B208-001 Introduction to Modeling and Simulation Semester / 1 Lecture: 1:10 PM-2:30 PM MW Park 229
Graham,E.
MATH B295-001 Select Topics in Mathematics: Statistics with R Semester / 1 LEC: 12:10 PM-1:00 PM MWF Dalton Hall 300
Myers,A.
PHIL B258-001 Data Ethics in Social Media Semester / 1 Lecture: 1:10 PM-2:30 PM MW Old Library 110
Faller,A.
PSYC B205-001 Research Methods and Statistics Semester / 1 Lecture: 11:25 AM-12:45 PM TTH Dalton Hall 300
Thapar,A.
PSYC B205-00A Research Methods and Statistics Semester / 1 Laboratory: 10:10 AM-11:30 AM F Canaday Computer Lab
Thapar,A.
PSYC B205-00B Research Methods and Statistics Semester / 1 Laboratory: 1:10 PM-2:30 PM F Canaday Computer Lab
Thapar,A.
PSYC B205-00Z Research Methods and Statistics 1

Fall 2024 DSCI

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

Spring 2025 DSCI

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

 

Additional Fall 2023 Courses from Haverford

 

Music H255

(at Haverford)

Encoding Music:  Digital Approaches to Scores and Sounds

2:30-4 PM MW + Lab

Freedman, Richard

 

CMSC H265 Critical Study of Data and Algorithms MW 2:30-3:55 pm Minocher, Xerxes

2023-24 Catalog Data: DSCI

BIOL B215 Biostatistics with R

Fall 2023

An introductory course in statistical analysis focusing on biological data. This course is structured to develop students' understanding of statistics and probability and when to apply different quantitative methods. The lab component focuses on how to implement those methods using the R statistics environment. Topics include summary statistics, distributions, randomization, replication, and probability. The course is geared around problem sets, lab reports, and interactive learning. No prior experience with programming is required. 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 & Molecular Bio

Counts Toward Data Science

Counts Toward Health Studies

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BIOL B216 Genomics

Spring 2024

An introduction to the study of genomes and genomic data. This course will examine the history of this exciting field, the types of biological questions that can be answered using large biological data sets and complete genome sequences as well as the techniques and technologies that make such studies possible. Topics include genome organization and evolution, comparative genomics, and analysis of transcriptomes, with a focus on animal genomics and humans in particular. Prerequisite: One semester of BIOL 110. BIOL 201 highly recommended.

Writing Attentive

Quantitative Methods (QM)

Scientific Investigation (SI)

Counts Toward Biochemistry & Molecular Bio

Counts Toward Data Science

Counts Toward Health Studies

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BIOL B250 Computational Methods in the Sciences

Fall 2023

A study of how and why modern computation methods are used in scientific inquiry. Students will learn basic principles of analyzing, modeling, and visualizing scientific data through hands-on programming exercises. Content will draw on examples from across the life sciences. This course will use the Python programming language. Six hours of combined lecture/lab per week.

Quantitative Methods (QM)

Quantitative Readiness Required (QR)

Scientific Investigation (SI)

Counts Toward Data Science

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CITY B201 Introduction to GIS for Social and Environmental Analysis

Fall 2023, Spring 2024

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

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CITY B217 Topics in Research Methods

Section 001 (Spring 2023): Planning and Policy Analysis
Section 001 (Fall 2023): Research Mthds/Social Sciences

Fall 2023

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 Data Science

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CITY B328 Analysis of Geospatial Data Using GIS

Spring 2024

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 Data Science

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CMSC B109 Introduction to Computing

Fall 2023

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

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CMSC B113 Computer Science I

Fall 2023, Spring 2024

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

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CMSC B151 Introduction to Data Structures

Fall 2023, Spring 2024

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

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CMSC B283 Topics in Computer Science

Not offered 2023-24

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

Current topic description: From Data to Knowledge is a course that explores the fundamental principles of machine learning and its applications. Students will learn the basics of data analysis, preprocessing, and modeling techniques to extract insights from large datasets. The course will also cover common machine learning algorithms, including supervised and unsupervised learning, regression, classification, and clustering. Emphasis will be placed on understanding the strengths and limitations of different machine learning models and how to implement them in Python code and apply them to real-world problems.

Course does not meet an Approach

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CMSC B383 Recent Advances in Computer Science

Section 001 (Spring 2023): Computational Text Analysis
Section 001 (Spring 2024): Database Systems in Practice
Section 002 (Spring 2024): Deep Learning/Lrg Lang Models

Spring 2024

This is a topics course. Course content varies.

Current topic description: This course covers foundational concepts in computational text analysis. The course is designed for students interested in using text analysis methods to discover and measure concepts and phenomena in large amounts of text. Topics include core computational text analysis concepts, text-based machine learning, deep learning, basic statistical methods, and data collection. The course will culminate around research projects where groups of students will formulate and iteratively refine an empirical question; collect relevant textual data; implement appropriate methods of analysis; and interpret and present their results.

Current topic description: This intensive course is designed for advanced undergraduate students in computer science, data science, linguistics, or related fields who are interested in the cutting-edge domain of Large Language Models (LLMs). The course provides a comprehensive exploration of the theoretical foundations, practical implementations, and ethical considerations surrounding LLMs. Students will gain hands-on experience with Python programming, sequence modeling, transformer architectures, and the deployment of LLMs, including multimodal models. The course also addresses the societal impacts and future research directions in the field of LLMs.

Counts Toward Data Science

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DSCI B100 Introduction to Data Science

Fall 2023

"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

Not offered 2023-24

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

Spring 2024

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

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DSCI B314 Advanced Data Science:Regression & Multivariate Statistics

Fall 2023

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

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DSCI B315 Bayesian and Frequentist Statistical Inference

Spring 2024

What are the different ways in which we can derive conclusions (and certainty of those conclusions) from the same sample of data? This course provides an introduction to the logic and application of statistical methods for analyzing data relevant to fields in data science utilizing two popular perspectives: the traditional Null Hypothesis Significance Testing (NHST) or Frequentist approach as well as the more contemporary approach of Bayesian inference. In doing so, we will tackle two of the most predominate ways of drawing conclusions about the world and gain important insight into quantifying uncertainty in our conclusions. Topics covered include data management and screening; methods for describing and presenting data; t-tests; analysis of variance; advanced applications of the general linear model (i.e., regression) including moderator analyses; and generalized versions of the general linear model such as logistic regression. Some of these topics may be seen as a review from the NHST perspective; however we will jump straight into modeling these parameters using the more flexible general linear model. This is an applied course in statistics. Thus, the emphasis is not on learning math (i.e., doing statistical analyses by hand). Rather, the major objectives of this course are for you to gain a conceptual understanding of statistical inference from both Bayesian and NHST perspectives, learn how to implement statistical analyses using both approaches on a computer using R (a free, open-source program), interpret R output, and communicate the results of statistical analyses in clear and compelling language. No prior knowledge of the R statistical platform is required. Prerequisites: BIOL 215 Biostatistics with R, or PSYC 205 Research Methods and Statistics, or SOCL 265 Quantitative Methods or A comparable statistics course in the BICO (e.g., PSYC H200).

Course does not meet an Approach

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ECON B253 Introduction to Econometrics

Fall 2023, Spring 2024

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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ECON B304 Econometrics

Spring 2024

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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GEOL B104 The Science of Climate Change

Spring 2024

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

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GEOL B210 Cataloging Collections

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

Not offered 2023-24

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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HLTH B302 Survey Methods for Health Research

Not offered 2023-24

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.

Course does not meet an Approach

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MATH B104 Basic Probability and Statistics

Fall 2023, Spring 2024

This course introduces key concepts in descriptive and inferential statistics. Topics include summary statistics, graphical displays, correlation, regression, probability, the Law of Large Numbers, expected value, standard error, the Central Limit Theorem, hypothesis testing, sampling procedures, bias, and the use of statistical software.

Quantitative Methods (QM)

Quantitative Readiness Required (QR)

Counts Toward Data Science

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MATH B195 Select Topics in Mathematics

Section 001 (Fall 2022): Intro to Math & Sustainability

Not offered 2023-24

This is a topics course. Course content varies.

Course does not meet an Approach

Quantitative Methods (QM)

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MATH B205 Theory of Probability with Applications

Not offered 2023-24

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.

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MATH B208 Introduction to Modeling and Simulation

Spring 2024

Mathematical models are constructed to describe the complex world within and around us. Computational methods are employed to visualize and solve these models. In this course, we focus on developing mathematical models to describe real-world phenomena, while using computer simulations to examine prescribed and/or random behavior of various systems. The course includes an introduction to programming (in R or Matlab/Octave), and mathematical topics may include discrete dynamical systems, model fitting using least squares, elementary stochastic processes, and linear models (regression, optimization, linear programming). Applications to economics, biology, chemistry, and physics will be explored. Prior programming experience not required. Prerequisite: MATH B102 or the equivalent (merit score on the AP Calculus BC Exam or placement).

Course does not meet an Approach

Quantitative Methods (QM)

Quantitative Readiness Required (QR)

Counts Toward Data Science

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MATH B295 Select Topics in Mathematics

Section 001 (Fall 2022): History of Math
Section 001 (Fall 2023): Math Modeling and Sustainability
Section 001 (Spring 2024): Statistics with R

Fall 2023, Spring 2024

This is a topics course. Course content varies. Not all topics are open to first year students.

Current topic description: In this course, we will use mathematics to study issues of sustainability. How much energy does a typical person in the United States use? What is the carbon footprint associated with this energy use? Is it possible to meet all of our energy needs using renewable energy? How can we carry out a cost-benefit analysis to determine if a particular energy-saving device is "worth it"? The course has a Praxis component: students will work in teams to analyze a real world sustainability issue of interest to a community partner.

Current topic description: This course introduces descriptive and inferential statistics through work with real data and programming in R (statistical computing and graphics software). Topics include: introduction to data, data visualization, sample statistics, probability, sampling variability, confidence intervals, hypothesis testing, inference for categorical and numerical data, outliers, statistical models, and confounding.

Quantitative Methods (QM)

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PHIL B258 Data Ethics in Social Media

Spring 2024

From sharing our life experiences to reading the news, social media permeates our daily lives. It affects how we communicate, what we buy, and who we vote for. It also generates an immense amount of data, which is eagerly collected by individuals, corporations, and governments. In this course we will investigate some of the threats (and promises) of this data. We will ask questions like: What is the value of privacy online, and how might it be protected? Are we being manipulated by algorithms? Are the algorithms that generate and moderate content biased? What are some of the ways online data can be used for good? Students will investigate these questions through practical and theoretical approaches. Course materials will be drawn from diverse sources including philosophy, data science, sociology, legal theory, and the Internet. Visiting speakers will enrich our discussion by offering academic and professional perspectives on the uses and misuses of data.

Critical Interpretation (CI)

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POLS B233 Intro to Research Design and Data Analysis for PoliSci

Fall 2023

This course offers students an introduction to the research design and methods used in political science. Topics are as follows (but are not limited to): (1) Positivism vs. interpretivism, (2) Causal vs. descriptive inference (3) Conceptualization, operationalization and measurement, (4) Experimental design, (5) Quasi-experimental design, (6) Survey research and sampling, (7) In-depth interviewing, (8) Quantitative data analysis and statistics, (9) Case selection, and (10) Multi-method research design. Students will have problem sets to finish every two weeks for which they will use the necessary software (usually R and R Studio). At the end of the semester, they will submit a research design which they can use as a basis for their senior thesis.

Quantitative Methods (QM)

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PSYC B205 Research Methods and Statistics

Fall 2023, Spring 2024

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

Not offered 2023-24

In 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 experimental design, building statistical models, and visualizing uncertainty. Students will work throughout the term on an independent data science project leveraging real-world data to investigate their hypotheses culminating in a data blitz presentation. Students will learn how to respond to coding challenges with a puzzle-solving, growth-oriented mindset. No prior R experience is not required. 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

Fall 2023

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 2023

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

Not offered 2023-24

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.

Course does not meet an Approach

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flowers

Contact Us

Data Science

Marc Schulz
Professor of Psychology on the Sue Kardas Ph.D. 1971 Professorship and Director of Data Science
mschulz@brynmawr.edu
610-526-5039

Nina Fichera
Administrative Support Staff
nfichera@brynmawr.edu