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Statistical & Data Sciences

Statistical and Data Sciences at Smith College
The Statistical & Data Sciences (SDS) Program links faculty and students from across the college interested in learning things from data. At Smith, students learn statistics by doing—class time emphasizes problem-solving and hands-on contact with data. Many courses employ student-driven projects that allow students to pursue their interest in fields such as economics, psychology, political science, sociology, engineering, biology, environmental science, neuroscience and geology.

News & Events

Upcoming Talks & Lectures

The Statistical & Data Sciences Program hosts regular talks & lectures that are free and open to the public. No prior exposure to statistics is presumed. Stay tuned for exciting presentations coming up for the fall 2019 semester!

    Intermittent Events

    Please see the Western Mass Statistics and Data Science Meetup for additional events.

    Recurring Events

    Requirements

    • Identify and work with a wide variety of data types (including, but not limited to, categorical, numerical, text, spatial and temporal) and formats (e.g. CSV, XML, JSON, relational databases, audio, video, etc.). 
    • Extract meaningful information from data sets that have a variety of sizes and formats.
    • Fit and interpret statistical models, including but not limited to linear regression models. Use models to make predictions, and evaluate the efficacy of those models and the accuracy of those predictions.
    • Understand the strengths and limits of different research methods for the collection, analysis and interpretation of data. Be able to design studies for various purposes.
    • Attend to and explain the role of uncertainty in inferential statistical procedures.
    • Read and understand data analyses used in research reports. Contribute to the data analysis portion of a research project in at least one applied discipline.
    • Compute with data in at least one high-level programming language, as evidenced by the ability to analyze a complex data set.
    • Work in multiple languages and computational environments.
    • Convey quantitative information in written, oral and graphical forms of communication to both technical and nontechnical audiences.
    • Assess the ethical implications to society of data-based research, analyses, and technology in an informed manner. Use resources, such as professional guidelines, institutional review boards, and published research, to inform ethical responsibilities.

    The program is designed to produce highly skilled, versatile statisticians and data scientists who possess powerful abilities for analyzing data. As such, SDS students learn not only how to build statistical models that generate predictions, but how to validate these models and interpret their parameters. Students learn to use their ingenuity to “wrangle” with complex data streams and construct informative data visualizations.

    The major in statistical & data sciences consists of 10 courses, including depth in both statistics and computer science, an integrating course in data science, a course that emphasizes communication and an application domain of expertise. All but the application domain course must be graded; the application course can be taken S/U.

    Advisers
    Benjamin Baumer, R. Jordan Crouser, Randi Garcia, Katherine Halvorsen, Albert Y. Kim, Katherine Kinnaird, Miles Ott

    Study Abroad Adviser
    Benjamin Baumer

    Requirements

    See the major diagram below for prerequisites, and see the Note on course substitutions following the description of the major.

    SDS Major Diagram (Spring 2019)

     

    • Foundations and Core (5 courses): The following required courses build foundational skills in mathematics, statistics and computer science that are necessary for learning from modern data.
      • CSC 111: Intro to Programming
      • SDS 192: Intro to Data Science
      • MTH 211: Linear Algebra
      • MTH/SDS 220 or SDS 201: Introductory Statistics
      • MTH/SDS 291: Multiple Regression
    • Programming Depth (1 course): One additional course that deepens exposure to programming.
      • CSC 212: Data Structures
      • CSC 220: Advanced Programming Techniques
      • CSC/SDS 235: Visual Analytics—must take programming intensive track
      • CSC/SDS 293: Machine Learning
      • CSC/SDS 352: Parallel & Distributed Computing
    • Statistics Depth (1 course): One additional course that provides exposure to additional statistical models.
      • MTH/SDS 290: Research Design and Analysis
      • CSC/SDS 293: Machine Learning
      • SDS 390: Topics in SDS
        • Categorical Data Analysis
        • Structural Equation Modeling
        • Statistical Analysis of Social Networks
    • Communication (1 course): One course that focuses on the ability to communicate in written, graphical and/or oral forms in the context of data.
      • CSC/SDS 109: Communicating with Data 
      • CSC/SDS 235: Visual Analytics
      • SDS 236: Data Journalism
    • Application Domain (1 course): Every student is required to take a course that allows them to conduct a substantial data analysis project evaluated by an expert in a specific domain of application. The requirement is normally satisfied by one of the following options:
      • SDS 300: Applications of Statistical & Data Sciences
      • A research seminar (normally 300-level) or special studies of at least two credits. Normally, the domain would be outside of mathematics, statistics and computer science.
      • A departmental honors thesis in another major (normally not including MTH or CSC)

    A student and adviser should identify potential application domains of interest as early as possible, since many suitable courses will have prerequisites. Normally, this should happen during the fourth semester or at the time of major declaration, whichever comes first. The determination of whether a course satisfies the requirement will be made by the student’s major adviser.

    A nonexhaustive list of previously approved application domain courses includes:

    • SDS 300: Applications of Statistical & Data Sciences
      • Population Health: Data & Analysis
    • PSY 301: Research Design and Analysis
    • PSY 358: Research Seminar in Clinical Psychology
    • PSY/SDS 364: Research Seminar on Intergroup Relationships
    • ECO 311: Seminar: Topics in Economic Development
    • ECO 319: Seminar: Economics of Migration
    • ECO 363: Seminar: Inequality
    • EGR 389: Data Mining
    • BIO 334: Bioinformatics & Comparative Mol Bio
    • NSC 318: Neurobiology
    • Capstone (1 course): Every student is required to complete a capstone experience, which exposes them to real-world data analysis challenges.
      • SDS 410: Capstone
    • Electives: (as needed to complete to 10 courses): Provided that the requirements listed above are met, any of the courses listed above may be counted as electives to reach the 10 course requirement. Five College courses in statistics and computer science may be taken as electives. Additionally, the following courses may be counted toward completion of the major.
      • MTH/SDS 246: Probability
      • CSC 252: Algorithms
      • CSC 290: Artificial Intelligence
      • CSC 390: Seminar on Artificial Intelligence

    Note on course substitutions: CSC 111 may be replaced by a 4 or 5 on the AP computer science exam. MTH/SDS 220 or SDS 201 may be replaced by a 4 or 5 on the AP statistics exam. Replacement by AP courses does not diminish the total number of courses required for either the major or the minor. MTH 211 may be replaced by petition in exceptional circumstances. Any one of ECO 220, GOV 203, PSY 201, or SOC 201 may directly substitute for MTH/SDS 220 or SDS 201 without the need to take another course, in both the major and minor. Note that MTH/SDS 220 and ECO 220 require Calculus. Students should be aware that substituting for MTH/SDS 220 or SDS 201 could leave them without R programming experience, which is needed in subsequent courses, such as SDS 290 & 291. Five-College equivalents may substitute with permission of the program. MTH/SDS 107 and EDC 206 are important courses but do not count for the major or the minor. An Honors Thesis (SDS 430D) generally cannot substitute for the capstone SDS 410.

    The Minor in Statistical & Data Sciences

    The minor in statistical & data sciences consists of six courses, according to the following requirements:

    • four of the five foundational and core courses required for the major, not including MTH 211
    • any course satisfying the programming depth requirement for the major
    • any course satisfying the communication requirement for the major

    Should these three requirements be fulfilled with fewer than six courses, any of the courses in SDS or CSC that count toward the major may be counted towards the minor. Ordinarily, no more than one course graded S/U will be counted toward the minor.

    The Minor in Applied Statistics

    The interdepartmental minor in applied statistics offers students a chance to study statistics in the context of a field of application of interest to the student. The minor is designed with enough flexibility to allow a student to choose among many possible fields of application.

    The minor consists of five courses. Among the courses used to satisfy the student’s major requirement, a maximum of two courses can count toward the minor. Ordinarily, no more than one course graded S/U will be counted toward the minor.

    Students who have taken AP statistics in high school and received a 4 or 5 on the AP Statistics Examination will not be required to repeat the introductory statistics course, but they will be expected to complete five courses to satisfy the requirements for the minor in applied statistics.

    The student must take one of the following courses and no more than one of these courses will count toward the minor. (Students presenting a 4 or 5 on the AP Statistics Examination will receive exemption from this requirement.)

    • PSY/SDS 201: Statistical Methods for Undergraduate Research
    • MTH/SDS 220: Introduction to Probability and Statistics
    • MTH 219: Probability and Statistics for Engineers, Mathematicians and Computer Scientists
    • MTH 220: Introduction to Probability and Statistics
    • ECO 220: Introduction to Statistics and Econometrics
    • GOV 203: Empirical Methods in Political Science
    • SOC 201: Evaluating Information
    • SOC 202: Quantitative Research Methods

    The student must also take both of the following courses:

    • MTH 290 Research Design and Analysis
    • MTH 291 Multiple Regression

      The student must choose two (or more) application courses. Courses not on the following list must be approved by the student’s SDS adviser if they are to count toward the minor.

      • BIO 232: Evolution
      • BIO 234: Genetic Analysis
      • BIO 235: Genes and Genomes Laboratory
      • BIO 266: Principles of Ecology
      • BIO 267: Principles of Ecology Laboratory
      • BIO 334: Bioinformatics & Comparative Molecular Bio
      • ECO 240: Econometrics
      • ECO 311: Seminar: Topics in Economic Development
      • ECO 351: Seminar: The Economics of Education
      • ECO 362: Seminar: Population Economics
      • ECO 363: Seminar: Inequality
      • ECO 396: Seminar: International Financial Markets
      • EGR 389: Techniques for Modeling Engineering Processes
      • GOV 312: Seminar in American Government
      • MTH 246: Probability
      • MTH 292: Data Science
      • PSY 319: Research Seminar in Biological Rhythms
      • PSY 325: Research Seminar in Health Psychology
      • PSY 335: Research Seminar in the Study of Youth and Emerging Adults
      • PSY 358: Research Seminar in Clinical Psychology
      • PSY 369: Research Seminar on Categorization and Intergroup Behavior
      • PSY 373: Research Seminar in Personality Psychology
      • PSY 375: Research Seminar on Political Psychology
      • SOC 202: Methods of Social Research

      Students planning to minor in applied statistics should consult with their advisers when selecting applications courses. Some honors theses and special studies courses may apply if these courses focus on statistical applications in a field.

      Also see the concentration in statistics within the mathematics major offered by the Department of Mathematics and Statistics.

      It is possible for a Smith student to obtain a master of science in statistics from the University of Massachusetts Amherst in five years (four years at Smith plus one at UMass), through the Fifth Year MS in Statistics Program. Interested students should consult with the director of the program.

      Students interested in pursuing graduate work in statistics or data science should consult with their major adviser to plan an appropriate course of study. In either case, a solid foundation in mathematics (calculus I, II, and III, as well as linear algebra) is essential.

      Graduate Programs in Statistics

      The ASA maintains several lists of graduate programs in statistics that may help you find options that suit your needs.

      Graduate Programs in Data Science

      As a newer discipline, programs in data science are still in their infancy. The ASA maintains a list of graduate programs in “Big Data”, although this should not be conflated with data science. A more comprehensive list of data science degree programs is maintained by datascience.community.


      Courses

      Please see the SDS section of the online course catalog for the most recent information.

      Choosing a First Statistics Course

      A student who wishes to study statistics may place themself according to the following guidelines.

      A student with prior work in calculus or discrete math at college should start with Introduction to Probability & Statistics (MTH/SDS 220 or SDS 201, 5 credits). This is the recommended statistics course for biological sciences majors, and satisfies the basis requirement for engineering, environmental science, neuroscience and psychology. This is also the recommended course for a student who took AP statistics but didn't take the exam, or received a grade of 3 or below. ECO 220 is also a course at this general level.

      A student with four years of high school math (but little or no calculus) should select SDS 201 or PSY 201 (Statistical Methods for Undergraduates). SDS 201 also satisfies the basis requirement for psychology. Other introductory courses at this level include GOV 203 and SOC 201.

      A student with less preparation should select SDS 107 (Statistical Thinking) or SDS 109 (Communicating with Data).

      A student who received a score of 4 or 5 on the AP Statistics Exam should take MTH/SDS 290 (Research Design and Analysis) or MTH/SDS 291 (Regression Analysis).

      Taking Statistics Away (EGR Majors)

      Please see the guidelines for Picker Engineering majors.

      Courses Offered Through the Program

      • MTH/SDS 107: Statistical Thinking
      • CSC/SDS 109: Communicating with Data
      • SDS 192: Introduction to Data Science
      • SDS 201: Statistical Methods for Undergraduates
      • MTH/SDS 220: Introduction to Probability and Statistics
      • CSC/SDS 235: Visual Analytics
      • SDS 236: Data Journalism
      • MTH/SDS 246: Probability
      • MTH/SDS 290: Research Design and Analysis
      • MTH/SDS 291: Multiple Regression
      • SDS 293: Modeling for Machine Learning
      • SDS 300: Applications of Statistical & Data Sciences
      • MTH/SDS 320: Mathematical Statistics
      • CSC/SDS 352: Parallel and Distributed Computing
      • PSY/SDS 364: Research Seminar on Intergroup Relationships
      • SDS 390: Topics in Statistical & Data Sciences
      • SDS 400: Special Studies
      • SDS 430D: Honors Thesis
      • SDS 410: Capstone

      Cross-Listed Courses

      • CSC 111: Introduction to Computer Science Through Programming
      • CSC/MTH 205: Modeling in the Sciences
      • MTH 211: Linear Algebra
      • CSC 212: Programming With Data Structures
      • CSC 252: Algorithms
      • CSC 290: Artificial Intelligence
      • CSC 294 Computational Machine Learning
      • CSC 390: Seminar on Artificial Intelligence

      This page is intended to help EGR majors and their advisers identify appropriate courses at other universities that will satisfy the statistics requirement for the EGR major. It supplements the memo sent to EGR faculty on February 20, 2017. As noted in that memo, “equivalence of courses taken elsewhere [are] determined by...[a] qualified member of the SDS program.” Herein, we delineate the criteria used to determine equivalence in order to promote transparency and ensure a uniform experience for all.

      The following criteria are used to verify that a course taken to satisfy the statistics requirement for the EGR major (hereafter “COURSE”) is satisfactory:

      • Rigor: COURSE must be at or above the level of rigor of MTH/SDS 220. This is the primary criteria.
      • Statistical reasoning: COURSE must include statistical topics like hypothesis testing, confidence intervals, and regression—not just probability topics like random variables, distributions and expected value.

      Exception: Students who have earned a 4 or 5 on the AP statistics exam can waive these requirements. They can fulfill their statistics requirement by taking any non-introductory course in probability or statistics (e.g., MTH/SDS 246, MTH/SDS 290, MTH/SDS 291, CSC/SDS 293, etc.).

      SDS faculty will use the following set of questions to guide their thinking on whether a course meets the above criteria. Normally, a replacement course would satisfy all or nearly all of these questions.

      • Does COURSE cover most or all of the topics listed in the description for MTH/SDS 220?
        • An application-oriented introduction to modern statistical inference: study design, descriptive statistics; random variables; probability and sampling distributions; point and interval estimates; hypothesis tests, resampling procedures and multiple regression.
      • Does COURSE include linear regression as a topic in the syllabus?
      • Does COURSE use a comprehensive textbook?
      • Does COURSE include any prerequisites (e.g., calculus) that indicate mathematical maturity?
      • Is COURSE for statistical practice (like MTH/SDS 220) and not just for statistical concepts (like SDS 107)?
      • Does COURSE explicitly mention the use of a statistical computing environment like R, SPSS, Stata, JMP or SAS (that is, something beyond Excel or TI calculators)?
      • Does COURSE include the word “business” in the course title or textbook? Smith College does not give credit for business classes.

      EGR majors should consult this page first, and then present a syllabus (preferably electronic) to the SDS study abroad adviser. Although MTH/SDS 220 is a 5-credit course, the number of credits is not a determining factor.

      List of Previously Approved Courses

      For reference only, we provide a list of previously approved courses. Courses change over time and vary by instructor -- students should understand that just because a course was previously approved in the past does not guarantee that it will be approved in the future.

      Previously Approved

      Previously Not Approved

      • ECE 214: Introduction to Probability and Random Processes, UMass
        • This course would be considered equivalent to MTH/SDS 246. Note the Calc III requirement.

      Fall 2019

      • CSC/SDS 109: Communicating with Data (David Rockoff)
      • SDS 192: Introduction to Data Science (Albert Kim)
      • MTH/SDS 220: Introduction to Probability and Statistics (Miles Ott; David Rockoff)
      • MTH/SDS 246: Probability (Katherine Halvorsen)
      • MTH/SDS 291: Multiple Regression (Dhanamalee Bandara; Dhanamalee Bandara)
      • CSC 294: Computational Machine Learning (Katherine Kinnaird)
      • PSY/SDS 364: Research Seminar in Intergroup Relationships (Randi Garcia)
      • SDS 390: Topics in SDS: Categorical Data Analysis (Katherine Halvorsen)
      • SDS 390: Topics in SDS: Programming for Data Science (Ben Baumer)
      • SDS 400: Special Studies
      • SDS 404: Honors Thesis
      • SDS 410: Capstone (Ben Baumer)

      Spring 2020

      • CSC/SDS 109 Communicating with Data (Katherine Kinnaird)
      • SDS 192: Introduction to Data Science (Ben Baumer)
      • SDS 201: Statistical Methods for Undergraduates (Dhanamalee Bandara)
      • MTH/SDS 220: Introduction to Probability and Statistics (Albert Kim; Katherine Kinnaird)
      • SDS 236: Data Journalism (Ben Baumer & Naila Moreira)
      • MTH/SDS 290: Research Design and Analysis (Katherine Halvorsen)
      • MTH/SDS 291: Multiple Regression (Ben Capistrantt)
      • SDS 293: Modeling for Machine Learning (Albert Kim)
      • SDS 300: Applications of SDS: Educational Analysis (David Rockoff)
      • MTH/SDS 320: Mathematical Statistics (Katherine Halvorsen)
      • SDS 400: Special Studies
      • SDS 404: Honors Thesis
      • SDS 410: Capstone (Miles Ott)

       


      “Employment of statisticians is projected to grow 27 percent from 2012 to 2022, much faster than the average for all occupations. Growth is expected to result from more widespread use of statistical analysis to make informed business, healthcare, and policy decisions.”
      Bureau of Labor Statistics


      Associated Faculty

      Program Committee

      Glenn Ellis
      Professor of Engineering

      Howard J. Gold
      Professor of Government

      Philip K. Peake
      Professor of Psychology

      Charles P. Staelin
      Professor of Economics

      Advisory Committee

      Shannon Audley-Piotrowski
      Assistant Professor of Education & Child Study

      Joanne Corbin
      Professor of Social Work & Director of the Ph.D. Program

      Patricia DiBartolo
      Caroline L. Wall ’27 Professor of Psychology

      Rob Dorit
      Professor of Biological Sciences

      Simon Halliday
      Assistant Professor of Economics

       

      Simon Halliday
      Assistant Professor of Economics

      Catherine McCune
      Spinelli Center for Quantitative Learning

      Elizabeth Savoca
      Professor of Economics

      L. David Smith
      Professor of Biological Sciences

      Vis Taraz
      Assistant Professor of Economics

      Nancy Whittier
      Sophia Smith Professor of Sociology

      Tina Wildhagen
      Associate Professor of Sociology

       

      Think statistics is just about calculating things? Think again. 

      The fields of statistics and data science are growing exceptionally fast. As technology continues to reshape our world, more and more data are being collected on any number of subjects. There is a growing belief among decision makers that these data can be useful. Yet the process of transforming data into actionable information is challenging.

      To analyze modern streams of data, government agencies, nonprofits (NGOs) and private industries seek data analysts with technical skills (programming ability), the ability to reason quantitatively about data and uncertainty, and strong communication skills (in written, oral and visual forms). People with these skills are in high demand.

      Statisticians use their deep understanding of mathematics and probability theory to reason about variation and uncertainty in data. For example, if a drug was observed to have a positive effect on patient outcomes in a clinical trial, was that effect large enough—given the sample size and assumptions about how the data was collected—to justify concluding that the drug actually worked? Statisticians build, validate and interpret models. They design experiments and collaborate with scientists of all stripes to make precise estimates of unknown quantities.

      Data science is an emerging field that combines elements of mathematics, statistics and computer science to extract meaning from data. Data scientists work with large, complex, messy and live data sources. Often working on questions that are not well-defined, data scientists use their creativity and technical ability to dig deep into "Big Data." They build models, make predictions, and develop static and dynamic ways to visualize data.

      While at Smith, statistics students have created innovative classroom activities, authored honored theses, developed sophisticated statistical software and contributed to the Office of Institutional Research. Graduates have found internships with the National Institutes of Standards and Techonology and the New York Mets, employment with GoogleMIT’s Lincoln Laboratory, and MassMutual’s Data Science Development Program. Student have also gone on to graduate school at UC Berkeley, The Harvard School of Public Health, Ohio State University, and the University of Massachusetts.

      Awards

      Statistical & Data Sciences Research Prize

      The program awards an annual prize to a student majoring in SDS who performed outstanding research during her time at Smith. The faculty meets every spring to review the research projects they have supervised and select the prize recipient, who is then notified at the annual Awards Ceremony the day before Commencement.

      Awardees

      • 2019: Julianna Alvord ’19
      • 2018: Wencong (Priscilla) Li ’18
      • 2017: Weijia (Vega) Zhang ’17
      Five College Statistics Prize

      The Five College Statistics prize is awarded to one student at each of the Five Colleges, at their discretion. The award may be presented to a student satisfying any one of the following criteria (based on faculty vote from that institution):

      • outstanding independent research, thesis or capstone course project in statistics
      • outstanding service to statistics on their campus (or across campuses)
      • outstanding use of Five College statistics resources
      • outstanding non-senior pursuing further study in statistics

      Awardees

      • 2019: Yue Kuang ’19, Zixian Li ’19 and Jingyi Liu ’19 (outstanding senior capstone project)
      • 2018: Paige Patrick AC and Rutendo Madziwo ’19 (outstanding service to statistics)
      • 2017: Abby Doctor ’17 (outstanding senior capstone project)
      • 2016: Yiwen Zhu ’16 and Emma Beauchamp ’16 (outstanding theses)
      • 2015: Weijia (Vega) Zhang ’18 (outstanding non-senior)
      Departmental Honors

      2019

      2018

      2017

      Competitions

      ASA Five College DataFest

      DataFest is a national undergraduate data analysis competition hosted at UMass each spring, in which students work in teams of up to five to extract insights from a complex, challenging data set over a weekend.

      Recent Winners from Smith

      2018

      • Best in Group (Panel B): A-super-NOVA
        Audrey Bertin ’21, Riley Boeth ’18, Emma Livingston ’20, Clara Rosenberg ’20, Kara VanAllen ’20

      2017

      • Best in Show: DataBest
        Zainab Aqdas Rizvi ’18, Subashini Sridhar ’18J, Ji Young Yun ’18, Ji Wong Chung ’18 and Van Nguyen ’18
      • Best Statistical Interpretation: Normally Distributed
        Zixian Li ’19, Angie Dinh ’17, Abby Doctor ’17, Erina Fukuda ’18 and Raeesa Alam ’19J
      • Best Business Insight: Standard Divination
        Isabella Zhu ’20, Cas Sweeney ’19, Sarah Abowitz ’20 and Garcia Sun ’20
      Undergraduate Statistics Project Competition (USCLAP)

      The class project competition is for undergraduate students who conduct projects as part of an introductory or intermediate level statistics course. Most projects submitted to the USCLAP competition involve analyzing real data using existing statistical techniques. Students may choose any topic on which to conduct a study and students may use existing data or collect their own.

      Recent Winners from Smith

      2017

      2016

      Undergraduate Research Project Competition (USRESP)

      The research project competition is for undergraduate students who conduct research projects coming from activities like summer research projects, senior capstone course research projects, independent research projects (e.g., independent studies), Honor's college research projects, or extension of class research projects. Some submissions to USRESP are applied research projects using existing statistical/analytical techniques to solve real world problems, which others are involve methodological research involving statistical applications or simulation studies evaluating different techniques. Students may collect their own data or use existing data and students can select any topic to study.

      Recent Winners from Smith

      2015

      • 2nd Place: Sara Stoudt ’15: Geostatistical Models for the Spatial Distribution of Uranium in the Continental United States

      Mu Sigma Rho is the national statistics honor society, and the Boston Chapter of the ASA oversees nominations from Smith each spring. We also maintain a complete archive of recipients

      Recent Smith College inductees include:

      • Riley Boeth
      • Julianna Calabrese
      • Minji Kang
      • Katerina Kyuchukova
      • Katherine Lemiesz
      • Zixian Li
      • Rutendo Madziwo
      • Van Nguyen
      • Lily Qian
      • Syeda Zainab Aqdas Rizvi
      • Emily Ruppel
      • Zhu Shen
      • Yi Wang
      • Jing Xia
      • Ji Young Yun

          Contact

          Department of Statistical & Data Sciences

          Bass Hall 412
          Smith College
          Northampton, MA

          Phone: 413-585-3908
          Email: pdevilliers@smith.edu
          Peter de Villiers
          Director, Program in Statistical & Data Sciences