Tufts University offers a number of courses on statistics and qualitative analysis. These courses are taught throughout all three campuses and hosted in a number of different schools and departments. Below is an overview of available courses:
The School of Arts & Sciences and School of Engineering
Advanced Statistics I – PSY – 0207 – Fall
Advanced Statistics II – PSY – 0208 – Spring
Applied Regression Analysis – CSHD – 0249 – Spring
Multilevel Modeling in Developmental Science – CSHD – 243 – Fall
Structural Equation Modeling – CSHD – 252 – Fall
Biostatistics – BIO – 0132 – Fall
Fundamentals of Biostatistics – CH – 0036 – Fall
Introductory Statistics – MATH – 0021 – Fall / Spring
Introduction to Programming for Math – MATH – 0022 – Fall
Statistics – MATH – 0166 – Fall / Spring
Political Science Research Methods – PS – 0103 – Fall / Spring
Data Analysis and Statistical Methods – CEE – 0202 – Fall
Public Health – CEE – 0057 – Fall
Fundamentals of Biostatistics – CEE – 0006 – Fall
Quantitative Research Methods – SOC – 0101 – Fall
Qualitative Research Methods – SOC – 0102 – Fall
Quantitative Reasoning – UEP – 0254 – Fall / Spring
Urban Analytics & Visualization – UEP – 0237 – Fall
Statistics – EC – 0013 – Fall / Spring
Statistics for Quantitative Economics – EC – 0014 – Fall
Econometric Analysis – EC – 0107 – Spring
Statistics for Econometrics – EC – 0201 – Fall
Introduction to Data Analytics – EM – 0202 – Fall / Spring
Advanced Topics Data Analytics – EM – 0213 – Fall / Spring
Applied Data Science – EM – 0212 – Spring
Data Visualization & Communication – EM – 0251 – Fall
The Graduate School of Biomedical Sciences & The Friedman School of Nutrition Science and Policy
Biostatistics I – CTS – 0527/Nutr 0206 – Fall
Biostats in Public Health – CMPH 0132 – Spring
Introduction to SAS – Ph 0265 – Spring
Intro to SAS Programming – Nutr 0237 – Spring
Introduction to Bio Stats Regression Methods – Nutr 0323 – Fall
Nutritional Epidemiology – Nutr 0305 – Fall
Principles of Biostatistics – PH 0205/BMS 0201 – Spring
Probability and Statistics Basics – Nutr 0307 – Spring
Regression Analysis for Nutrition Policy – Nutr 0307 – Fall / Spring
Statistical Methods for Nutritional Science & Policy – Nutr 0207 – Fall / Spring
Statistical Methods II – Nutr 0309 – Spring
The Fletcher School of Law and Diplomacy
Analytic Frameworks – DHP P203 – Spring
Econometrics – EIB E213 – Fall / Spring
Data Analysis & Statistical Methods – EIB B205 – Spring
Applied Microeconometrics – EIB E218 – Spring
Econometric Impact Evaluation for Development – EIB 247 – Fall
Data Analysis and Statistical Methods for Business – EIB B206 – Spring
International Economic Policy Analysis – EIB E214 – Fall
The Cummings School of Veterinary Medicine
Statistics I – APP 516 – Fall
Statistics II: Intermediate – APP 517 – Spring
The School of Arts & Sciences and The School of Engineering
Advanced Statistics I – PSY – 0207
Offered: Fall
Instructor: Daniel Barch
Development of statistical concepts for the design and analysis of research. Consideration of the logic of statistical inference, analysis of variance, and nonparametric analysis.
Advanced Statistics II – PSY – 0208
Offered: Spring
Instructor: Daniel Barch
Consideration of certain multivariate designs, regression, and the analysis of covariance.
Applied Regression Analysis – CSHD – 0249
Offered Spring
Instructor: Sara Johnson
(Cross-listed w/DEIJ 276). This course covers the specification, conduct, and evaluation of various regression-based (general linear model) techniques used in developmental science and associated disciplines. Topics include models with continuous predictors, categorical predictors (including dummy codes), and interactions, as well as extensions of the general linear model such as logistic regression. Students must have a solid background in intermediate statistics, including analyses of variance. Prerequisite: CSHD 146 or equivalent intermediate statistics course with a focus on social and behavioral sciences.
Structural Equation Modeling – CSHD – 0252
Offered Fall
Instructor: Sara Johnson
(Cross-listed w/DEIJ 277) This course will provide an introduction to the theory and application of structural equation modeling (SEM) as it applies to developmental science. The course assumes that you have already completed courses in regression and multivariate statistics. The goal of this course is to have students be able to construct, analyze, modify, and test the adequacy of a variety of structural equation models and report the results of their analyses in a manner acceptable in refereed journals.
Multilevel Modeling in Developmental Science – CSHD – 243
Offered: Fall
Instructor: Sara Johnson
Guided individual study of an approved topic. Please contact the department for detailed information.Please see departmental website for specific details.
Biostatistics – Bio – 0132
Offered Fall
Instructor: Helen McCreery
An examination of statistical methods for designing, analyzing, and interpreting biological experiments and observations. Topics include probability, parameter estimation, inference, correlation, regression, analysis of variance, and nonparametric methods. (Group Q.) Required: BIO 13 and 14, or equivalent, plus one additional biology course above BIO 14.
Fundamentals of Biostatistics – CH – 0034
Offered Spring
Instructor Mark Woodin
(Cross listed as CEE 6) Examination of statistical methods used in biomedical and public health studies. Descriptive statistics, probability, basic hypothesis testing, ANOVA, linear regression, logistic regression, and an introduction to survival analysis. Instruction in the use of statistical software will be provided throughout the course. CEE 6 and CEE 156 cannot both be taken for credit
Introductory Statistics – MATH – 0021
Offered Fall / Spring
Instructor: Gail Florence Kaufmann, Wesley Patrick Clawson
Descriptive data analysis, sampling and experimentation, basic probability rules, binomial and normal distributions, estimation, regression analysis, one and two sample hypothesis tests for means and proportions. The course may also include contingency table analysis, and nonparametric estimation. Applications from a wide range of disciplines. Recommendations: High school algebra and geometry.
Introduction to Programming – MATH – 0022
Offered Fall
Instructor: Marshall Mueller
For students without programming backgrounds. Basics of organizing and implementing algorithms and programs in programming languages relevant to computational mathematics and scientific computing, such as Python and Matlab. Emphasis on key concepts needed for programming in any language.
Statistics – MATH – 0166
Offered Fall
Instructor Merek Johnson
Statistical estimation, sampling distributions of estimators, hypothesis testing, regression, analysis of variance, and nonparametric methods. Recommendations: MATH 165 or permission of instructor.
Political Science Research Methods – PS – 0103
Offered Fall / Spring
Instructor: Deborah Schildkraut
(Cross-listed w/ CVS 148). The study of quantitative methods for investigating political issues and policy controversies. Focuses on collecting, analyzing, and presenting data. Emphasizes hands-on training that provides useful skills for academic and professional settings. Topics covered include: measurement, hypothesis development, survey design, experiments, content analysis, significance tests, correlation, and regression. No prior statistics background necessary. Prerequisites: PS 11, 21, 41, 42, or 61. A methodologically focused course.
Data Analysis & Statistical Methods – CEE – 0202
Offered Fall
Instructor: Shafiqul Islam
Exploratory data analysis, descriptive statistics, representativeness of samples, inferential statistics, statistical estimation, sampling distributions of estimators, residual analysis, and confidence intervals. Recommendation: CEE201
Public Health – CEE – 0057
Offered Fall
Instructor: David Gute
An introduction to the public health approach is provided. The epidemiological model of the disease process is used to study a variety of infectious and noninfectious diseases. The wide variety of nonmedical approaches to disease control is emphasized. The public health aspects of vital statistics, evaluation, and administrative decision making are introduced and applied to current problems in public health. Recommendations: Consent of instructor.
Fundamentals of Biostatistics – CEE – 0006
Offered Fall / Spring
Instructor: Mark Woodin
(Cross listed as CH 36) Examination of statistical methods used in biomedical and public health studies. Descriptive statistics, probability, basic hypothesis testing, ANOVA, linear regression, logistic regression, and an introduction to survival analysis. Instruction in the use of statistical software will be provided throughout the course. CEE 6 and CEE 156 cannot both be taken for credit
Quantitative Research Methods – SOC – 0101
Offered Fall
Instructor: Felipe Dias
Data analysis and statistics for the social sciences. Sampling, describing data, and logic of inference, especially with surveys. Introduction to microcomputer tools for analysis and graphic display. Answering research questions through individual or group projects. Recommendations: One introductory social science course.
Qualitative Research Methods – SOC – 0102
Offered Fall
Instructor: Helen Marrow
Advanced course specializing in qualitative methods (e.g., ethnographic observation and in-depth interviewing). Epistemological foundations of qualitative research design, sampling, and methods, including related ethical issues. Development of an original research project, including formulation of a researchable sociological question, review of the sociological literature, identification of field sites, development of consent forms and interview guides, completion of systematic observations and qualitative interviews, coding and analysis of data, and development of a clear sociological argument and final paper simulating a published academic journal article. Strongly recommended for students interested in conducting independent qualitative research (e.g., senior honors theses).
Quantitative Reasoning – UEP – 0254
Offered Fall
Instructor: Shomon Shamsuddin
Required core course for M.A. and M.P.P. students. Introduction to the use of quantitative thinking. Designed to develop basic statistical skills as indispensable tools for policy research, planning and decision making. Students learn how to choose and apply statistical tools to data sources, when and how statistical tools can be used to analyze data, and how to interpret and understand others’ quantitative research. Statistical software is used to facilitate learning through active application of statistical tools. Although prior coursework in statistics is not required, students are required to have a solid understanding of college-level algebra. Waiver permitted for students with an undergraduate major or substantial work-related experience in statistics subject to faculty approval. Recommendations: College-level algebra
Urban Analytics & Visualization – UEP – 0237
Offered Fall
Instructor: Shan Jiang
With rapid urbanization, the development of data science, machine learning, and the emergence of ubiquitous sensing technologies, cities have become the foci of multidisciplinary investigations. This course is designed to equip future planners, data scientists, and policymakers with computational methods and tools to acquire new urban data from social media, crowdsourcing, and sensor networks, and use them to represent, understand, and visualize complex urban environments in comprehensive and scientific ways, to make informed decisions to design, plan and manage smart, sustainable and resilient cities. Prerequisites: (1) Intro to GIS; (2) UEP Coding Bootcamp (offered in Su/Fall/Spring) or an equivalent Data Lab Workshop.
Statistics – EC – 0013
Offered Fall / Spring
Instructor: Thomas Downes
An introduction to basic statistical techniques that are used in economic analysis. Major topics include probability, discrete random variables, continuous random variables, sampling distributions, estimation, and hypothesis testing. The course will conclude with some theory and applications of the linear regression model. Required of all economics majors. Prerequisites: Economics 5 or Economics 8; and Mathematics 32 or above.
Statistics for Quantitative Economics – EC – 0014
Offered Fall
Instructor: Marcelo Bianconi
Mathematical statistical techniques used in econometric analysis. Topics include probability theory, discrete and continuous random variables and their joint distributions, sampling and estimation, hypothesis testing, rudiments of statistical programming, and brief introduction to the theory of linear regression. Foundation course for students pursuing the quantitative economics major track. Economics 13 and 14 may not both be taken for credit. Prerequisites: one of EC 5 or EC 8; and MATH 34 or MATH 39 or above.
Econometric Analysis – EC – 0107
Offered Fall
Instructor: Silke Forbes
The study of multiple regression models and their applications. Focus on the properties of estimation techniques when the classical regression assumptions hold and when they do not hold. Topics include least squares estimation, instrumental variable estimation, panel data techniques, and time-series techniques. EC 15 and 107 may not both be taken for credit.
Statistics for Econometrics – EC – 0210
Offered Fall
Instructor: Adam Storeygard
Topics from probability and mathematical statistics that provide a foundation for econometrics. Includes elements of probability theory, estimation, hypothesis testing, and statistical programming. Emphasizes conditional expectations, and population vs. sample interpretations of bivariate regression. Offered in the Fall only. Prerequisites: Enrollment in the economics graduate program or instructor permission
Introduction to Data Analytics – EM – 0202
Offered Fall / Spring
Instructor: Kishore Pochampally
Data identification, analysis and interpretation to drive strategic and operational success in technology-based companies. Data-based decisions with uncertain or ambiguous conditions, and development of models for decision-making in a business setting.
Advanced Topics Data Analytics – EM – 0213
Offered Fall / Spring
Instructor: Kishore Pochampally
Use of established software frameworks to support applied projects in data science. Topics include: PyMC3, PySpark, NLP tools, PyTorch, TensorFlow, Keras, Flask and Docker. Pre-requisite EM206 or instructor consent.
Applied Data Science – EM – 0212
Offered Spring
Instructor: Kyle Monahan
Foundational skills for data science practice in business. Using data and systematic methods to generate business value. Shaping data practices within organizations.
Data Visualization & Communication – EM – 0251
Offered Fall
Instructor: Kyle Monahan
Concepts of visual analytics such as visual reports and dashboards with a hands-on tutorial using leading self-service business intelligence and data visualization tools. Hands-on exercises to identify datasets, explore, analyze, filter and structure data to communicate via visualizations.
The Graduate School of Biomedical Sciences & The Friedman School of Nutrition Science and Policy
Biostatistics I – CTS – 0527/Nutr 0206
Offered Fall 2017
Instructor: Farzad Noubary, Angie Rodday
This course introduces basic principles and applications of statistics to problems in clinical research. Topics covered include descriptive statistics, probability and random variation, sampling, hypothesis testing, proportions, measures of frequency, t-tests, chi-square tests, one-way analysis of variance, correlation, linear regression and nonparametric statistics.
Biostats in Public Health – CMPH 0132
Spring 2018
Instructor: Alice Tang
Biostatistical procedures used in the majority of biomedical investigations. Emphasizes the use of each procedure, how and when to apply it, and how to interpret the results. Each lecture is followed by a computer laboratory session that requires students to use appropriate methods when analyzing a specific data set. Students progress from a reading knowledge of biostatistics to the beginnings of conversant facility.
Introduction to SAS – Ph 0265
Offered Spring 2018
Instructor: David Tybor
This intensive course will introduce students to the concepts and syntax necessary for basic data management and analysis using the SAS System for Windows. Emphasis will be placed upon learning methods for data manipulation and gaining the necessary skills to prepare data for statistical analysis. SAS procedures for descriptive statistics will be covered, and methods for data visualization will be introduced. Weekly homework assignments and in-class exercises will allow students to gain practical experience solving SAS programming problems.
Intro to SAS Programming – Nutr 0237
Offered Spring 2018
Instructor: Richard Chechile
This first half-semester course will provide students with sufficient knowledge of how to obtain, manage, clean and prepare data in SAS for Windows. Emphasis will be placed on the basics of SAS programming and data manipulation. Upon completion, students should be able to use data in SAS and be familiar with the procedure steps required to import and export data, create SAS data sets, produce descriptive statistics, and clean and transform data in preparation for statistical analyses. In-class exercises and weekly homework assignments will allow students to acquire hands-on experience solving common SAS programming tasks. Important to Note: This course is designed for students with no SAS programming experience. Students with a basic knowledge of SAS should not take this course. If you are an NEPI student, it is strongly encouraged that you register for this course and acquire SAS Programming skills as you work toward completing your degree. Prerequisite: Graduate standing or instructor consent.
Introduction to Bio Stats Regression Methods – Nutr 0323
Offered: Fall 2017
Instructor: Kenneth Kwan Ho Chui
This course provides a survey of regression techniques for outcomes common in biomedical and public health data including continuous, count, binary, and time series data. Emphasis is on developing a conceptual understanding of the application of these techniques to solving problems, rather than to the numerical details. The objectives of this course are to (1) recognize when data can be described and analyzed by a regression model;(2) develop and interpret regression models; (3) plan and conduct an appropriate analysis; (4) summarize the results of the analysis in terms of the research question in both verbal and written formats suitable for targeted audiences. Prerequisites: PH 205 with a grade B or better, or NUTR 207 or NUTR 206 or NUTR 209 with a grade B- or better. Students who wish to use other statistics course as prerequisites please gather a syllabus of the said course and contact the course director for consent before the end of the add/drop period. This course is cross-listed with Public Health (PH 206).
Nutritional Epidemiology – Nutr 0305
Offered Fall 2017
Instructor: Fang Fang Zhang
This course is designed for graduate students at either the Master’s or Ph.D. level, who are interested in conducting or better interpreting epidemiologic studies relating diet and nutrition to health and disease. There is an increasing awareness that various aspects of diet and nutrition may be important contributing factors in chronic disease. There are many important problems, however, in the implementation and interpretation of nutritional epidemiologic studies. The purpose of this course is to examine epidemiologic methodology in relation to nutritional measures, and to review the current state of knowledge regarding diet and other nutritional indicators as etiologic factors in disease. This course is designed to enable students to better conduct nutritional epidemiologic research and/or to better interpret the scientific literature in which diet or other nutritional indicators are factors under study. Prerequisites: NUTR 0202: Principles of Nutrition Science and NUTR 0204/PH 0201: Principles of Epidemiology and NUTR 0206: Biostatistics I/PH 0205: Principles of Biostatistics. Prerequisites may not be taken concurrently with NUTR 0305.
Principles of Biostatistics – PH 0205/BMS 0201
Offered Spring 2018
Instructor: David Tybor
This course provides an introduction to the basic principles and applications of statistics as they are applied to problems in clinical and public health settings. Topics include the description and presentation of data, random variables and distributions, descriptive statistics, introduction to probability, estimation, elements of hypothesis testing, and one- and two-sample tests, ANOVA (including repeated-measures), non-parametric tests, and an introduction to linear and logistic regression. Lectures, problem sets, and computer output are used to develop these and additional concepts. Graduate standing.
Probability and Statistics Basics – Nutr 0307
Offered Spring 2018
Instructor: Daniel Cox
This course provides an introduction to the principles of probability and statistics and emphasizes the application of these disciplines to the analysis of basic science biomedical research data. Topics include: summarizing data, testing for differences between means, analysis of variance, laws of probability, common probability distributions, the analysis of categorical data, correlation, linear regression, nonlinear curve fitting, and exponential processes
Regression Analysis for Nutrition Policy – Nutr 0307
Offered Fall 2017 /Spring 2018
Instructor: Parke Wilde
Part two of a one-year, two-semester course sequence in statistics. This course is intended for students whose main focus is non-experimental or survey-based research. The course covers non-experimental research design, simple linear regression, multiple regression, analysis of variance, non-linear functional forms, heteroskedasticity, complex survey designs, and real-world statistical applications in nutrition science and policy. Students will make extensive use of Stata for Windows. NOTE: Students cannot receive credit for both NUTR 307 and its second semester counterpart NUTR 309. Pre-requisites: NUTR 207 or NUTR 206/209 and graduate standing or instructor consent.
Statistical Methods for Nutritional Science & Policy – Nutr 0207
Offered Fall 2017 /Spring 2018
Instructor: Farzad Noubary
Part one of a one-year, two-semester course covering descriptive statistics, graphical displays, confidence intervals, hypothesis testing, t test, chi-square test, nonparametric tests, multiple linear regression, multiple logistic regression, experimental design, multi-factor and multiple comparisons procedures. Students will learn how to use Stata statistical analysis software. This course was formerly listed as NUTR 209A-02. Prerequisite: Graduate standing or instructor consent.
Statistical Methods II – Nutr 0309
Offered Spring 2018
Instructor: Farzad Noubary
Part two of a one-year, two-semester course covering descriptive statistics, graphical displays, confidence intervals, hypothesis testing, t test, chi-square test, nonparametric tests, multiple linear regression, multiple logistic regression, experimental design, multi-factor and multiple comparisons procedures. Students will make extensive use of SPSS for Windows.NOTE: Students cannot receive credit for both NUTR 309 and NUTR 307. Pre-requisites: NUTR 206/209 and graduate standing or instructor consent.
The Fletcher School of Law and Diplomacy
Analytic Frameworks – DHP P203
Offered Spring 2018
Instructor: Carolyn Friedman
Introduction to the basic tools of policy analysis and decision making, providing students with analytic skills to make policy decisions in many types of organizations. The course includes an introduction to public policy objectives, decision making, and the role of analysis. Students then learn powerful analytic decision-making techniques, including decision trees, Bayes theorem, utility theory, prospect theory, game theory, benefit-cost analysis, and tipping models. Case studies are used to learn the policy analysis tools while applying them to real world policy problems. Cases come from developed and developing countries, and cover many different policy fields. No background in economics or statistics is required.
Econometrics – EIB E213
Offered Fall 2017 and Spring 2018
Instructor: Julie Schaffner
This course introduces students to the primary tools of quantitative data analysis employed in the study of economic and social relationships. It equips students for independent econometric research and for critical reading of empirical research papers. The course covers ordinary least squares, probit, fixed effects, two-stage least squares and weighted least squares regression methods, and the problems of omitted variables, measurement error, multicollinearity, heteroskedasticity, and autocorrelation. Prerequisites include familiarity with (1) basic probability and statistics (B205), and (2) basic concepts of functions and derivatives (E210m or an introductory calculus course).
Data Analysis & Statistical Methods – EIB B205
Offered Spring 2018
Instructor: Robert Nakosteen
This course provides an overview of classical statistical analysis and inference. The language and methods of statistics are used throughout the Fletcher curriculum, both in the classroom and in assigned readings. In addition, the language and methods of statistical analysis have permeated much of academic and professional writing, as well as media reporting. The goal is to present a broad introduction to statistical thinking, concepts, methods, and vocabulary.
Applied Microeconometrics – EIB E218
Offered Spring 2018
Instructor: Shinsuke Tanaka
This course is designed for students who are interested in learning advanced econometric techniques to answer a broad array of academic empirical research questions. To this end, this course covers a set of theoretical and practical econometric techniques for conducting high-quality empirical research. The curriculum is oriented toward applied practitioners by focusing on research design and methods for causal inference. The topics include several of the most commonly used estimation techniques (i.e., matching, fixed effects, difference-in-differences, instrumental variables, and regression discontinuity). Econometrics (at the level of E213) is a strict prerequisite and may not be taken concurrently
Econometric Impact Evaluation for Development – EIB 247
Offered Fall 2017
Instructor: Jenny C. Aker
The course will cover econometric impact evaluation theory and empirical methods for measuring the impact of development programs (including randomization, difference-in-differences, regression discontinuity, and propensity score matching). The curriculum will combine theory and practice. The primary objectives of the course are to provide participants with the skills to understand the value and practice of impact evaluation within development economics, design and implement impact evaluations and act as critical consumers of impact evaluations. Econometrics (at the level of E213) is a strict prerequisite and may not be taken concurrently.
Data Analysis and Statistical Methods for Business – EIB B206
Offered Spring 2018
Instructor: Robert Nakosteen
This course provides an overview of classical statistical analysis and inference. The goal is to provide you with an introduction to statistical thinking, concepts, methods, and vocabulary. This will give you some tools for dealing with statistical methods you may encounter in your coursework or research while at The Fletcher School, especially “regression analysis,” which is covered at the end of the course. In addition, this section of the course has a particular emphasis on business applications. Students who plan to or have completed B205 are not permitted to take this course.
International Economic Policy Analysis – EIB E214
Offered Fall 2017
Instructor: Michael W. Klein
This seminar teaches skills that enable students to bridge the gap between coursework in economics and the types of economic analysis used in both government and private sector settings. These skills and tools build on material taught in Econometrics. The topics addressed in the seminar include a range of timely and policy-relevant issues in international economics and macroeconomics. The seminar will also focus on the use of empirical analysis for writing concise, effective policy memorandums. Open to students who have completed E213, which may be taken concurrently.
The Cummings School of Veterinary Medicine
Statistics I – APP 516
Offered Fall 2017
Instructor: Allen Rutber
This course introduces students to the basics of statistical methods and research design. Students learn to state hypotheses, evaluate sampling procedures, create and manage data sets, and carry out basic statistical testing. Examples are drawn from research in veterinary medicine, animal science, human-animal relationships, and animal ecology.
Statistics II: Intermediate – APP 517
Offered Spring 2018
Instructor: Megan Mueller, Phyllis Mann
Intended for advanced research track students and tailored to their interests, this course will focus on experimental design and analysis of survey data, exploring the use of analysis of variance (ANOVA) and regression models, factor analysis, and other advanced techniques using SPSS or an equivalent statistical package.