Fall 2022
Introduction to Quantitative Methods for the Social Sciences and Humanities
Hidefusa Okabe ALM, Business Analytics Advisor, Evernorth
This course introduces the basic concepts of data analysis and statistical computing, both increasingly used in the social sciences and the humanities. The emphasis is on the practical application of quantitative reasoning, visualization, and data analysis. The goal is to provide students pragmatic tools for assessing statistical claims and conducting their own basic statistical analyses. Topics covered include basic descriptive measures, measures of association, sampling and sample size estimation, and simple linear regression. Assignments are based on real-world data and problems in a wide range of fields in the social sciences and humanities, including psychology, sociology, education, and public health. Students may count one of the following courses toward a degree or certificate, but not more than one: MGMT E-104, STAT E-100, STAT E-101 (offered previously), STAT E-102, or STAT E-104.
Prerequisites: No prior data analytic experience required, but a working knowledge of basic high school algebra is recommended.
Spring 2023
Introduction to Quantitative Methods for the Social Sciences and Humanities
Hidefusa Okabe ALM, Business Analytics Advisor, Evernorth
This course introduces the basic concepts of data analysis and statistical computing, both increasingly used in the social sciences and the humanities. The emphasis is on the practical application of quantitative reasoning, visualization, and data analysis. The goal is to provide students pragmatic tools for assessing statistical claims and conducting their own basic statistical analyses. Topics covered include basic descriptive measures, measures of association, sampling and sample size estimation, and simple linear regression. Assignments are based on real-world data and problems in a wide range of fields in the social sciences and humanities, including psychology, sociology, education, and public health. Students may count one of the following courses toward a degree or certificate, but not more than one: MGMT E-104, STAT E-100, STAT E-101 (offered previously), STAT E-102, or STAT E-104.
Prerequisites: No prior data analytic experience required, but a working knowledge of basic high school algebra is recommended.
Spring 2023
Introduction to Quantitative Methods for the Social Sciences and Humanities
Ethan Fosse PhD, Assistant Professor of Sociology, University of Toronto
This course introduces the basic concepts of data analysis and statistical computing, both increasingly used in the social sciences and the humanities. The emphasis is on the practical application of quantitative reasoning, visualization, and data analysis. The goal is to provide students pragmatic tools for assessing statistical claims and conducting their own basic statistical analyses. Topics covered include basic descriptive measures, measures of association, sampling and sample size estimation, and simple linear regression. Assignments are based on real-world data and problems in a wide range of fields in the social sciences and humanities, including psychology, sociology, education, and public health. Students may count one of the following courses toward a degree or certificate, but not more than one: MGMT E-104, STAT E-100, STAT E-101 (offered previously), STAT E-102, or STAT E-104.
Prerequisites: No prior data analytic experience required, but a working knowledge of basic high school algebra is recommended.
Spring 2023
Fundamentals of Biostatistics
Bernard A. Rosner PhD, Professor of Medicine (Biostatistics), Harvard Medical School and Harvard T. H. Chan School of Public Health
This course is an introduction to statistical methods used in biological and medical research. Elementary probability theory, basic concepts of statistical inference, regression and correlation methods, and sample size estimation are covered. Emphasis on applications to medical problems. Students may count one of the following courses toward a degree or certificate, but not more than one: MGMT E-104, STAT E-100, STAT E-101 (offered previously), STAT E-102, or STAT E-104.
Prerequisites: High school algebra.
Spring 2023
Introduction to Statistical Modeling
Bharatendra Rai PhD, Professor of Decision and Information Sciences, Charlton College of Business, University of Massachusetts Dartmouth
This is a second course in statistical inference and is a further examination of statistics and data analysis beyond the introductory course. Topics include t-tools and permutation-based alternatives including bootstrapping, analysis of variance, linear regression, model checking, and refinement. Statistical computing and simulation-based emphasis is also covered as well as basic programming in the R statistical package. Emphasis is placed on thinking statistically, evaluating assumptions, and developing tools for real-life applications. By the end of the course, students should be able to evaluate the strengths and weaknesses of a variety of statistical techniques appearing in the media, scientific literature, or students' own work. Students may not count this course toward a degree if they have already completed STAT E-139, offered previously. Students may not count both CSCI E-106 and STAT E-109 toward a degree or certificate.
Prerequisites: An introductory statistics course such as STAT E- 100 or STAT E-104.
Fall 2022
Intermediate Statistics: Methods and Modeling
Karyn Gunnet-Shoval PhD, Lecturer in Extension and Associate of the Department of Psychology, Harvard University and Assistant Professor of Psychiatry, Geisel School of Medicine, Dartmouth College
This intermediate statistics course is intended to give students familiarity with statistical tools used to analyze data in a variety of disciplines, including psychology, and provides experience reading and understanding studies based on data analysis. The focus is on understanding underlying concepts rather than on memorizing mathematical formulas. Students use R to analyze data and gain experience reading output and translating it into meaningful findings. The course covers linear and logistic regression, various types of ANOVA, as well as effect sizes and power analyses. Students may not take both PSYC E-1900 and STAT E-150 for degree or certificate credit.
Prerequisites: STAT E-100, STAT E-102, STAT E-104, or the equivalent; understanding of univariate statistics, correlation, univariate regression, t-tests, and one-way ANOVA is assumed.
Spring 2023
Intermediate Statistics: Methods and Modeling
Karyn Gunnet-Shoval PhD, Lecturer in Extension and Associate of the Department of Psychology, Harvard University and Assistant Professor of Psychiatry, Geisel School of Medicine, Dartmouth College
This intermediate statistics course is intended to give students familiarity with statistical tools used to analyze data in a variety of disciplines, including psychology, and provides experience reading and understanding studies based on data analysis. The focus is on understanding underlying concepts rather than on memorizing mathematical formulas. Students use R to analyze data and gain experience reading output and translating it into meaningful findings. The course covers linear and logistic regression, various types of ANOVA, as well as effect sizes and power analyses. Students may not take both PSYC E-1900 and STAT E-150 for degree or certificate credit.
Prerequisites: STAT E-100, STAT E-102, STAT E-104, or the equivalent; understanding of univariate statistics, correlation, univariate regression, t-tests, and one-way ANOVA is assumed.
Fall 2022
Quantitative Social Science Methods
Gary King PhD, Albert J. Weatherhead III University Professor, Harvard University
This course introduces students to quantitative methods and how they are applied in social science research. It has two overarching goals. First, we focus on the theory of statistical inference using facts you know to learn about facts you don't know so that students can truly understand the wide range of methods we introduce; feel comfortable using them in their research; digest new ones invented after the course ends; implement them; apply them to data; interpret the results; and explain them to others. Second, students learn how to publish novel substantive contributions in a scholarly journal. A substantial portion of those in this course, including undergraduates and others, publish a revised version of their course paper as their first scholarly journal article.
Prerequisites: STAT E-190 (offered previously) or the equivalent.