Fall 2024
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 2025
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 2025
Introduction to Quantitative Methods for the Social Sciences and Humanities
Stacey Gelsheimer PhD, Senior Lecturer on Economics, Boston University
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 2025
Fundamentals of Biostatistics
Amy Tsurumi PhD, Assistant Professor of Surgery, Massachusetts General Hospital and Harvard Medical School
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 2025
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 (offered previously).
Fall 2024
Intermediate Statistics: Methods and Modeling
Natasha Prasadini Ramanayake PhD, Associate Psychologist, Psychiatry, Brigham and Women's Hospital
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 only take one of the following for degree or certificate credit: PSYC E-1900 (offered previously), STAT E-150, or STAT E-160.
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 2025
Intermediate Statistics: Methods and Modeling
Carolyn Gardner-Thomas PhD, Director, Mathematics for Teaching Program, Harvard Extension School
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 only take one of the following for degree or certificate credit: PSYC E-1900 (offered previously), STAT E-150, or STAT E-160.
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 2024
Statistics for the Behavioral Sciences
Max Krasnow PhD, Lecturer in Extension, Harvard University
Statistics are the tools we use to summarize and describe the world around us and to explore the causal processes at work. Understanding statistics and how they are used and misused is vital to assimilating information as an informed citizen, as well as pursuing a career in the behavioral sciences and other fields. This course covers introductory and intermediate-level statistics, and covers topics including principles of measurement, central tendency and variability, probability and distributions, correlation, hypothesis testing, t-tests, analysis of variance and covariance, linear and logistic regression, and chi-square tests. Students may only take one of the following for degree or certificate credit: PSYC E-1900 (offered previously), STAT E-150, or STAT E-160.
Spring 2025
Statistics for the Behavioral Sciences
Adam Smith PhD, Consulting Associate, Leadership Advisory Services, Spencer Stuart
Understanding and performing statistical analyses is a vital ability for those working in the psychological and behavioral sciences. Regardless of a person's specialty, the concepts of variability, probability, and predictive modeling are fundamental for answering questions involving data. This intermediate-level statistics course is designed to help students understand how to manage data, formulate strong questions and hypotheses, perform analyses, and accurately evaluate statistical results and output. We use the free and open-source program R/RStudio to run statistical analyses. Because we use this tool, both academic and industry-oriented students leave the course with the capability to run complex analyses without the need for expensive software. We cover topics related to the general linear model, including regression, analysis of variance (ANOVA), and analysis of covariance (ANCOVA). Students may only take one of the following for degree or certificate credit: PSYC E-1900 (offered previously), STAT E-150, or STAT E-160.
Spring 2025
Advanced Statistics
Alexis Montecinos Bravo PhD, Assistant Professor, Finance, Suffolk University
This course delves into the intricate world of advanced statistics, seamlessly integrating machine learning, artificial intelligence (AI), and programming to equip students with the skills needed for modern data analysis. Students explore sophisticated statistical methods, with a focus on statistical learning, and they learn how to implement these techniques using the programming language Python. The course covers the fundamentals of machine learning, from supervised and unsupervised learning to neural networks, providing students with a solid foundation in AI principles and practices. Through hands-on projects and case studies, participants apply statistical models to real-world data sets, gaining proficiency in data manipulation, visualization, and interpretation. Programming sessions focus on writing efficient code, using statistical libraries, and developing algorithms to solve complex problems in various domains. By the end of the course, students are well-equipped to tackle advanced statistical problems, develop machine learning models, and contribute to AI research and development with strong programming skills.
Prerequisites: For this course, students should have a strong foundation in statistics, including familiarity with probability, hypothesis testing, and basic statistical methods. A basic background in calculus and linear algebra is recommended but not required. Basics in programming, particularly in Python, are recommended but the class starts from scratch. Prior exposure to basic machine learning or artificial intelligence concepts, such as supervised and unsupervised learning algorithms, is also recommended. Additionally, students should possess strong analytical skills, including the ability to formulate and solve mathematical problems.