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Beth Andrews

• Statistics and Data Science - Associate Professor

Person: Academic

Thomas D Cook

• Sociology - Professor Emeritus
• Human Development and Social Policy PhD Program
• Management and Organizations and Sociology PhD Program
• Sociology PhD Program
• IPR - Institute for Policy Research - Member

Larry Vernon Hedges

• Statistics and Data Science - Professor, Board Of Trustees Professorship
• Medical Social Sciences - Professor
• Psychology - Professor
• School of Education and Social Policy - Professor
• IPR - Institute for Policy Research - Professor
• SILC - Spatial Intelligence and Learning Center - Member

Joel L Horowitz

• Economics - Professor, Morrison Professor
• Economics PhD Program

Hongmei Jiang

• Statistics and Data Science - Professor

Wenxin Jiang

• Computer Science - Professor
• Industrial Engineering and Management Sciences - Orrington Lunt Professor, Professor
• Computer Science PhD Program
• NIMSI - NU Initiative for Manufacturing, Science and Innovation - Member

Miklos Z Racz

• Statistics and Data Science - Assistant Professor
• Computer Science - Assistant Professor

Noelle I Samia

Thomas A Severini

Bruce D Spencer

• Statistics and Data Science - Professor Emeritus

Bradly Stadie

Elizabeth Tipton

Jiping Wang

• Statistics and Data Science - Professor, Chairperson
• Robert H. Lurie Comprehensive Cancer Center - Member

Sandy L Zabell

• Mathematics - Professor
• Mathematics PhD Program
• CIERA - Center for Interdisciplinary Exploration and Research in Astrophysics - Member

Academic Catalog

2023-2024 Edition

Statistics and Data Science

statistics.northwestern.edu

Statistics and Data Science are closely related scientific disciplines that deal with the collection, organization, analysis, interpretation, and reporting of data. As data becomes more abundant and readily accessible, the need for methods and techniques for extracting information from data has greatly increased. The wide range of applications of Statistics and Data Science methods include finance, engineering, medicine, sports, law, and biological, social, and physical sciences. Indeed, it is hard to think of any discipline nowadays that does not call upon the use of statistical methods and approaches.

Statistical methods are widely used in observational studies and for the design and analysis of experiments, sample surveys, and censuses. Such analysis involves diverse fields as clinical trials, political polling, actuarial science, and the design of financial instruments.

Data Science methods are widely used in settings with large amounts of data with a focus on computer analysis, efficiency in terms of both compute time and memory demands, and prediction in aid of decision-making.  Entire new fields based on these methods have sprung up such as deep learning, artificial intelligence, and bioinformatics.

Programs of Study

• Data Science Major
• Data Science Minor
• Statistics Major
• Statistics Minor

STAT 101-7 College Seminar (1 Unit)   Small, writing and discussion-oriented course exploring a specific topic or theme, and introducing skills necessary to thriving at Northwestern. Not eligible to be applied towards a WCAS major or minor except where specifically indicated.

STAT 101-8 First-Year Writing Seminar (1 Unit)   Small, writing and discussion-oriented course exploring a specific topic or theme, and focused on the fundamentals of effective, college-level written communication. Not eligible to be applied towards a WCAS major or minor except where specifically indicated.

STAT 201-0 Introduction to Programming for Data Science (1 Unit)   This course is an introduction programming for Data Science. It will prepare students to use essential programming methods as implemented in either Python or R as a tool in the subsequent data science courses including STAT 301-1 , STAT 301-2 , STAT 301-3 , STAT 303-1 , STAT 303-2 , STAT 303-3 , STAT 304-0 , STAT 305-0 , STAT 362-0 , and STAT 390-0 , etc. Empirical and Deductive Reasoning Foundational Dis Formal Studies Distro Area

STAT 202-0 Introduction to Statistics and Data Science (1 Unit)   Data collection, summarization, correlation, regression, sampling, confidence intervals, tests of significance. Introduction to data analysis techniques using R programming, no prior programming experience required. Does not require calculus and makes minimal use of mathematics. May not receive credit for both STAT 202-0 and STAT 210-0 . Empirical and Deductive Reasoning Foundational Dis Formal Studies Distro Area

STAT 202-SG Peer-Guided Study Group: Introduction to Statistics and Data Science (0 Unit)   Peer-guided study group for students enrolled in STAT 202-0 . Meets weekly in small groups, along with a peer facilitator, to collaboratively review material, work through practice problems, and clarify course concepts. Enrollment optional. Graded S/U.

STAT 210-0 Introduction to Probability and Statistics (1 Unit)   A mathematical introduction to probability theory and statistical methods, including properties of probability distributions, sampling distributions, estimation, confidence intervals, and hypothesis testing. STAT 210-0 is primarily intended for economics majors. May not receive credit for both STAT 202-0 and STAT 210-0 . Prerequisite: strong background in high school algebra (calculus is not required). Empirical and Deductive Reasoning Foundational Dis Formal Studies Distro Area

STAT 210-SG Peer-Guided Study Group: Introduction to Probability and Statistics (0 Unit)   Peer-guided study group for students enrolled in STAT 210-0 . Meets weekly in small groups, along with a peer facilitator, to collaboratively review material, work through practice problems, and clarify course concepts. Enrollment optional. Graded S/U.

STAT 228-0 Series and Multiple Integrals (1 Unit)   Sequences and series, and convergence tests. Power series, Taylor polynomials and error. Double integrals, triple integrals, and change of variables. Students may receive credit for only one of MATH 235‐0, MATH 226‐0, or STAT 228‐0. Prerequisite: MATH 218‐3 or MATH 220‐2, and MATH 228‐1 or MATH 230‐1 or MATH 281‐1 or MATH 285‐2 or MATH 290‐2 or MATH 291‐2 or ES_APPM 252‐1. Empirical and Deductive Reasoning Foundational Dis Formal Studies Distro Area

STAT 232-0 Applied Statistics (1 Unit)   Basic concepts of using statistical models to draw conclusions from experimental and survey data. Topics include simple linear regression, multiple regression, analysis of variance, and analysis of covariance. Practical application of the methods and the interpretation of the results will be emphasized. Prerequisites: STAT 202-0 , STAT 210-0 , or equivalent; MATH 220-1 . Formal Studies Distro Area

STAT 301-1 Data Science 1 with R (1 Unit)  

First course in Data Science, with focus on data management, manipulation, and visualization skills and techniques for exploratory data analysis. The course also introduces the R programming language in the context of Data Science. Students may not receive credit for both this course and STAT 303-1 .

Prerequisite: STAT 202-0 or STAT 210-0 or consent of the instructor.

STAT 301-2 Data Science 2 with R (1 Unit)  

Introduction to supervised machine/statistical learning with a focus on application using R. Course covers essential concepts in machine learning while surveying standard machine learning models such as linear and logistic regression. Course provides a foundation for learning more machine learning methods. Students may not receive credit for both this course and STAT 303-2 .

Prerequisite: STAT 301-1 or consent of instructor.

STAT 301-3 Data Science 3 with R (1 Unit)  

An intermediate course that covers machine learning methods in R, including supervised and unsupervised learning. It provides the knowledge and skills necessary to tackle real world problems with machine learning. Students may not receive credit for both this course and STAT 303-3 .

Prerequisite: STAT 301-2 or consent of the instructor.

STAT 302-0 Data Visualization (1 Unit)  

Introduction to the knowledge, skills, and tools required to visualize data of various formats across statistical domains and to create quality visualizations for both data exploration and presentation.

Prerequisite: STAT 202-0 or equivalent.

STAT 303-1 Data Science 1 with Python (1 Unit)  

First course in Data Science, with focus on data management, manipulation, and visualization skills and techniques for exploratory data analysis. The course also introduces the Python programming language in the context of Data Science. Students may not receive credit for both this course and STAT 301-1 .

STAT 303-2 Data Science 2 with Python (1 Unit)  

Introduction to supervised machine/statistical learning with a focus on application using Python. Course covers essential concepts in machine learning while surveying standard machine learning models such as linear and logistic regression. Course provides a foundation for learning more machine learning methods. Students may not receive credit for both this course and STAT 301-2 .

Prerequisite: STAT 303-1 or consent of the instructor.

STAT 303-3 Data Science 3 with Python (1 Unit)  

An intermediate course that covers machine learning methods in Python, including supervised and unsupervised learning. It provides the knowledge and skills necessary to tackle real world problems with machine learning. Students may not receive credit for both this course and STAT 301-3 .

Prerequisite: STAT 303-2 or consent of the instructor.

STAT 304-0 Data Structures and Algorithms for Data Science (1 Unit)  

This course will introduce students to the design, implementation, analysis, and proper application of abstract data types, data structures, and their algorithms. Python will be used to implement and explore various algorithms and data structures. Students should be prepared for a significant amount of hands-on programming.

Prerequisites: STAT 202-0 or STAT 210-0 or STAT 232-0 , and COMP_SCI 110-0 or COMP_SCI 111-0 .

STAT 305-0 Information Management for Data Science (1 Unit)  

This course aims to give students an extensive data processing and visualization skillset using various Python libraries. It will also focus on relational databases and queries in SQL. Students will learn data scraping from online sources and mobile applications as well as a brief introduction to statistical and predictive analysis after the data is clean and ready to use.

STAT 320-1 Statistical Theory & Methods 1 (1 Unit)  

Sample spaces, computing probabilities, random variables, distribution functions, expected values, variance, correlation, limit theory. May not receive credit for both STAT 320-1 and any of STAT 383-0 , MATH 310-1 , MATH 311-1 , MATH 314-0 , MATH 385-0 , ELEC_ENG 302-0 , or IEMS 202-0. Co-requisites: STAT 202-0 or STAT 210-0 , and STAT 228-0 or MATH 235-0 or both MATH 226-0 and MATH 230-2 .

STAT 320-2 Statistical Theory & Methods 2 (1 Unit)  

Parameter estimation, confidence intervals, hypothesis tests.

Prerequisite: STAT 320-1 or MATH 310-1 .

STAT 320-3 Statistical Theory & Methods 3 (1 Unit)  

Comparison of parameters, goodness-of-fit tests, regression analysis, analysis of variance, and nonparametric methods.

Prerequisites: STAT 320-2 , MATH 240-0 .

STAT 325-0 Survey Sampling (1 Unit)  

Probability sampling, simple random sampling, error estimation, sample size, stratification, systematic sampling, replication methods, ratio and regression estimation, cluster sampling.

Prerequisites: MATH 230-1 and 2 quarters of statistics, or consent of instructor.

STAT 328-0 Causal Inference (1 Unit)  

Introduction to modern statistical thinking about causal inference. Topics include completely randomized experiments, confounding, ignorability of assignment mechanisms, matching, observational studies, noncompliance, and Bayesian methods.

Prerequisites: STAT 320-2 , STAT 350-0 .

STAT 330-1 Applied Statistics for Research 1 (1 Unit)  

First Quarter: Design of experiments and surveys, numerical summaries of data, graphical summaries of data, correlation and regression, probability, sample mean, sample proportion, confidence intervals and tests of significance, one and two sample problems, ANOVA. Second Quarter: Simple linear regression, inference, diagnostics, multiple regression diagnostics, autocorrelation, 1-way ANOVA, power and sample size determination, 2-way ANOVA, ANCOVA, randomized block designs.

STAT 330-2 Applied Statistics for Research 2 (1 Unit)  

Second Quarter: Simple linear regression, inference, diagnostics, multiple regression diagnostics, autocorrelation, 1-way ANOVA, power and sample size determination, 2-way ANOVA, ANCOVA, randomized block designs.

STAT 332-0 Statistics for Life Sciences (1 Unit)   Application of statistical methods and data analysis techniques to the life sciences. Parametric statistics, nonparametric approaches, resampling-based approaches. Prerequisite: 1 introductory statistics course. Formal Studies Distro Area

STAT 342-0 Statistical Data Mining (1 Unit)  

Methods for modeling binary responses with multiple explanatory variables. Potential topics include statistical decision theory, binary regression models, cluster analysis, probabilistic conditional independence, and graphical models.

Prerequisites: courses in probability and statistics comparable to STAT 320-1 , STAT 320-2 ; a course in multiple regression comparable to STAT 350-0 ; familiarity with statistical computing software such as MINITAB or SPSS.

STAT 344-0 Statistical Computing (1 Unit)  

Exploration of theory and practice of computational statistics with emphasis on statistical programming in R.

Prerequisite: STAT 320-2 or equivalent.

STAT 345-0 Statistical Demography (1 Unit)  

Introduction to statistical theory of demographic rates (births, deaths, migration) in multistate setting; statistical models underlying formal demography; analysis of error in demographic forecasting.

Prerequisite: STAT 350-0 , MATH 240-0 , or equivalent.

STAT 348-0 Applied Multivariate Analysis (1 Unit)  

Statistical methods for describing and analyzing multivariate data. Principal component analysis, factor analysis, canonical correlation, clustering. Emphasis on statistical and geometric motivation, practical application, and interpretation of results.

Prerequisites: STAT 320-2 , MATH 240-0 , and STAT 350-0 .

STAT 350-0 Regression Analysis (1 Unit)  

Simple linear regression and correlation, multiple regression, residual analysis, model building, variable selection, multi-collinearity and shrinkage estimation, nonlinear regression. Prerequisite STAT 202-0 or STAT 210-0 or STAT 232-0 or PSYCH 201 or IEMS 201 or IEMS 303. Co-requisite: STAT 320-1 or STAT 383-0 or MATH 310-1 or MATH 311-1 or MATH 314-0 or MATH 385-0 or ELEC_ENG 302-0 or IEMS 202-0.

STAT 351-0 Design and Analysis of Experiments (1 Unit)  

Methods of designing experiments and analyzing data obtained from them: one-way and two-way layouts, incomplete block designs, factorial designs, random effects, split-plot and nested designs.

Prerequisite: STAT 320-1 or equivalent.

STAT 352-0 Nonparametric Statistical Methods (1 Unit)  

Survey of nonparametric methods, with emphasis on understanding their application. Estimation of a distribution function, density estimation, and nonparametric regression.

Prerequisite: STAT 350-0 .

STAT 353-0 Advanced Regression (1 Unit)  

This course covers modern regression methods, including: (1) generalized linear models (binary, categorical, and count data), (2) random effects, mixed effects, and nonlinear models, and (3) model selection. The course emphasizes both the theoretical development of the methods, as well as their application, including the communication of models and results both verbally and in writing.

Prerequisites: STAT 320-2 or 420-2 or MATH 310-2 and a first course in regression is required at the level of STAT 350-0 .

STAT 354-0 Time Series Modeling (1 Unit)  

Introduction to modern time series analysis. Autocorrelation, time series regression and forecasting, ARIMA and GARCH models.

Prerequisites: STAT 320-1 . Corequisite: STAT 350-0 .

STAT 355-0 Analysis of Qualitative Data (1 Unit)  

Introduction to the analysis of qualitative data. Measures of association, loglinear models, logits, and probits.

STAT 356-0 Hierarchical Linear Models (1 Unit)  

Introduction to the theory and application of hierarchical linear models. Two and three level linear models, hierarchical generalized linear models, and application of hierarchical models to organizational research and growth models.

STAT 357-0 Introduction to Bayesian Statistics (1 Unit)  

Introduction to basic concepts and principles in Bayesian inference such as the prior, likelihood, posterior and predictive distributions, as well as an introduction to a variety of computational algorithms for Bayesian inference. Students learn how to develop, describe, implement and critique statistical models from a Bayesian perspective.

Prerequisites: STAT 320-1 , STAT 320-2 , STAT 301-2 or 350-0 , or consent of instructor.

STAT 359-0 Topics in Statistics (1 Unit)  

Topics in theoretical and applied statistics to be chosen by instructor.

Prerequisite: consent of instructor.

STAT 362-0 Advanced Machine Learning for Data Science (1 Unit)   This course aims to focus on the theory and applications of advanced Machine Learning (ML) and Deep Learning (DL) topics. It also includes an introduction to Bayesian Modeling and Reinforcement Learning (RL). The students are expected to have a basic understanding of ML from STAT 301-1-2-3/303-1-2-3. The coding language for the homework projects is Python. Prerequisites: STAT 301-3 or STAT 303-3 . Co-requisite MATH 240-0 . Formal Studies Distro Area

STAT 365-0 Introduction to the Analysis of Financial Data (1 Unit)  

Statistical methods for analyzing financial data. Models for asset returns, portfolio theory, parameter estimation.

Prerequisites: STAT 320-3 , MATH 240-0 .

STAT 370-0 Human Rights Statistics (1 Unit)  

Development, analysis, interpretation, use, and misuse of statistical data and methods for description, evaluation, and political action regarding war, disappearances, justice, violence against women, trafficking, profiling, elections, hunger, refugees, discrimination, etc.

Prerequisites: Two of STAT 325-0 , STAT 350-0 , STAT 320-2 , STAT 320-3 ; or ECON 381-1 , ECON 381-2 ; or MATH 386-1 , MATH 386-2 ; or IEMS 303-0 , IEMS 304-0 .

STAT 383-0 Probability and Statistics for ISP (1 Unit)   Probability and statistics. Ordinarily taken only by students in ISP; permission required otherwise. May not receive credit for both STAT 383-0 and any of STAT 320-1 ; MATH 310-1 , MATH 311-1 , MATH 314-0 , MATH 385-0 ; ELEC_ENG 302-0 ; or IEMS 202-0. Prerequisites: MATH 281-1 , MATH 281-2 , MATH 281-3 ; PHYSICS 125-1 , PHYSICS 125-2 , PHYSICS 125-3 . Formal Studies Distro Area

STAT 390-0 Data Science Project (1 Unit)   An opportunity to develop and create solutions for stakeholders with data needs. Students will work in teams to appropriately scope and solve data problems. Students should expect to spend significant amounts of time coordinating and working with team mates outside of class. Prerequisites: STAT 301-3 or STAT 303-3 or consent of instructor.

STAT 398-0 Undergraduate Seminar (1 Unit)  

STAT 399-0 Independent Study (1-3 Units)   Independent work under the guidance of a faculty member. Consent of department required.

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• Faculty Intranet

DEPARTMENT OF ECONOMICS

• Summer 2021 Newsletter

Ph.D. Admission Statistics

• News & Events

SESP Ranked No. 5 by U.S. News

For the second year in a row, Northwestern University’s School of Education and Social Policy (SESP) has been ranked among the top five graduate schools of education according to the 2024-25 U.S. News and World Report Best Education Graduate Schools rankings.

The School of Education and Social Policy and Vanderbilt University's Peabody College tied for fifth place. Among specialties, SESP's education policy program tied for the No. 11 spot with the University of Virginia, rising from No. 12 the previous year.

“SESP is a place of possibility,” said School of Education and Social Policy Dean Bryan McKinley Jones Brayboy, the Carlos Montezuma Professor. “It is also a place of the now. We will continue to raise new sets of ideas, rooted in empirical research theories of human development, and learning theories, that create the conditions for thriving futures for the peoples, places, and communities that we serve.”

Overall, Columbia University and the University of Wisconsin Madison tied for the top spot. The University of California Los Angeles and the University of Michigan were ranked third and fourth. The rest of the top 10 included: University of Pennsylvania (No. 7), and Harvard University, Johns Hopkins University, New York University, Stanford University, University of Texas Austin and the University of Virginia (tied for No. 8).

The School of Education and Social Policy broke into the top ten US News rankings in 2001-02 and has remained in the top 15 every year since then. Its consistent designation as one of the top schools in the nation reflects an ambitious commitment to interdisciplinary work, research, and innovation.

The School’s three pioneering doctoral programs–learning sciences, human development and social policy, and the joint learning sciences + computer science program–were the first of their kind in the nation and have many imitators.

Our versatile faculty includes experts across an array of disciplines, including social science, natural science, education, psychology, sociology, economics, political science, anthropology, geography, neuroscience, history, and philosophy.

Other faculty highlights include:

• Seventeen National Academy of Education members.
• Thirteen American Educational Research Association Fellows
• Early career superstars. Two-thirds of full professors ages 40-55 are National Academy of Education Members – an award that generally recognizes lifetime achievement.

U.S. News evaluated education schools on research activity, academic excellence of entering students, faculty resources, and opinions on program quality from education school deans and school hiring professionals. Read more about the methodology.

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DEPARTMENT OF STATISTICS AND DATA SCIENCE

• For Current PhD Students

Required Courses for PhD

Required statistics and data science coursework:.

View requirements prior to 2021

The required Statistics and Data Science courses are:

• STAT 344 Statistical Computing
• STAT 350-0 Regression Analysis
• STAT 353-0 Advanced Regression
• STAT 415-0 Introduction to Machine Learning ( was STAT 435-0 Mathematical Foundations of Machine Learning in 2021-2022 )
• STAT 420-1 Introduction to Statistical Theory and Methodology 1
• STAT 420-2 Introduction to Statistical Theory and Methodology 2
• STAT 420-3 Introduction to Statistical Theory and Methodology 3
• STAT 457-0 Applied Bayesian Inference
• At least 4 electives (300- and 400-level graduate courses in Statistics), among which 2 must be 400 level. See STAT courses approved for the PhD coursework below. (STAT graduate level courses excluded for Statistics PhD students: STAT 301-1,2,3, STAT 303-1,2,3, STAT 320-1,2,3, STAT 330-1, and STAT 357) Independent Study registrations cannot be used to fulfill the coursework requirements. 

Additional Required Coursework:

In addition to the 12 courses listed above, PhD students must take:

• STAT 430-1 Probability for Statistical Inference 1 (offered in 2022-23)
• STAT 430-2 Probability for Statistical Inference 2 (offered in 2022-23)
• STAT 440 Stochastic Processes for Statistical Modeling and Inference (offered in 2022-23)

Approved STAT elective courses for PhD:

at least 2 elective courses must be 400 level

• STAT 302 Data Visualization
• STAT 328-0 Causal Inference
• STAT 348-0 Applied Multivariate Analysis
• STAT 351-0 Design Analysis of Experiments
• STAT 352-0 Nonparametric Statistical Methods
• STAT 354-0 Applied Time Series Modeling (currently would register for the STAT 359 section of this course)
• STAT 356-0 Hierarchical Linear Models
• STAT 359-0 Topics in Statistics
• STAT 365-0 Intro Analysis Financial Data
• STAT 439-0 Meta-Analysis
• STAT 455-0 Advanced Qualitative Data Analysis
• STAT 456-0 Generalized Linear Models
• STAT 461-0 Advanced Topics in Statistics
• STAT 465-0 Statistical Methods for Bioinformatics and Computational Biology

STAT 519 Requirement:

All PhD students are required to take STAT 519 Responsible Conduct of Research Training, typically in their second year.

Prior to 2021

The required Statistics courses are:

• STAT 350 Regression Analysis
• STAT 351 Design and Analysis of Experiments or IEMS 463 Statistical Analysis of Designed Experiments (DGS will specify)
• STAT 425 Sampling Theory and Applications
• 6 other 300 and 400 graduate level courses in Statistics to complete the 12 course requirement. Of these six, at least two should be 400 level courses. Independent Study registrations cannot be used to fulfill the coursework requirements. See STAT courses approved for the PhD coursework below.

In addition to the 12 courses listed above, PhD students must take either:

• MATH 450-1 Probability 1 and MATH 450-2 Probability 2
• MATH 450-1 Probability 1 and IEMS 460-1 Stochastic Processes 1 and IEMS 460-2 Stochastic Processes 2
• STAT 344-0 Statistical Computing
• STAT 370-0 Human Rights Statistics

DEPARTMENT OF PHYSICS AND ASTRONOMY

Physics and astronomy welcomes new faculty member.

April 15, 2024

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DEPARTMENT OF ANTHROPOLOGY

Erin waxenbaum elected president of the american board of forensic anthropology.

April 10, 2024

Prof. Erin Waxenbaum is the president elect of the American Board of Forsensic Anthropology She will take office July 1, 2024.

The ABFA is the only approved certification body for forensic anthropologists accredited by The Forensic Specialties Accreditation Board (FSAB). It was incorporated in 1977 as a non-profit organization to serve in the interest of the public and the advancement of science.

https://www.instagram.com/americanboardforensicanthro/

https://www.facebook.com/AmericanBoardofForensicAnthropology/