The Five College Statistics Program was created in 2011 to enable statistics faculty members at the five campuses to coordinate and integrate resources to better serve our statistics and data science students.
“Distinguishing the signal from the noise requires both scientific knowledge and self-knowledge: the serenity to accept the things we cannot predict, the courage to predict the things we can, and the wisdom to know the difference.” - Nate Silver (Five Thirty Eight), The Signal and the Noise: Why So Many Predictions Fail - But Some Don't
"Statisticians now collaborate with the scientists generating the data to develop innovative new theory and methods to tackle problems never envisioned." - Marie Davidian (North Carolina State University)
"Data is the sword of the 21st century, those who wield it well, the Samurai." - Jonathan Rosenberg(former Google Inc. Senior Vice President)
The time to become statistically literate is now. The Five College Statistics Program was created in 2011 to enable statistics faculty members at the five campuses to coordinate and integrate resources to better serve our statistics and data science students.
Whether you want to take an introductory statistics or data science class or pursue elective course offerings, the Five Colleges has courses and programs of study just waiting for you. There are undergraduate majors in statistics and data science at Smith, Mount Holyoke, and Amherst Colleges, undergraduate and graduate programs at the University of Massachusetts, and growth in faculty staffing and enrollments at all of the institutions that make up the Five Colleges.
On this page, you can find resources, including links to statistics courses at each school, statistics faculty on each campus, news and events, announcements and more.
The Five College Statistics Program is committed to fostering closer ties between the faculty members teaching statistics and facilitating additional curricular cooperation to continue the strong statistical presence in the Valley. The Five College Statisticians meet on a regular basis to coordinate activities and curricular offerings.
This course is an intermediate applied statistics course that builds on the statistical data analysis methods introduced in STAT 111 or STAT 135. Students will learn how to pose a statistical question, perform appropriate statistical analysis of the data, and properly interpret and communicate their results. Emphasis will be placed on the use of statistical software, data wrangling, model fitting, and assessment. Topics covered will include ethics, experimental design, resampling approaches, analysis of variance models, multiple regression, model selection, and logistic regression. No prior experience with statistical software is expected
Requisite: STAT 111 or 135. Limited to 24 students. Four spots reserved for incoming first-year students in each Fall section. Fall and Spring semester. Fall Professor Liao, Spring Professor Liao, Professor Matheson
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
4.00
Amy Wagaman
MWF 11:00AM-11:50AM
Amherst College
STAT-230-02-2122F
WEBS 102
awagaman@amherst.edu
This course is an intermediate applied statistics course that builds on the statistical data analysis methods introduced in STAT 111 or STAT 135. Students will learn how to pose a statistical question, perform appropriate statistical analysis of the data, and properly interpret and communicate their results. Emphasis will be placed on the use of statistical software, data wrangling, model fitting, and assessment. Topics covered will include ethics, experimental design, resampling approaches, analysis of variance models, multiple regression, model selection, and logistic regression. No prior experience with statistical software is expected
Requisite: STAT 111 or 135. Limited to 24 students. Four spots reserved for incoming first-year students in each Fall section. Fall and Spring semester. Fall Professor Liao, Spring Professor Liao, Professor Matheson
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Brittney Bailey
TTH 08:30AM-09:50AM
Amherst College
STAT-231-01-2122F
WEBS 102
bebailey@amherst.edu
Computational data analysis is an essential part of modern statistics and data science. This course provides a practical foundation for students to think with data by participating in the entire data analysis cycle. Students will generate statistical questions and then address them through data acquisition, cleaning, transforming, modeling, and interpretation. This course will introduce students to tools for data management, wrangling, and databases that are common in data science and will apply those tools to real-world applications. Students will undertake practical analyses of large, complex, and messy data sets leveraging modern computing tools
Requisite: STAT 111 or STAT 135 and COSC 111 or consent of the instructor. Limited to 24 students. Fall and Spring semesters. The Department.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
4.00
Brittney Bailey
TTH 11:30AM-12:50PM
Amherst College
STAT-231-02-2122F
WEBS 102
bebailey@amherst.edu
Computational data analysis is an essential part of modern statistics and data science. This course provides a practical foundation for students to think with data by participating in the entire data analysis cycle. Students will generate statistical questions and then address them through data acquisition, cleaning, transforming, modeling, and interpretation. This course will introduce students to tools for data management, wrangling, and databases that are common in data science and will apply those tools to real-world applications. Students will undertake practical analyses of large, complex, and messy data sets leveraging modern computing tools
Requisite: STAT 111 or STAT 135 and COSC 111 or consent of the instructor. Limited to 24 students. Fall and Spring semesters. The Department.
Instructor Permission: Permission is required for interchange registration during all registration periods.
01
4.00
Kevin Donges
MWF 01:30PM-02:20PM
Amherst College
STAT-360-01-2122F
SMUD 206
kdonges@amherst.edu
STAT-360-01,MATH-360-01
(Offered as STAT 360 and MATH 360) This course explores the nature of probability and its use in modeling real world phenomena. There are two explicit complementary goals: to explore probability theory and its use in applied settings, and to learn parallel analytic and empirical problem-solving skills. The course begins with the development of an intuitive feel for probabilistic thinking, based on the simple yet subtle idea of counting. It then evolves toward the rigorous study of discrete and continuous probability spaces, independence, conditional probability, expectation, and variance. Distributions covered include the binomial, hypergeometric, Poisson, normal, Gamma, Beta, multinomial, and bivariate normal. Other topics include generating functions, order statistics, and limit theorems.
Requisite: MATH 121 or consent of the instructor. Limited to 24 students. Fall semester. Professor Donges.
Instructor Permission: Permission is required for interchange registration during all registration periods.
02
4.00
Kevin Donges
MWF 03:00PM-03:50PM
Amherst College
STAT-360-02-2122F
SMUD 206
kdonges@amherst.edu
STAT-360-02,MATH-360-02
(Offered as STAT 360 and MATH 360) This course explores the nature of probability and its use in modeling real world phenomena. There are two explicit complementary goals: to explore probability theory and its use in applied settings, and to learn parallel analytic and empirical problem-solving skills. The course begins with the development of an intuitive feel for probabilistic thinking, based on the simple yet subtle idea of counting. It then evolves toward the rigorous study of discrete and continuous probability spaces, independence, conditional probability, expectation, and variance. Distributions covered include the binomial, hypergeometric, Poisson, normal, Gamma, Beta, multinomial, and bivariate normal. Other topics include generating functions, order statistics, and limit theorems.
Requisite: MATH 121 or consent of the instructor. Limited to 24 students. Fall semester. Professor Donges.
Instructor Permission: Permission is required for interchange registration during all registration periods.
01
4.00
Ryan McShane
TTH 01:00PM-02:20PM
Amherst College
STAT-375-01-2122F
SCCE E208
rmcshane@amherst.edu
Competitions, which can include individual and team sports, eSports, tabletop gaming, preference formation, and elections, produce data dependent on interrelated competitors and the decision, league, or tournament format. In this course, students will learn to think about the ways a wide variety of statistical methodologies can be applied to the complex and unique data that emerge through competition, including paired comparisons, decision analysis, rank-based and kernel methods, and spatio-temporal methods. The course will focus on the statistical theory relevant to analyzing data from contests and place an emphasis on simulation and data visualization techniques. Students will develop data collection, wrangling,combination, exploration, analysis, and interpretation skills individually and in groups. Applications may include rating players and teams, assessing shot quality, animating player tracking data, roster construction, comparing alternative voting systems, developing optimal strategies for games, and predicting outcomes. Prior experience with probability such as STAT 360 may be helpful, but is not required.
Requisite: STAT 230 and STAT 231. Limited to 24 students. Fall semester. Professor McShane.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Nicholas Horton
MWF 10:00AM-10:50AM
Amherst College
STAT-495-01-2122F
SMUD 205
nhorton@amherst.edu
Our world is awash in data. To allow decisions to be made based on evidence, there is a need for statisticians to be able to make sense of the data around us and communicate their findings. In this course, students will be exposed to advanced statistical methods and will undertake the analysis and interpretation of complex and real-world datasets that go beyond textbook problems. Course topics will vary from year to year depending on the instructor and selected case studies but will include static and dynamic visualization techniques to summarize and display high dimensional data, advanced topics in design and linear regression, ethics, and selected topics in data mining. Other topics may vary but might include nonparametric analysis, spatial data, and analysis of network data. Through a series of case studies, students will develop the capacity to think and compute with data, undertake and assess analyses, and effectively communicate their results using written and oral presentation.
Requisite: STAT 230, STAT 231, STAT 370, and the computing requirement; or consent of the instructor. Recommended requisite: STAT 231. Limited to 20 students. Fall semester. Professor Wagaman.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
1
4.00
Elizabeth Conlisk
10:30AM-11:50AM TU;10:30AM-11:50AM TH
Hampshire College
334021
Cole Science Center 316;Cole Science Center 316
eacNS@hampshire.edu
This course is an introduction to descriptive and inferential statistics with examples drawn primarily from the fields of medicine, public health, and ecology. The approach is applied and hands-on; students are expected to complete two problem sets each week, collect and analyze data as a class, and design and carry out their own examples of each analysis in four review exercises. We cover description, estimation and hypothesis testing (z-scores, t-tests, chi-square, correlation, regression, and analysis of variance). More advanced techniques such as multi-way ANOVA and multiple regression are noted but not covered in detail. We also discuss the role of statistics in causal inference though the emphasis of the course is on practical applications in design and analysis. The course text is The Basic Practice of Statistics by David S. Moore and the primary software is Minitab. There are no prerequisites and students of all levels and abilities are encouraged to enroll. Key Words: Statistics, Research Desgin, Quantitative Analysis
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Amy Nussbaum
TTH 10:00AM-11:15AM
Mount Holyoke College
115338
Clapp Laboratory 407
anussbau@mtholyoke.edu
In this course, students will learn how to analyze data arising from a broad array of observational and experimental studies. Topics covered will include exploratory graphics, description techniques, the fitting and assessment of statistical models, hypothesis testing, and communication of results. Specific topics may include multiple regression, ANOVA, and non-linear regression. Statistical software will be used.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
4.00
Amy Nussbaum
TTH 11:30AM-12:45PM
Mount Holyoke College
115339
Clapp Laboratory 407
anussbau@mtholyoke.edu
In this course, students will learn how to analyze data arising from a broad array of observational and experimental studies. Topics covered will include exploratory graphics, description techniques, the fitting and assessment of statistical models, hypothesis testing, and communication of results. Specific topics may include multiple regression, ANOVA, and non-linear regression. Statistical software will be used.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
03
4.00
Pramesh Subedi
TTH 10:00AM-11:15AM
Mount Holyoke College
115849
Clapp Laboratory 402
psubedi@mtholyoke.edu
In this course, students will learn how to analyze data arising from a broad array of observational and experimental studies. Topics covered will include exploratory graphics, description techniques, the fitting and assessment of statistical models, hypothesis testing, and communication of results. Specific topics may include multiple regression, ANOVA, and non-linear regression. Statistical software will be used.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Marie Ozanne
TTH 01:45PM-03:00PM
Mount Holyoke College
115627
Clapp Laboratory 402
mozanne@mtholyoke.edu
Infectious disease has plagued humanity since time immemorial. Statistical models serve a critical role in improving understanding of the progression and proliferation of infection in a population, as well as the impact of interventions in stopping the spread of disease. In this course, we will explore regression and compartmental model-based approaches, which will be motivated by some of the most impactful epidemics and pandemics in recent history, including HIV/AIDS, Ebola, Zika, and COVID-19. R statistical software will be used.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
4.00
Marie Ozanne
TTH 08:30AM-09:45AM
Mount Holyoke College
115859
Clapp Laboratory 402
mozanne@mtholyoke.edu
Infectious disease has plagued humanity since time immemorial. Statistical models serve a critical role in improving understanding of the progression and proliferation of infection in a population, as well as the impact of interventions in stopping the spread of disease. In this course, we will explore regression and compartmental model-based approaches, which will be motivated by some of the most impactful epidemics and pandemics in recent history, including HIV/AIDS, Ebola, Zika, and COVID-19. R statistical software will be used.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Laurie Tupper
MWF 01:45PM-03:00PM
Mount Holyoke College
115341
Clapp Laboratory 402
ltupper@mtholyoke.edu
This course includes methods for choosing, fitting, evaluating, and comparing statistical models; introduces statistical inference; and analyzes data sets taken from research projects in the natural, physical, and social sciences.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
4.00
Laurie Tupper
MWF 11:30AM-12:45PM
Mount Holyoke College
115777
Clapp Laboratory 401
ltupper@mtholyoke.edu
This course includes methods for choosing, fitting, evaluating, and comparing statistical models; introduces statistical inference; and analyzes data sets taken from research projects in the natural, physical, and social sciences.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Jordan Crouser
M W 1:20 PM - 2:35 PM
Smith College
CSC-109-01-202201
Stoddard G2
jcrouser@smith.edu
SDS 109-01, CSC 109-01
Offered as SDS 109/CSC 109. The world is growing increasingly reliant on collecting and analyzing information to help people make decisions. Because of this, the ability to communicate effectively about data is an important component of future job prospects across nearly all disciplines. In this course, students learn the foundations of information visualization and sharpen their skills in communicating using data. Throughout the semester, we explore concepts in decision-making, human perception, color theory and storytelling as they apply to data-driven communication. Whether you’re an aspiring data scientist or you just want to learn new ways of presenting information, this course helps you build a strong foundation in how to talk to people about data.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Scott J. LaCombe
TU TH 1:20 PM - 2:35 PM
Smith College
GOV-338-01-202201
Burton 219
slacombe@smith.edu
SDS 338-01, GOV 338-01
How does the behavior of a state, politician, or interest group affect the behavior of others? Does Massachusetts’s decision to legalize recreational marijuana influence Vermont’s marijuana policies? From declarations of war to the decision of who congressmembers will vote with, social scientists are increasingly looking to political networks to recognize the inter-connectedness of the world around us. This course will overview the essentials of social network analysis and how they are applied to give us a better understanding of American politics. Prerequisites: SDS 220 or an equivalent introductory statistics course.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
4.00
Katherine Taylor Halvorsen
TU TH 10:50 AM - 12:05 PM
Smith College
MTH-246-02-202201
Burton 301
khalvors@smith.edu
An introduction to probability, including combinatorial probability, random variables, discrete and continuous distributions. Prerequisites: MTH 153 and MTH 212 (may be taken concurrently), or permission of the instructor.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Jordan Crouser
M W 1:20 PM - 2:35 PM
Smith College
SDS-109-01-202201
Stoddard G2
jcrouser@smith.edu
SDS 109-01, CSC 109-01
Offered as SDS 109/CSC 109. The world is growing increasingly reliant on collecting and analyzing information to help people make decisions. Because of this, the ability to communicate effectively about data is an important component of future job prospects across nearly all disciplines. In this course, students learn the foundations of information visualization and sharpen their skills in communicating using data. Throughout the semester, we explore concepts in decision-making, human perception, color theory and storytelling as they apply to data-driven communication. Whether you’re an aspiring data scientist or you just want to learn new ways of presenting information, this course helps you build a strong foundation in how to talk to people about data.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Albert Y. Kim
M W F 10:50 AM - 12:05 PM
Smith College
SDS-192-01-202201
Stoddard G2
akim04@smith.edu
An introduction to data science using Python, R and SQL. Students learn how to scrape, process and clean data from the web; manipulate data in a variety of formats; contextualize variation in data; construct point and interval estimates using resampling techniques; visualize multidimensional data; design accurate, clear and appropriate data graphics; create data maps and perform basic spatial analysis; and query large relational databases. No prerequisites, but a willingness to write code is necessary.
Instructor Permission: Permission is required for interchange registration during all registration periods.
02
4.00
Albert Y. Kim
M W F 9:25 AM - 10:40 AM
Smith College
SDS-192-02-202201
Sabin-Reed 220
akim04@smith.edu
An introduction to data science using Python, R and SQL. Students learn how to scrape, process and clean data from the web; manipulate data in a variety of formats; contextualize variation in data; construct point and interval estimates using resampling techniques; visualize multidimensional data; design accurate, clear and appropriate data graphics; create data maps and perform basic spatial analysis; and query large relational databases. No prerequisites, but a willingness to write code is necessary.
Instructor Permission: Permission is required for interchange registration during all registration periods.
01
5.00
William Hopper
M W F 9:25 AM - 10:40 AM
Smith College
SDS-201-01-202201
McConnell 404
whopper@smith.edu
(Formerly MTH/SDS 201). An overview of the statistical methods needed for undergraduate research emphasizing methods for data collection, data description and statistical inference including an introduction to study design, confidence intervals, testing hypotheses, analysis of variance and regression analysis. Techniques for analyzing both quantitative and categorical data are discussed. Applications are emphasized, and students use R for data analysis. Classes meet for lecture/discussion and a required laboratory that emphasizes the analysis of real data. This course satisfies the basic requirement for the psychology major. Students who have taken MTH 111 or the equivalent should take SDS 220, which also satisfies the basic requirement. Normally students receive credit for only one of the following introductory statistics courses: SDS 201; PSY 201; ECO 220, GOV 190, SDS 220 or SOC 201.
Instructor Permission: Permission is required for interchange registration during all registration periods.
L01
0.00
William Hopper
TU 9:25 AM - 10:40 AM
Smith College
SDS-201-L01-202201
Sabin-Reed 301
whopper@smith.edu
(Formerly MTH/SDS 201). An overview of the statistical methods needed for undergraduate research emphasizing methods for data collection, data description and statistical inference including an introduction to study design, confidence intervals, testing hypotheses, analysis of variance and regression analysis. Techniques for analyzing both quantitative and categorical data are discussed. Applications are emphasized, and students use R for data analysis. Classes meet for lecture/discussion and a required laboratory that emphasizes the analysis of real data. This course satisfies the basic requirement for the psychology major. Students who have taken MTH 111 or the equivalent should take SDS 220, which also satisfies the basic requirement. Normally students receive credit for only one of the following introductory statistics courses: SDS 201; PSY 201; ECO 220, GOV 190, SDS 220 or SOC 201.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
L02
0.00
William Hopper
TU 10:50 AM - 12:05 PM
Smith College
SDS-201-L02-202201
Sabin-Reed 301
whopper@smith.edu
(Formerly MTH/SDS 201). An overview of the statistical methods needed for undergraduate research emphasizing methods for data collection, data description and statistical inference including an introduction to study design, confidence intervals, testing hypotheses, analysis of variance and regression analysis. Techniques for analyzing both quantitative and categorical data are discussed. Applications are emphasized, and students use R for data analysis. Classes meet for lecture/discussion and a required laboratory that emphasizes the analysis of real data. This course satisfies the basic requirement for the psychology major. Students who have taken MTH 111 or the equivalent should take SDS 220, which also satisfies the basic requirement. Normally students receive credit for only one of the following introductory statistics courses: SDS 201; PSY 201; ECO 220, GOV 190, SDS 220 or SOC 201.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
5.00
Katherine M. Kinnaird
M W F 10:50 AM - 12:05 PM
Smith College
SDS-220-01-202201
Sabin-Reed 301
kkinnaird@smith.edu
(Formerly 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. A wide variety of applications from the natural and social sciences are used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data. SDS 220 satisfies the basic requirement for biological science, engineering, environmental science, neuroscience and psychology. Normally students receive credit for only one of the following introductory statistics courses: SDS 201, PSY 201, GOV 190, ECO 220, SDS 220 or SOC 201. Exceptions may be allowed in special circumstances and require the permission of the adviser and the instructor. Prerequisite: MTH 111 or the equivalent, or permission of the instructor. Lab sections limited to 20.
Instructor Permission: Permission is required for interchange registration during all registration periods.
02
5.00
Fatou Sanogo
M W F 1:20 PM - 2:35 PM
Smith College
SDS-220-02-202201
Sabin-Reed 301
fsanogo@smith.edu
(Formerly 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. A wide variety of applications from the natural and social sciences are used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data. SDS 220 satisfies the basic requirement for biological science, engineering, environmental science, neuroscience and psychology. Normally students receive credit for only one of the following introductory statistics courses: SDS 201, PSY 201, GOV 190, ECO 220, SDS 220 or SOC 201. Exceptions may be allowed in special circumstances and require the permission of the adviser and the instructor. Prerequisite: MTH 111 or the equivalent, or permission of the instructor. Lab sections limited to 20.
Instructor Permission: Permission is required for interchange registration during all registration periods.
L01
0.00
Katherine M. Kinnaird
TH 9:25 AM - 10:40 AM
Smith College
SDS-220-L01-202201
Sabin-Reed 301
kkinnaird@smith.edu
(Formerly 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. A wide variety of applications from the natural and social sciences are used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data. SDS 220 satisfies the basic requirement for biological science, engineering, environmental science, neuroscience and psychology. Normally students receive credit for only one of the following introductory statistics courses: SDS 201, PSY 201, GOV 190, ECO 220, SDS 220 or SOC 201. Exceptions may be allowed in special circumstances and require the permission of the adviser and the instructor. Prerequisite: MTH 111 or the equivalent, or permission of the instructor. Lab sections limited to 20.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
L02
0.00
Katherine M. Kinnaird
TH 10:50 AM - 12:05 PM
Smith College
SDS-220-L02-202201
Sabin-Reed 301
kkinnaird@smith.edu
(Formerly 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. A wide variety of applications from the natural and social sciences are used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data. SDS 220 satisfies the basic requirement for biological science, engineering, environmental science, neuroscience and psychology. Normally students receive credit for only one of the following introductory statistics courses: SDS 201, PSY 201, GOV 190, ECO 220, SDS 220 or SOC 201. Exceptions may be allowed in special circumstances and require the permission of the adviser and the instructor. Prerequisite: MTH 111 or the equivalent, or permission of the instructor. Lab sections limited to 20.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
L03
0.00
Fatou Sanogo
TH 1:20 PM - 2:35 PM
Smith College
SDS-220-L03-202201
Sabin-Reed 301
fsanogo@smith.edu
(Formerly 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. A wide variety of applications from the natural and social sciences are used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data. SDS 220 satisfies the basic requirement for biological science, engineering, environmental science, neuroscience and psychology. Normally students receive credit for only one of the following introductory statistics courses: SDS 201, PSY 201, GOV 190, ECO 220, SDS 220 or SOC 201. Exceptions may be allowed in special circumstances and require the permission of the adviser and the instructor. Prerequisite: MTH 111 or the equivalent, or permission of the instructor. Lab sections limited to 20.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
L04
0.00
Fatou Sanogo
TH 2:45 PM - 4:00 PM
Smith College
SDS-220-L04-202201
Sabin-Reed 301
fsanogo@smith.edu
(Formerly 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. A wide variety of applications from the natural and social sciences are used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data. SDS 220 satisfies the basic requirement for biological science, engineering, environmental science, neuroscience and psychology. Normally students receive credit for only one of the following introductory statistics courses: SDS 201, PSY 201, GOV 190, ECO 220, SDS 220 or SOC 201. Exceptions may be allowed in special circumstances and require the permission of the adviser and the instructor. Prerequisite: MTH 111 or the equivalent, or permission of the instructor. Lab sections limited to 20.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Lindsay Poirier
TU TH 9:25 AM - 10:40 AM
Smith College
SDS-237-01-202201
Seelye 106
lpoirier@smith.edu
This course introduces the theory and practice of data ethnography, demonstrating how qualitative data collection and analysis can be used to study of data settings and artifacts. Students will learn techniques in field-note writing, participant observation, in-depth interviewing, documentary analysis, and archival research and how they may be used to contextualize the cultural underpinnings of datasets. Students will learn how to visualize datasets in ways that foreground their sociopolitical provenance in R. Students will also learn how ethnographic methods can be leveraged to improve data documentation and communication. The course will introduce debates regarding the politics of technoscientific fieldwork. Prerequisites: SDS 192 is recommended.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Randi Garcia
TU TH 10:50 AM - 12:05 PM
Smith College
SDS-290-01-202201
McConnell 103
rgarcia@smith.edu
(Formerly MTH/SDS 290). A survey of statistical methods needed for scientific research, including planning data collection and data analyses that provide evidence about a research hypothesis. The course can include coverage of analyses of variance, interactions, contrasts, multiple comparisons, multiple regression, factor analysis, causal inference for observational and randomized studies and graphical methods for displaying data. Special attention is given to analysis of data from student projects such as theses and special studies. Statistical software is used for data analysis. Prerequisites: One of the following: PSY 201, SDS 201, GOV 190, ECO 220, SDS 220 or a score of 4 or 5 on the AP Statistics examination or the equivalent. Enrollment limited to 35.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
William Hopper
TU TH 1:20 PM - 2:35 PM
Smith College
SDS-291-01-202201
Sabin-Reed 220
whopper@smith.edu
(Formerly MTH/SDS 291). Theory and applications of regression techniques; linear and nonlinear multiple regression models, residual and influence analysis, correlation, covariance analysis, indicator variables and time series analysis. This course includes methods for choosing, fitting, evaluating and comparing statistical models and analyzes data sets taken from the natural, physical and social sciences. Prerequisite: one of the following: SDS 201, PSY 201, GOV 190, SDS 220, ECO 220, or the equivalent or a score of 4 or 5 on the AP Statistics examination. Enrollment limited to 30.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
4.00
Randi Garcia
TU TH 2:45 PM - 4:00 PM
Smith College
SDS-291-02-202201
Sabin-Reed 220
rgarcia@smith.edu
(Formerly MTH/SDS 291). Theory and applications of regression techniques; linear and nonlinear multiple regression models, residual and influence analysis, correlation, covariance analysis, indicator variables and time series analysis. This course includes methods for choosing, fitting, evaluating and comparing statistical models and analyzes data sets taken from the natural, physical and social sciences. Prerequisite: one of the following: SDS 201, PSY 201, GOV 190, SDS 220, ECO 220, or the equivalent or a score of 4 or 5 on the AP Statistics examination. Enrollment limited to 30.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Scott J. LaCombe
TU TH 1:20 PM - 2:35 PM
Smith College
SDS-338-01-202201
Burton 219
slacombe@smith.edu
SDS 338-01, GOV 338-01
How does the behavior of a state, politician, or interest group affect the behavior of others? Does Massachusetts’s decision to legalize recreational marijuana influence Vermont’s marijuana policies? From declarations of war to the decision of who congressmembers will vote with, social scientists are increasingly looking to political networks to recognize the inter-connectedness of the world around us. This course will overview the essentials of social network analysis and how they are applied to give us a better understanding of American politics. Prerequisites: SDS 220 or an equivalent introductory statistics course.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Anna Liu,Krista Gile
TU TH 2:30PM 3:45PM
UMass Amherst
21295
Hasbrouck Laboratory room 130
anna@math.umass.edugile@cns.umass.edu
This course is an introduction to the fundamental principles of statistical science. It does not rely on detailed derivations of mathematical concepts, but does require mathematical sophistication and reasoning. It is an introduction to statistical thinking/reasoning, data management, statistical analysis, and statistical computation. Concepts in this course will be developed in greater mathematical rigor later in the statistical curriculum, including in STAT 515, 516, 525, and 535. It is intended to be the first course in statistics taken by math majors interested in statistics. Concepts covered include point estimation, interval estimation, prediction, testing, and regression, with focus on sampling distributions and the properties of statistical procedures. The course will be taught in a hands-on manner, introducing powerful statistical software used in practical settings and including methods for descriptive statistics, visualization, and data management.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
3.00
Haben Michael
M W 2:30PM 3:45PM
UMass Amherst
22888
Lederle Grad Res Tower Rm 177
habenmichael@umass.edu
This course is an introduction to the fundamental principles of statistical science. It does not rely on detailed derivations of mathematical concepts, but does require mathematical sophistication and reasoning. It is an introduction to statistical thinking/reasoning, data management, statistical analysis, and statistical computation. Concepts in this course will be developed in greater mathematical rigor later in the statistical curriculum, including in STAT 515, 516, 525, and 535. It is intended to be the first course in statistics taken by math majors interested in statistics. Concepts covered include point estimation, interval estimation, prediction, testing, and regression, with focus on sampling distributions and the properties of statistical procedures. The course will be taught in a hands-on manner, introducing powerful statistical software used in practical settings and including methods for descriptive statistics, visualization, and data management.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Joanna Jeneralczuk
TU TH 11:30AM 12:45PM
UMass Amherst
21252
Lederle Grad Res Ctr rm A301
jeneral@math.umass.edu
For graduate and upper-level undergraduate students, with focus on practical aspects of statistical methods.Topics include: data description and display, probability, random variables, random sampling, estimation and hypothesis testing, one and two sample problems, analysis of variance, simple and multiple linear regression, contingency tables. Includes data analysis using a computer package. Prerequisites: high school algebra; junior standing or higher.
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Yueqiao Zhang
M W 2:30PM 3:45PM
UMass Amherst
21254
Lederle Grad Res. Ctr rm A201
yueqiaozhang@umass.edu
First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
3.00
Yalin Rao
M W F 11:15AM 12:05PM
UMass Amherst
21255
Lederle Grad Res. Ctr rm A201
yalin@schoolph.umass.edu
First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
03
3.00
Yalin Rao
M W F 10:10AM 11:00AM
UMass Amherst
21278
Lederle Grad Res. Ctr rm A201
yalin@schoolph.umass.edu
First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
04
3.00
Luc Rey-Bellet
TU TH 10:00AM 11:15AM
UMass Amherst
21281
Hasbrouck Laboratory room 137
luc@math.umass.edu
First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
05
3.00
Brian Van Koten
TU TH 2:30PM 3:45PM
UMass Amherst
21282
Tobin Hall room 204
bvankoten@umass.edu
First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
06
3.00
Jiayu Zhai
M W 4:00PM 5:15PM
UMass Amherst
21283
Hasbrouck Laboratory room 137
jiayuzhai@umass.edu
First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Budhinath Padhy
TU TH 4:00PM 5:15PM
UMass Amherst
21280
Lederle Grad Res. Ctr rm A201
bpadhy@math.umass.edu
Basic ideas of point and interval estimation and hypothesis testing; one and two sample problems, simple linear regression, topics from among one-way analysis of variance, discrete data analysis and nonparametric methods. Prerequisite: Statistc 515 or equivalent.
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
3.00
Haben Michael
M W 4:00PM 5:15PM
UMass Amherst
21285
Lederle Grad Res. Ctr rm A201
habenmichael@umass.edu
Basic ideas of point and interval estimation and hypothesis testing; one and two sample problems, simple linear regression, topics from among one-way analysis of variance, discrete data analysis and nonparametric methods. Prerequisite: Statistc 515 or equivalent.
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
03
3.00
Shixiao Zhang
TU TH 8:30AM 9:45AM
UMass Amherst
21288
Lederle Grad Res. Ctr rm A201
shixiaozhang@umass.edu
Basic ideas of point and interval estimation and hypothesis testing; one and two sample problems, simple linear regression, topics from among one-way analysis of variance, discrete data analysis and nonparametric methods. Prerequisite: Statistc 515 or equivalent.
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
04
3.00
Shixiao Zhang
TU TH 2:30PM 3:45PM
UMass Amherst
21296
Lederle Grad Res. Ctr rm A201
shixiaozhang@umass.edu
Basic ideas of point and interval estimation and hypothesis testing; one and two sample problems, simple linear regression, topics from among one-way analysis of variance, discrete data analysis and nonparametric methods. Prerequisite: Statistc 515 or equivalent.
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Daeyoung Kim
TU TH 8:30AM 9:45AM
UMass Amherst
21253
Integ. Learning Center S231
dkim1@umass.edu
Regression analysis is the most popularly used statistical technique with application in almost every imaginable field. The focus of this course is on a careful understanding and of regression models and associated methods of statistical inference, data analysis, interpretation of results, statistical computation and model building. Topics covered include simple and multiple linear regression; correlation; the use of dummy variables; residuals and diagnostics; model building/variable selection; expressing regression models and methods in matrix form; an introduction to weighted least squares, regression with correlated errors and nonlinear regression. Extensive data analysis using R or SAS (no previous computer experience assumed). Requires prior coursework in Statistics, preferably ST516, and basic matrix algebra. Satisfies the Integrative Experience requirement for BA-Math and BS-Math majors.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
3.00
Budhinath Padhy
TU TH 1:00PM 2:15PM
UMass Amherst
21287
Lederle Grad Res Ctr rm A203
bpadhy@math.umass.edu
Regression analysis is the most popularly used statistical technique with application in almost every imaginable field. The focus of this course is on a careful understanding and of regression models and associated methods of statistical inference, data analysis, interpretation of results, statistical computation and model building. Topics covered include simple and multiple linear regression; correlation; the use of dummy variables; residuals and diagnostics; model building/variable selection; expressing regression models and methods in matrix form; an introduction to weighted least squares, regression with correlated errors and nonlinear regression. Extensive data analysis using R or SAS (no previous computer experience assumed). Requires prior coursework in Statistics, preferably ST516, and basic matrix algebra. Satisfies the Integrative Experience requirement for BA-Math and BS-Math majors.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Patrick Flaherty
TU TH 2:30PM 3:45PM
UMass Amherst
21256
Morrill 1 N 347
pflaherty@umass.edu
This course will introduce computing tools needed for statistical analysis including data acquisition from database, data exploration and analysis, numerical analysis and result presentation. Advanced topics include parallel computing, simulation and optimization, and package creation. The class will be taught in a modern statistical computing language.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
3.00
Shai Gorsky
W 6:00PM 8:30PM
UMass Amherst
21289
Sch of Design@MountIda Rm 105
sgorsky@umass.edu
This course will introduce computing tools needed for statistical analysis including data acquisition from database, data exploration and analysis, numerical analysis and result presentation. Advanced topics include parallel computing, simulation and optimization, and package creation. The class will be taught in a modern statistical computing language.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Krista Gile,Anna Liu
TU 1:00PM 2:15PM
UMass Amherst
21286
Lederle Grad Res Tower Rm 143
gile@cns.umass.eduanna@math.umass.edu
This course provides a forum for training in statistical consulting. Application of statistical methods to real problems, as well as interpersonal and communication aspects of consulting are explored in the consulting environment. Students enrolled in this class will become eligible to conduct consulting projects as consultants in the Statistical Consulting and Collaboration Services group in the Department of Mathematics and Statistics. Consulting projects arising during the semester will be matched to students enrolled in the course according to student background, interests, and availability. Taking on consulting projects is not required, although enrolled students are expected to have interest in consulting at some point. The class will include some presented classroom material; most of the class will be devoted to discussing the status of and issues encountered in students' ongoing consulting projects.
Instructor Permission: Permission is required for interchange registration during all registration periods.
02
3.00
Hyunsun Lee
M 6:00PM 8:30PM
UMass Amherst
21291
Sch of Design@MountIda Rm 105
hyunsunlee@umass.edu
Probability theory, including random variables, independence, laws of large numbers, central limit theorem; statistical models; introduction to point estimation, confidence intervals, and hypothesis testing. Prerequisite: advanced calculus and linear algebra, or consent of instructor.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
John Staudenmayer
M W F 11:15AM 12:05PM
UMass Amherst
22831
Lederle Grad Res Tower Rm 173
jstauden@math.umass.edu
This course will introduce students to Bayesian data analysis, including modeling and computation. We will begin with a description of the components of a Bayesian model and analysis (including the likelihood, prior, posterior, conjugacy and credible intervals). We will then develop Bayesian approaches to models such as regression models, hierarchical models and ANOVA. Computing topics include Markov chain Monte Carlo methods. The course will have students carry out analyses using statistical programming languages and software packages.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Krista Gile
TU TH 10:00AM 11:15AM
UMass Amherst
21227
Hasbrouck Laboratory room 230
gile@cns.umass.edu
Regression is the most widely used statistical technique. In addition to learning about regression methods this course will also reinforce basic statistical concepts and expose students (for many for the first time) to "statistical thinking" in a broader context. This is primarily an applied statistics course. While models and methods are written out carefully with some basic derivations, the primary focus of the course is on the understanding and presentation of regression models and associated methods, data analysis, interpretation of results, statistical computation and model building. Topics covered include simple and multiple linear regression; correlation; the use of dummy variables; residuals and diagnostics; model building/variable selection, regression models and methods in matrix form; an introduction to weighted least squares, regression with correlated errors and nonlinear including binary) regression.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
3.00
Shai Gorsky
TU 6:00PM 8:30PM
UMass Amherst
21290
Sch of Design@MountIda Rm 105
sgorsky@umass.edu
Regression is the most widely used statistical technique. In addition to learning about regression methods this course will also reinforce basic statistical concepts and expose students (for many for the first time) to "statistical thinking" in a broader context. This is primarily an applied statistics course. While models and methods are written out carefully with some basic derivations, the primary focus of the course is on the understanding and presentation of regression models and associated methods, data analysis, interpretation of results, statistical computation and model building. Topics covered include simple and multiple linear regression; correlation; the use of dummy variables; residuals and diagnostics; model building/variable selection, regression models and methods in matrix form; an introduction to weighted least squares, regression with correlated errors and nonlinear including binary) regression.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Erin Conlon
SA 1:00PM 3:30PM
UMass Amherst
21299
Sch of Design@MountIda Rm 105
conlon@math.umass.edu
01
3.00
Zijing Zhang
TH 6:00PM 8:30PM
UMass Amherst
21297
Sch of Design@MountIda Rm 105
zhangzijingapply@gmail.com
Distribution and inference for binomial and multinomial variables with contingency tables, generalized linear models, logistic regression for binary responses, logit models for multiple response categories, loglinear models, inference for matched-pairs and correlated clustered data. Prerequisites: Previous course work in probability and mathematical statistics including knowledge of distribution theory, estimation, confidence intervals, hypothesis testing and multiple linear regression; e.g. Stat 516 and Stat 525 (or equivalent). Prior programming experience.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Patrick Flaherty
TU TH 1:00PM 2:15PM
UMass Amherst
21284
Lederle Grad Res Tower rm 1334
pflaherty@umass.edu
This course is intended as an introductory course in statistical machine learning with emphasis on statistical methodology as it applies to large-scale data applications. At the end of this course, students will be able to build and test a latent variable statistical model with companion inference algorithm to solve real problems in a domain of their interest. Course topics include: introduction to exponential families, sufficiency and conjugacy, graphical model framework and approximate inference methods such as expectation-maximization, variational inference, and sampling-based methods. Additional topics may include: cross-validation, bootstrap, empirical Bayes, and deep learning networks. Graphical model examples will include: naive Bayes, regression, hidden Markov models, principal component, factor analysis, and latent variable/topic models.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Hyunsun Lee
W 6:00PM 8:30PM
UMass Amherst
21298
Sch of Design@MountIda Rm 101
hyunsunlee@umass.edu
This course will cover several workhorse models for analysis of time series data. The course will begin with a thorough and careful review of linear and general linear regression models, with a focus on model selection and uncertainty quantification. Basic time series concepts will then be introduced. Having built a strong foundation to work from, we will delve into several foundational time series models: autoregressive and vector autoregressive models. We will then introduce the state-space modeling framework, which generalizes the foundational time series models and offers greater flexibility. Time series models are especially computationally challenging to work with - throughout the course we will explore and implement the specialized algorithms that make computation feasible in R and/or STAN. Weekly problem sets, two-to-three short exams, and a final project will be required.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
John Staudenmayer
M W F 10:10AM 11:00AM
UMass Amherst
21258
Lederle Grad Res Tower Rm 173
jstauden@math.umass.edu
First semester of two-semester sequence in the theory of linear models. Basic results on the multivariate normal distribution; linear and quadratic forms; noncentral Chi-square and F distributions; inference in linear models, including point and interval estimation, hypothesis testing, etc. Prerequisites: Statistc 607-608 or equivalent; linear algebra.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Theodore Westling
TU TH 2:30PM 3:45PM
UMass Amherst
23015
Hasbrouck Laboratory room 136
twestling@umass.edu
Statistical inference in parametric models is generally well-understood, but parametric assumptions are unrealistic in many settings. Semiparametric and nonparametric models provide more flexible alternatives that may better reflect our knowledge of the problem at hand, but statistical inference in these models is often challenging. In this course, we will introduce the statistical theory and methods underlying targeted inference of Euclidean parameters in semiparametric and nonparametric models. We will begin by discussing aspects of semiparametric efficiency theory. We will then introduce several general-purpose methods of targeted estimation in these models. Finally, we will provide an overview of tools for analyzing the behavior of such estimators, emphasizing the role that modern machine learning methods can play. Throughout the course, we will illustrate these methods using problems from causal inference.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
News
Winners for the Five College Statistics award for 2021 are:
Amherst College: Breanna Richards ’21 and Maria-Cristiana (Kitty) Gîrjău ’21
Hampshire College: Molly Dent and Flynn Hibbs
Mount Holyoke College: Huong (Amelia) Tran '21
Smith College: Yujia (Starry) Zhou '21 and Hannah Snell '21
UMass Statistics: Erica Laider and Ka Wing Cheung
Congratulations to all!
Professor Nick Horton, Beitzel Professor of Technology and Society (Statistics and Data Science), Department of Mathematics and Statistics, Amherst College, has been elected as vice president of the American Statistical Association (ASA). Professor Horton's term begins in January 2021; he will serve with ASA president-elect Dionne Price, who will become the first African-American president of the ASA.
Professor Miles Ott, Assistant Professor of Statistical and Data Sciences is the 2021 LGBTQ+ Educator of the Year. This award is given an educator for significant impact on STEM students "through teaching, advocacy, and role modeling."
Congratulations to Professor Ott on this honor!
The Five Colleges had a strong showing in the Fall 2020 Undergraduate Statistics Project Competition, with winners in the Electronic Undergraduate Statistics Research Conference (eUSR), the Undergraduate Statistics Class Project (USCLAP), and the Undergraduate Statistics Research Project (USRESP).
Congratulations to our Fall 2020 competition winners:
DataFest is a nationally-coordinated undergraduate competition in which teams of up to 5 students work over a weekend to extract insight from a rich and complex data set. The mission of DataFest is to expose undergraduate students to challenging questions with immediate real-world significance that can be addressed through data analysis. Apart from developing data analysis and team building skills, students can win cash prizes, fame, glory, or some combination thereof… and will get a free t-shirt!
The Lorna M. Peterson Award supports scholarly and creative work by undergraduate students taking part in Five College programs. The prize is awarded annually based on nominations from Five College programs.
