Climate Adaptation Discussion

This is the discussion that corresponds with the course CLMT 5015 Climate Change Adaptation. Students are required to register for a discussion section.

• Department: Climate School
• F 1:10 p.m.-2:25 p.m.
• 0 points

Climate Adaptation Discussion

This is the discussion that corresponds with the course CLMT 5015 Climate Change Adaptation. Students are required to register for a discussion section.

• Department: Climate School
• F 1:10 p.m.-2:25 p.m.
• F 2:40 p.m.-3:55 p.m.
• 0 points

Climate Adaptation Discussion

This is the discussion that corresponds with the course CLMT 5015 Climate Change Adaptation. Students are required to register for a discussion section.

• Department: Climate School
• F 2:40 p.m.-3:55 p.m.
• R 8:40 a.m.-9:55 a.m.
• 0 points

Climate Adaptation Discussion

This is the discussion that corresponds with the course CLMT 5015 Climate Change Adaptation. Students are required to register for a discussion section.

• Department: Climate School
• R 8:40 a.m.-9:55 a.m.
• 0 points

MACROECONOMICS ANALYSIS I

• Department: Economics
• R 6:10 p.m.-8 p.m.
• Instructor: Julian Di Giovanni
  Info
• 3 points

ADVANCED MICROECONOMICS

• Department: Economics
• W 12:10 p.m.-2 p.m.
• Instructor: Murat Yilmaz
  Info
• 3 points

ADVANCED MACROECONOMICS

• Department: Economics
• M 4:10 p.m.-6 p.m.
• Instructor: Irasema Alonso
  CULPA: 12 Info
• 3 points

QUANT MTHDS IN INVESTMENT MGMT

Prerequisites: Knowledge of statistics basics and programming skills in any programming language. Surveys the field of quantitative investment strategies from a buy side perspective, through the eyes of portfolio managers, analysts and investors. Financial modeling there often involves avoiding complexity in favor of simplicity and practical compromise. All necessary material scattered in finance, computer science and statistics is combined into a project-based curriculum, which give students hands-on experience to solve real world problems in portfolio management. Students will work with market and historical data to develop and test trading and risk management strategies. Programming projects are required to complete this course.

• Department: Mathematics
• R 7:40 p.m.-10 p.m.
• Instructor: Alberto Botter
  Info
• 3 points

FOUNDATIONS OF SPORTS MANAGEMENT

FOUNDATIONS OF SPORTS MGMT

• Department: Sports Management
• W 12:10 p.m.-2 p.m.
• M 6:10 p.m.-8 p.m.
• W 2:10 p.m.-4 p.m.
• Instructor: Scott Rosner
  Info
• Instructor: Christopher Lencheski
  Info
• Instructor: Sarbjit Singh
  Info
• 3 points

SOCIAL JUSTICE: NARRATIVES OF INEQUITY (

• Department: Narrative Medicine
• Instructor: Courtni K Jeffers
  Info
• 3 points

TIME SERIES ANALYSIS

Prerequisites: STAT GR5205 Least squares smoothing and prediction, linear systems, Fourier analysis, and spectral estimation. Impulse response and transfer function. Fourier series, the fast Fourier transform, autocorrelation function, and spectral density. Univariate Box-Jenkins modeling and forecasting. Emphasis on applications. Examples from the physical sciences, social sciences, and business. Computing is an integral part of the course.

• Department: Statistics
• TR 2:40 p.m.-3:55 p.m.
• Instructor: Rongning Wu
  CULPA: 1 Info
• 3 points

Climate Technology and Innovation

This course is designed to furnish students with a conceptual framework for understanding climate tech innovation and an overview of practical ways to professionally engage in it. We focus on climate tech because the current global rate of decarbonization is not sufficient to limit warming to 1.5°C. To accelerate the rate of change and stabilize our planet’s climate, innovative technology development and diffusion is required. Beyond the moral imperative, rapid decarbonization represents an unprecedented economic opportunity. To realize the promise of a low-carbon economy, new practitioners must join the innovation ecosystem and drive it forward. This course will prepare students to do so.

The course starts by framing what climate tech means (i.e., all technologies focused on mitigating greenhouse gas emissions and addressing the impacts of climate change) and how climate tech innovation will occur (i.e., as a complex process including co-evolution of technology, regulations, infrastructure, and consumer behavior). It then provides an overview of the innovation value chain including various stakeholders and avenues for professional involvement. It concludes with a survey of sectoral innovation opportunities. Considerations of equity and just transition are covered throughout.

• Department: Sustainability Management
• W 6:10 p.m.-8 p.m.
• Instructor: Luke C Apicella
  Info
• 3 points

Climate Technology and Innovation

This course is designed to furnish students with a conceptual framework for understanding climate tech innovation and an overview of practical ways to professionally engage in it. We focus on climate tech because the current global rate of decarbonization is not sufficient to limit warming to 1.5°C. To accelerate the rate of change and stabilize our planet’s climate, innovative technology development and diffusion is required. Beyond the moral imperative, rapid decarbonization represents an unprecedented economic opportunity. To realize the promise of a low-carbon economy, new practitioners must join the innovation ecosystem and drive it forward. This course will prepare students to do so.

The course starts by framing what climate tech means (i.e., all technologies focused on mitigating greenhouse gas emissions and addressing the impacts of climate change) and how climate tech innovation will occur (i.e., as a complex process including co-evolution of technology, regulations, infrastructure, and consumer behavior). It then provides an overview of the innovation value chain including various stakeholders and avenues for professional involvement. It concludes with a survey of sectoral innovation opportunities. Considerations of equity and just transition are covered throughout.

• Department: Auditing
• W 6:10 p.m.-8 p.m.
• Instructor: Luke C Apicella
  Info
• 0 points

Climate Justice Discussion

Discussion for CLMT 5023: Climate Justice: Theory, Practice, and Policy.

• Department: Climate School
• F 1:10 p.m.-2:25 p.m.
• Instructor: Sheila Foster
  Info
• 0 points

Climate Justice Discussion

Discussion for CLMT 5023: Climate Justice: Theory, Practice, and Policy.

• Department: Climate School
• F 1:10 p.m.-2:25 p.m.
• F 2:40 p.m.-3:55 p.m.
• Instructor: Sheila Foster
  Info
• 0 points

Climate Justice Discussion

Discussion for CLMT 5023: Climate Justice: Theory, Practice, and Policy.

• Department: Climate School
• F 2:40 p.m.-3:55 p.m.
• Instructor: Sheila Foster
  Info
• 0 points

BAYESIAN STATISTICS

This course introduces the Bayesian paradigm for statistical inference.  Topics covered include prior and posterior distributions: conjugate priors, informative and non-informative priors; one- and two-sample problems; models for normal data, models for binary data, Bayesian linear models, Bayesian computation: MCMC algorithms, the Gibbs sampler; hierarchical models; hypothesis testing, Bayes factors, model selection; use of statistical software.

 

Prerequisites: A course in the theory of statistical inference, such as STAT GU4204/GR5204 a  course in statistical modeling and data analysis such as STAT GU4205/GR5205.

• Department: Statistics
• MW 6:10 p.m.-7:25 p.m.
• Instructor: Casey Bradshaw
  Info
• 3 points

Sustainability in Sports: Theory and Pra

The global sports industry is substantial, encompassing various aspects such as sporting events, merchandise, broadcasting, and more. In 2024, the industry's revenue amounted to nearly $470 billion. By 2028, the global sports market is expected to surpass $680 billion. By 2027, the global sports market is expected to surpass $623 billion. However, the influence of sports extends far beyond the field. Fans are both dedicated and passionate supporters who contribute to the industry's success and have a massive following across continents. From local matches to international tournaments, fans engage through attendance, viewership, merchandise purchases, social media interactions, and so much more.

As the market continues to grow, the sports industry has made significant progress toward embracing sustainability practices. Brands are increasingly transparent about their sustainability efforts, businesses are looking to partner with sustainability-focused organizations that have reputable certifications and initiatives, real estate developers and investors are designing environmentally friendly facilities, and athletes and their fan bases are demanding climate action, just to name a few. Despite some progress, there's ample room for growth within emerging sustainability practices in sports. Continued innovation can lead to eco-friendly materials, sustainable event management, ensuring sustainability across supply chains, and greening stadiums, venues, and event infrastructure, which can further minimize resource consumption and pollution and contribute to a healthier planet.    

This course introduces the concept of sustainability and its relevance to the sports industry. It examines the environmental, social, and economic impacts of sports activities, events, and organizations and explores the strategies and practices that can enhance the sustainability performance of the sports sector. The course covers topics such as the definitions and dimensions of sustainability and how they relate to sports; the drivers and challenges of sustainability in sports (climate change, stakeholder expectations, governance, and innovation); frameworks and tools for assessing and reporting on sustainability in sports; best practices and case studies of sustainability in sports; and opportunities and benefits of sustainability in sports (fan engagement, athlete activism, business development, and social impact).

 

This course will be structured in the following main se

• Department: Sports Management
• R 6:10 p.m.-8 p.m.
• 3 points

Sustainability in Sports: Theory and Pra

The global sports industry is substantial, encompassing various aspects such as sporting events, merchandise, broadcasting, and more. In 2022, the industry's revenue amounted to nearly $487 billion. By 2027, the global sports market is expected to surpass $623 billion.
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However, the influence of sports extends far beyond the field. Fans are both dedicated and passionate supporters who contribute to the industry's success and have a massive following across continents. From local matches to international tournaments, fans engage through attendance, viewership, merchandise purchases, social media interactions, and so much more.

As the market continues to grow, the sports industry has made significant progress toward embracing sustainability practices. Brands are increasingly transparent about their sustainability efforts, businesses are looking to partner with sustainability-focused organizations that have reputable certifications and initiatives, real estate developers and investors are designing environmentally friendly facilities, and athletes and their fan bases are demanding climate action, just to name a few. Despite some progress, there's ample room for growth within emerging sustainability practices in sports. Continued innovation can lead to eco-friendly materials, sustainable event management, ensuring sustainability across supply chains, and greening stadiums, venues, and event infrastructure, which can further minimize resource consumption and pollution and contribute to a healthier planet.   

This course introduces the concept of sustainability and its relevance to the sports industry. It examines the environmental, social, and economic impacts of sports activities, events, and organizations and explores the strategies and practices that can enhance the sustainability performance of the sports sector. The course covers topics such as the definitions and dimensions of sustainability and how they relate to sports; the drivers and challenges of sustainability in sports (climate change, stakeholder expectations, governance, and innovation); frameworks and tools for assessing and reporting on sustainability in sports; best practices and case studies of sustainability in sports; and opportunities and benefits of sustainability in sports (fan engagement, athlete activism, business development, and social impact).

• Department: Sustainability Management
• R 6:10 p.m.-8 p.m.
• 3 points

DIRECTING ACTORS I

A workshop in which the student explores the craft and vocabulary of the actor through exercises and scene study as actors and the incorporation of the actor's vocabulary in directed scenes. Exploration of script analysis, casting, and the rehearsal process.

• Department: Film
• M 6 p.m.-9 p.m.
• W 2 p.m.-5 p.m.
• T 2 p.m.-5 p.m.
• T 10 a.m.-1 p.m.
• T 6 p.m.-9 p.m.
• Instructor: Emil Benjamin
  Info
• Instructor: Mikael Sodersten
  Info
• Instructor: Jodie Markell
  Wikipedia Info
• Instructor: Keith Manning
  Info
• Instructor: John Rubin
  Info
• Instructor: Abigail Zealey Bess
  Info
• Instructor: Jennifer McCabe
  Info
• Instructor: Eli Pauley
  Info
• 3 points

ADVOCACY & ORGANIZING

Our interpersonal experiences and the personal identities we hold both shape and contribute to our individual concepts of health, as well as to our awareness of the beliefs and identities held by others.  This course examines how various marginalized groups have historically organized and advocated to bring about change in communities impacted by health disparities and social injustice.  How can understanding their stories and the strategies they've implemented to construct, share, and collect their narratives, inform health professionals and their allies in developing new and innovative approaches to hear, interpret, and respond to the needs of the communities they are charged with serving?  At a time when a renewed focus is being placed on health equity, social justice, race, bias, resource distribution, and access, it is imperative to look more closely at the experiences of communities and the individuals within them who have been placed at greater vulnerability.  With an attentiveness to intersectionality, critical race theory, and media studies, course materials will guide an exploration of narrative and its relationship to activism, advocacy, and messaging around community health.

• Department: Narrative Medicine
• R 3:10 p.m.-5 p.m.
• 3 points

ATHLETE ACTIVISM & SOCL JUSTICE IN SPRTS

• Department: Sports Management
• T 2:10 p.m.-4 p.m.
• Instructor: Leonard Elmore
  Info
• 3 points

THE EARTH'S CLIMATE SYSTEM

• Department: Sustainability Management
• T 4:10 p.m.-6 p.m.
• Instructor: Benjamin I Cook
  CULPA: 3 h-index: 56 Info
• 3 points

THE EARTH'S CLIMATE SYSTEM

• Department: Auditing
• T 4:10 p.m.-6 p.m.
• Instructor: Benjamin I Cook
  CULPA: 3 h-index: 56 Info
• 0 points

Causal Inference

This course is an introduction to Causal Inference at the masters level. Students will be introduced to a broad range of causal inference methods including randomized
experiments, observational studies, instrumental variables, di?erence-in-di?erences, regression discontinuity design, and synthetic controls. In addition, the course will cover modern, controversial debates regarding the foundations and limitations of causal inference.

The primary learning goal of this course will be to familiarize students with a variety of the most popular causal inference methods: which causal e?ects they seek to estimate, basic assumptions required for identi?cation and estimation, and their practical implementation. To this end, the course will focus both on developing the pre-requisite statistical / methodological theory and as well as gaining hands-on experience through implementation exercises with real datasets. By the end of the course, students should have deep familiarity of various causal inference methods and—more importantly—be able to determine which method is most appropriate
for a given applied problem and to judge whether the pre-requisite identifying conditions are appropriate.

• Department: Statistics
• T 8:40 a.m.-11:25 a.m.
• Instructor: Christopher Harshaw
  Info
• 3 points

NARRATIVE THERAPY

Whether alone with ourselves, or in close relationships with important people in our lives, dominant narratives shape our encounters by bringing certain aspects of our experience to the fore and marginalizing others. Narrative Therapy is a school of thought developed by Michael White, the Australian psychotherapist and social activist. Influenced by Social Constructionism and the writings of Michel Foucault (among others), White sought to understand the ways in which systems of power and control on the societal level shape our most intimate experiences. There is a price we pay for the hegemony of dominant narratives (as Foucault would say) as other aspects of our experience become marginalized and pushed out of awareness in this process. But by analyzing the dynamics by which certain narratives come to hold sway over us, and by considering what goes missing from our experience, Narrative Therapy seeks to undo this price by re-evaluating the stories we live by so that they can be more expansive and less limiting.

In this course we will look at the basic concepts and theoretical underpinnings of Narrative Therapy, and then begin to understand the essential techniques and areas of application of this important therapeutic school. This course does not train students to practice therapy. Our emphasis instead will be on developing ideas for ways in which the concepts and techniques introduced by Narrative Therapy can inform the practice of Narrative Medicine.

Questions we will address include:
● What can we learn from Narrative Therapy about the ways people structure stories about themselves, and how does this affect their relationship with their bodies, with illness and their conceptions of healing?
● What are the mechanisms by which dominant narratives from the social sphere are integrated into an individual’s self concept, and how does this then influence power relations in the clinical encounter?
● Theorists within Narrative Therapy strive to foster a non-hierarchical, non-expert stance in the clinical encounter. What are the possibilities and the challenges inherent in maintaining this?

• Department: Narrative Medicine
• M 12:10 p.m.-2 p.m.
• Instructor: Michael E Davidovits
  Info
• 3 points

BUSINESS INTELLIGENCE

• Department: Sports Management
• T 6:10 p.m.-8 p.m.
• Instructor: Jordan Rutner
  Info
• 3 points

ADVANCED MACHINE LEARNING

Prerequisites: STAT GR5241 This course covers some advanced topics in machine learning and has an emphasis on applications to real world data. A major part of this course is a course project which consists of an in-class presentation and a written project report.

• Department: Statistics
• W 10:10 a.m.-12:40 p.m.
• R 4:10 p.m.-6 p.m.
• Instructor: Kamiar Rahnama Rad
  Info
• Instructor: Parijat Dube
  Info
• 3 points

APPLIED DATA SCIENCE

Prerequisites: Pre-requisite for this course includes working knowledge in Statistics and Probability, data mining, statistical modeling and machine learning. Prior programming experience in R or Python is required. This course will incorporate knowledge and skills covered in a statistical curriculum with topics and projects in data science. Programming will covered using existing tools in R. Computing best practices will be taught using test-driven development, version control, and collaboration. Students finish the class with a portfolio of projects, and deeper understanding of several core statistical/machine-learning algorithms. Short project cycles throughout the semester provide students extensive hands-on experience with various data-driven applications.

• Department: Statistics
• T 4:10 p.m.-6:40 p.m.
• Instructor: Bianca Dumitrascu
  Info
• 3 points

Unsupervised Learning

Description.
Unsupervised Learning is a masters level course on foundations, methods, practice, and applications in machine learning from data without associated labels or outcomes.  This course will focus on dimension reduction and clustering techniques while also covering graphical models, missing data imputation, anomaly detection, generative models, and others. The course will also emphasize conceptual understanding and practical applications of unsupervised learning in data visualization, exploratory data analysis, data pre-processing, and data-driven discovery.   

 

Prerequisites.

STAT GR 5206 Statistical Computing and Intro to Data Science

STAT GR 5241 Statistical Machine Learning (strongly recommended)

STAT GR 5205 Linear Regression (recommended)

STAT GR 5203 Probability (recommended)

Students should also be familiar with linear algebra.

• Department: Statistics
• W 8:40 a.m.-11:25 a.m.
• Instructor: Genevera Allen
  Info
• 3 points

THE BUSINESS OF NONPROFITS

This Business of Nonprofits course is designed to prepare students to identify, understand, consider, and manage common business and related legal issues arising in the operation of a nonprofit organization. Operational legal issues are pervasive in every aspect of nonprofit management and governance, including: (1) decisions on organizational structures, (2) the design of collaborative relationships, (3) entering into contracts, (4) human resource issues, (5) the creation and use of intellectual property, and (6) the assessment and management of risks. Because of the increasingly complex legal environment nonprofits face, managers knowledgeable about the topics covered in this course will be better equipped to contribute to the structuring of external business arrangements and relationships, as well as to manage internal operational matters. This elective course is intended to provide a solid foundation of practical business and business law basics to managers, board members, and consultants working for nonprofit organizations.

• Department: Non Profit Management
• M 6:30 p.m.-8 p.m.
• Instructor: Astrid Andre
  Info
• 3 points

Python for Deep Learning

This course is an optional companion lab course for GR5242 Advanced Machine Learning.  The aim of this course is to help students acquire the basic computational skills in a python-based Deep Learning library (such as Troch, TensorFlow) to implement deep learning models.  lab class materials will be aligned closely with the topics covered in GR5242.  Google Colab will be used as the main tools for the hands-on lab exercises.

• Department: Statistics
• R 5:35 p.m.-6:35 p.m.
• R 6:45 p.m.-7:45 p.m.
• Instructor: Ka-Yi Ng
  Info
• 1 points

Smart Agriculture for a Changing Climate

Agriculture is highly dependent on stable climate conditions to produce the world’s food with sufficient nutritional quality at an affordable cost. Climate change is threatening the breadbaskets of the world with shifting rainfall, pests, and weather patterns. Farmers face enormous challenges in adapting to this volatility that is affecting their livelihoods and communities locally, and threatens the global food systems stability. Adaptation to these changes has become a high priority for policy makers, corporations, and investors around the world. Climate smart agriculture presents solutions to the existential threat to the global food supply by utilizing a range of tech enabled methods for producing more food with less resources. The challenge is daunting because there is no “one size fits all” solution. Instead, localized solutions that meet the social, environmental, and economic realities of farmers need to be developed, accelerated, and implemented.

• Department: Sustainability Management
• M 4:10 p.m.-6 p.m.
• Instructor: Henry A Gordon-Smith
  Info
• 3 points

Applied Integrative Seminar for Duals

The Applied Integrative Experience for Duals (1 credit) is a year-long, pass/fail program requirement that supports the integrative learning goals of students pursuing dual degrees between the MS in Climate and either the MS in Carbon Management (SEAS) or the MS in Architecture and Urban Design (GSAPP). Students engage in cross-school experiences and guided written reflections to deepen their understanding of how their dual programs intersect and support their professional aspirations.

Students are expected to work closely with their respective faculty advisors throughout the year to identify appropriate events and ensure that their integrative experiences and reflections align with their academic and professional goals. Advisor consultation is essential for shaping meaningful engagement during your time at the Climate School.

• Department: Climate School
• Instructor: Alix Schroder
  Info
• 0.5 points

EXTERNAL STAKEHOLDER REQUIREMENTS

Provides a global review of ERM requirements of regulators, rating agencies, and shareholders. Addresses three industry sectors: (1) insurance; (2) banking; and (3) corporate.

• Department: Enterprise Risk Management
• T 6:10 p.m.-8 p.m.
• W 6:10 p.m.-8 p.m.
• Instructor: Melody Feinberg
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• Instructor: R S Forston
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• 3 points

FUNDAMENTALS OF EDITING

This online class explores the creative and narrative principals of editing through the editing of students' 8-12 minute films and / or other footage. Instructors are professional editors who will provide lecture and individual based instruction.

• Department: Film
• T 6 p.m.-9 p.m.
• T 10 a.m.-1 p.m.
• M 2 p.m.-5 p.m.
• Instructor: Joshua Lee
  Info
• Instructor: Elizabeth Kling
  Info
• 1.5 points

DATA ANALYSIS AND VISUALIZATION IN SUSTA

DATA ANALYSIS & VIS IN SU

• Department: Sustainability Management
• T 6:10 p.m.-8 p.m.
• Instructor: Tavis M Barr
  Info
• 3 points

ORGANIZATIONAL STRATEGY AND LEARNING

ORG STRAT AND LEARNING

• Department: Human Capital Management
• M 8:30 p.m.-10 p.m.
• Instructor: Sara Daly-Padron
  Info
• 3 points

CORPORATE, PRIVATE, COMMUNTY FOUNDATIONS

• Department: Auditing
• R 6:30 p.m.-8 p.m.
• Instructor: John Tyler
  h-index: 3 Info
• 0 points

CORPORATE, PRIVATE, COMMUNTY FOUNDATIONS

• Department: Non Profit Management
• R 6:30 p.m.-8 p.m.
• Instructor: John Tyler
  h-index: 3 Info
• 3 points

Digital Product Development for Sustaina

In April 2022, the Intergovernmental Panel on Climate Change (IPCC) reported that global efforts are unlikely to reduce carbon emissions in line with COP21 targets of 1.5o C above preindustrial levels.  This finding underscores the urgency around decarbonizing the economy and sustainably managing natural resources.  A so-called “big, hairy, audacious goal,” it requires that similarly ambitious solutions be implemented across countries and industries.

It is only by measuring resources that stakeholders can manage them and ensure that they are available in sufficient quantities for future generations. Web tools provide up-to-date analyses of aggregated data; distill complex issues into accessible visualizations; enable users to drill down to answer questions; offer insights into complicated and interdependent issues; and display changes in performance over time.  For example, Sustainable 1/S&P Global’s ESG Scores are valuable because they expose patterns in data related to environmental, social and governance risks and opportunities.

This elective course will introduce students to the digital product management role in the context of sustainability.  Students will get a strong understanding of what it means to be a product manager and its role in the organization.  The course will demonstrate how to define a product vision; identify a product strategy; create product roadmaps; design a customer experience; enable data-driven decisions; understand the development process; manage for results; and, by “leading through influence,” coordinate cross-functional teams of business analysts, developers, data providers, marketing, users, customers, senior management and other stakeholders.  The course is about product strategy and how to innovate and launch new products and features.  Students will be prepared for product management roles in companies; though many of those skills are applicable to entrepreneurship, the course is not geared toward start-ups or new ventures.

• Department: Auditing
• T 6:10 p.m.-8 p.m.
• Instructor: Teri Mendelsohn
  Info
• 0 points

STATISTICAL METHODS IN FINANCE

Prerequisites: STAT GR5204 or the equivalent. STAT GR5205 is recommended. A fast-paced introduction to statistical methods used in quantitative finance. Financial applications and statistical methodologies are intertwined in all lectures. Topics include regression analysis and applications to the Capital Asset Pricing Model and multifactor pricing models, principal components and multivariate analysis, smoothing techniques and estimation of yield curves statistical methods for financial time series, value at risk, term structure models and fixed income research, and estimation and modeling of volatilities. Hands-on experience with financial data.

• Department: Statistics
• F 10:10 a.m.-12:40 p.m.
• Instructor: Hammou El Barmi
  CULPA: 8 Info
• 3 points

STAT INF/TIME-SERIES MODELLING

Available to SSP, SMP Modeling and inference for random processes, from natural sciences to finance and economics. ARMA, ARCH, GARCH and nonlinear models, parameter estimation, prediction and filtering.

• Department: Statistics
• TR 6:10 p.m.-7:25 p.m.
• S 10:10 a.m.-12:40 p.m.
• Instructor: Alberto Gonzalez Sanz
  Info
• Instructor: Franz Rembart
  Info
• 3 points

STOCHASTC PROCSSES-APPLICTNS I

Prerequisites: STAT GR5203 or the equivalent. Basics of continuous-time stochastic processes. Wiener processes. Stochastic integrals. Ito's formula, stochastic calculus. Stochastic exponentials and Girsanov's theorem. Gaussian processes. Stochastic differential equations. Additional topics as time permits.

• Department: Statistics
• MW 1:10 p.m.-2:25 p.m.
• MW 4:10 p.m.-5:25 p.m.
• Instructor: Graeme Baker
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• Instructor: Jaehyuk Choi
  Info
• 3 points

VISUAL COMMUNICATION & DESIGN

This course provides strategic communication students with the foundational notions and methods of design needed to collaborate with designers and amplify their work. It examines the impact technology and social transformations are having on design: the application of digital and generative technology, the redirection toward human-centric approaches, and the discipline’s standing in embracing social and ethical concerns related to ensuring inclusivity and preventing cultural bias. The course begins with a historical overview of design’s evolution and contemporary methods, setting the stage for an in-depth exploration of visual perception principles and key design elements like shape, form, color, typography, imagery, and layout. Students will apply the knowledge gained by experimenting with design practices and developing design strategies and applications through serial hands-on, collaborative assignments and workshops.

• Department: Strategic Communication
• W 8:10 p.m.-10 p.m.
• Instructor: Diego Kolsky
  Info
• 3 points

STOCHASTIC METHODS IN FINANCE

Prerequisites: STAT GR5264 Available to SSP, SMP. Mathematical theory and probabilistic tools for modeling and analyzing security markets are developed. Pricing options in complete and incomplete markets, equivalent martingale measures, utility maximization, term structure of interest rates.

• Department: Statistics
• MW 4:10 p.m.-5:25 p.m.
• Instructor: Steven Campbell
  Info
• 3 points

TRANSFORMING TOTAL REWARDS

• Department: Human Capital Management
• W 6:10 p.m.-8 p.m.
• W 8:30 p.m.-10 p.m.
• Instructor: Solange Charas
  h-index: 4 Info
• Instructor: Edna B Chun
  Info
• 3 points

Navigating conflict: Diverging stakehold

This course will explore ways in which the shifting relationship between the human economy and our physical environment drive divergent, often conflicting, responses from different segments of society, including distinct economic classes, communities, nations, industries, etc. For the sustainability professional, such conflicts are important in the development of equitable solutions. They are also critical pragmatic issues in implementation of any new policies. The relative strength of different stakeholders, and the tactics they deploy to pursue their goals can determine what actually happens “on the ground”. We will take a case study approach, looking at how specific socio-economic impacts of environmental change generate calls for social change, shift alignments, deepen stakeholder entrenchment, and influence sustainability policy. Our cases include impacts of global warming, land-use changes, and expanded material throughputs as a result of growing demand in agriculture, fishing, forestry, mining and manufacturing.

• Department: Sustainability Science
• R 4:10 p.m.-6 p.m.
• Instructor: Robert Newton
  h-index: 9 Info
• 3 points

Navigating conflict: Diverging stakehold

This course will explore ways in which the shifting relationship between the human economy and our physical environment drive divergent, often conflicting, responses from different segments of society, including distinct economic classes, communities, nations, industries, etc. For the sustainability professional, such conflicts are important in the development of equitable solutions. They are also critical pragmatic issues in implementation of any new policies. The relative strength of different stakeholders, and the tactics they deploy to pursue their goals can determine what actually happens “on the ground”. We will take a case study approach, looking at how specific socio-economic impacts of environmental change generate calls for social change, shift alignments, deepen stakeholder entrenchment, and influence sustainability policy. Our cases include impacts of global warming, land-use changes, and expanded material throughputs as a result of growing demand in agriculture, fishing, forestry, mining and manufacturing.

• Department: Auditing
• R 4:10 p.m.-6 p.m.
• Instructor: Robert Newton
  h-index: 9 Info
• 0 points

CAPITAL MARKETS & INVESTMENTS

Risk/return tradeoff, diversification and their role in the modern portfolio theory, their consequences for asset allocation, portfilio optimization. Capitol Asset Pricing Model, Modern Portfolio Theory, Factor Models, Equities Valuation, definition and treatment of futures, options and fixed income securities will be covered.

• Department: Mathematics
• S 7 p.m.-9:20 p.m.
• Instructor: Alexei Chekhlov
  CULPA: 3 h-index: 10 Info
• 3 points

THE NONPROFIT SECTOR: SCOPE & IMPACT

NONPROFIT SECTOR: SCOPE&I

This course introduces students to the roles the nonprofit sector plays in providing for social needs, such as healthcare, education, and basic needs.  Throughout this course, we will also grapple with the ethical questions inherent in these pursuits, including the challenge of tainted money, participatory grantmaking, social impact, and the politicization of nonprofit organizations. The course will also explore distinctions, similarities and relationships among the nonprofit, government, and for-profit sectors. The course examines the parameters of the United States’ nonprofit sector and philanthropic practice, with some opportunity for global comparison.  

The course will require students to utilize and reflect critical and analytical thinking; students will write individual papers, actively participate in discussion both in class and through postings on Canvas and present material to classroom colleagues.  This full-semester course is required the first semester of study.

• Department: Non Profit Management
• M 4:10 p.m.-6 p.m.
• W 6:10 p.m.-8 p.m.
• W 6:30 p.m.-8 p.m.
• W 6:10 p.m.-8 p.m.
• W 6:30 p.m.-8 p.m.
• Instructor: Erwin Vincent S de Leon
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• Instructor: Thomas W Watson
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• Instructor: Jill Lalonde
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• Instructor: Thomas W Watson
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• Instructor: Jill Lalonde
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• 3 points

COMMUNICATION RESEARCH & INSIGHTS

COMM RESEARCH & INSIGHTS

To make informed decisions about communication, we need a clear understanding of our audience and its motivations. We begin by asking the right questions and interpreting the results. This course covers essential market research methods, including quantitative and qualitative techniques. Students gain direct experience in collecting and analyzing data, developing insights and choosing research-driven communication strategies that meet client objectives.

• Department: Strategic Communication
• R 6:10 p.m.-8 p.m.
• Instructor: Laura Milewczik
  Info
• 3 points

ADVANCED DATA ANALYSIS

Course Description

STAT GR5291 Advanced Data Analysis serves as one of the required capstone experiences for MA students in statistics. This course is project-based and covers advanced topics in traditional data analysis. Students are presented with a mix of theory and application in homework assignments.  The final project  is a major contribution to the final grade and is arguably considered the capstone project for the MA in Statistics Program.

Students will learn a myriad of topics related to data analysis and hypothesis testing, and are responsible for application through statistical packages or manual programming. Topics include, exploratory data analysis & descriptive statistics, review of sampling distribution, point estimation, review of hypothesis testing & confidence interval procedures, non-parametric tests, computational methods (Monte Carlo, bootstrap, permutation tests), categorical data analysis, linear regression, diagnostics & residual analysis, robust regression, model selection, non-linear regression & smoothers, aspects of experimental design (ANOVA, two-way ANOVA, blocking, multiple comparisons, ANCOVA, semi-parametric procedures, random effects models, mixed effects models, nested models, repeated measures), and general linear models (logistic regression, penalized logistic, multinomial regression, link functions).

Also, time permitting the class covers:
survival analysis (hazard function, survival curve), time series analysis (stationarity, ACF/PACF, MA, AR, ARMA, ARIMA, order selection, forecasting).

• Department: Statistics
• F 5:10 p.m.-7:40 p.m.
• Instructor: Demissie Alemayehu
  h-index: 14 Info
• 3 points

ADVANCED DATA ANALYSIS

Course Description

STAT GR5291 Advanced Data Analysis serves as one of the required capstone experiences for MA students in statistics. This course is project-based and covers advanced topics in traditional data analysis. Students are presented with a mix of theory and application in homework assignments.  The final project  is a major contribution to the final grade and is arguably considered the capstone project for the MA in Statistics Program.

Students will learn a myriad of topics related to data analysis and hypothesis testing, and are responsible for application through statistical packages or manual programming. Topics include, exploratory data analysis & descriptive statistics, review of sampling distribution, point estimation, review of hypothesis testing & confidence interval procedures, non-parametric tests, computational methods (Monte Carlo, bootstrap, permutation tests), categorical data analysis, linear regression, diagnostics & residual analysis, robust regression, model selection, non-linear regression & smoothers, aspects of experimental design (ANOVA, two-way ANOVA, blocking, multiple comparisons, ANCOVA, semi-parametric procedures, random effects models, mixed effects models, nested models, repeated measures), and general linear models (logistic regression, penalized logistic, multinomial regression, link functions).

Also, time permitting the class covers:
survival analysis (hazard function, survival curve), time series analysis (stationarity, ACF/PACF, MA, AR, ARMA, ARIMA, order selection, forecasting).

• Department: Statistics
• F 5:10 p.m.-7:40 p.m.
• Instructor: Demissie Alemayehu
  h-index: 14 Info
• 3 points

ADVANCED DATA ANALYSIS

Course Description

STAT GR5291 Advanced Data Analysis serves as one of the required capstone experiences for MA students in statistics. This course is project-based and covers advanced topics in traditional data analysis. Students are presented with a mix of theory and application in homework assignments.  The final project  is a major contribution to the final grade and is arguably considered the capstone project for the MA in Statistics Program.

Students will learn a myriad of topics related to data analysis and hypothesis testing, and are responsible for application through statistical packages or manual programming. Topics include, exploratory data analysis & descriptive statistics, review of sampling distribution, point estimation, review of hypothesis testing & confidence interval procedures, non-parametric tests, computational methods (Monte Carlo, bootstrap, permutation tests), categorical data analysis, linear regression, diagnostics & residual analysis, robust regression, model selection, non-linear regression & smoothers, aspects of experimental design (ANOVA, two-way ANOVA, blocking, multiple comparisons, ANCOVA, semi-parametric procedures, random effects models, mixed effects models, nested models, repeated measures), and general linear models (logistic regression, penalized logistic, multinomial regression, link functions).

Also, time permitting the class covers:
survival analysis (hazard function, survival curve), time series analysis (stationarity, ACF/PACF, MA, AR, ARMA, ARIMA, order selection, forecasting).

• Department: Statistics
• F 5:10 p.m.-7:40 p.m.
• Instructor: Demissie Alemayehu
  h-index: 14 Info
• 3 points

ADVANCED DATA ANALYSIS

Course Description

STAT GR5291 Advanced Data Analysis serves as one of the required capstone experiences for MA students in statistics. This course is project-based and covers advanced topics in traditional data analysis. Students are presented with a mix of theory and application in homework assignments.  The final project  is a major contribution to the final grade and is arguably considered the capstone project for the MA in Statistics Program.

Students will learn a myriad of topics related to data analysis and hypothesis testing, and are responsible for application through statistical packages or manual programming. Topics include, exploratory data analysis & descriptive statistics, review of sampling distribution, point estimation, review of hypothesis testing & confidence interval procedures, non-parametric tests, computational methods (Monte Carlo, bootstrap, permutation tests), categorical data analysis, linear regression, diagnostics & residual analysis, robust regression, model selection, non-linear regression & smoothers, aspects of experimental design (ANOVA, two-way ANOVA, blocking, multiple comparisons, ANCOVA, semi-parametric procedures, random effects models, mixed effects models, nested models, repeated measures), and general linear models (logistic regression, penalized logistic, multinomial regression, link functions).

Also, time permitting the class covers:
survival analysis (hazard function, survival curve), time series analysis (stationarity, ACF/PACF, MA, AR, ARMA, ARIMA, order selection, forecasting).

• Department: Statistics
• F 5:10 p.m.-7:40 p.m.
• Instructor: Demissie Alemayehu
  h-index: 14 Info
• 3 points

TOPICS IN MODERN STATISTICS

APP MACH LEARN COMP VIS

Today's data scientist needs to have familiarity with data processing and management tools for processing large volumes of data, so-called Big Data. The class introduces the main principles of database management systems (DBMS) and will provide in depth knowledge in accessing data with SQL.  This class will study techniques and systems for ingesting, efficiently processing, and statistically analyzing large data sets. We will touch on modern technologies developed specifically for Big Data, but also focus on the ways cloud service help to store, access, analyze and model data. Upon completion of the course, you should be able to:

 


Utilize database management systems to parse and explore Big Data

Distribute data processing and statistical summary calculations efficiently

Carry out statistical analysis at different scales using local and distributed computing systems

Use Software Engineering principles to create and share standardized Machine Learning (ML) solutions

• Department: Statistics
• M 1:10 p.m.-3:40 p.m.
• Instructor: Xiaofu He
  h-index: 21 Info
• 3 points

TOPICS IN MODERN STATISTICS

STAT ANALYSIS-NEURAL DATA

Today's data scientist needs to have familiarity with data processing and management tools for processing large volumes of data, so-called Big Data. The class introduces the main principles of database management systems (DBMS) and will provide in depth knowledge in accessing data with SQL.  This class will study techniques and systems for ingesting, efficiently processing, and statistically analyzing large data sets. We will touch on modern technologies developed specifically for Big Data, but also focus on the ways cloud service help to store, access, analyze and model data. Upon completion of the course, you should be able to:

 


Utilize database management systems to parse and explore Big Data

Distribute data processing and statistical summary calculations efficiently

Carry out statistical analysis at different scales using local and distributed computing systems

Use Software Engineering principles to create and share standardized Machine Learning (ML) solutions

• Department: Statistics
• F 10 a.m.-noon
• Instructor: Liam M Paninski
  CULPA: 4 Info
• 3 points

TOPICS IN MODERN STATISTICS

Decode AI w/Info Theory

Today's data scientist needs to have familiarity with data processing and management tools for processing large volumes of data, so-called Big Data. The class introduces the main principles of database management systems (DBMS) and will provide in depth knowledge in accessing data with SQL.  This class will study techniques and systems for ingesting, efficiently processing, and statistically analyzing large data sets. We will touch on modern technologies developed specifically for Big Data, but also focus on the ways cloud service help to store, access, analyze and model data. Upon completion of the course, you should be able to:

 


Utilize database management systems to parse and explore Big Data

Distribute data processing and statistical summary calculations efficiently

Carry out statistical analysis at different scales using local and distributed computing systems

Use Software Engineering principles to create and share standardized Machine Learning (ML) solutions

• Department: Statistics
• M 6:10 p.m.-8:40 p.m.
• Instructor: Ori Shental
  Info
• 3 points

TOPICS IN MODERN STATISTICS

ML Engineering for Stats

Today's data scientist needs to have familiarity with data processing and management tools for processing large volumes of data, so-called Big Data. The class introduces the main principles of database management systems (DBMS) and will provide in depth knowledge in accessing data with SQL.  This class will study techniques and systems for ingesting, efficiently processing, and statistically analyzing large data sets. We will touch on modern technologies developed specifically for Big Data, but also focus on the ways cloud service help to store, access, analyze and model data. Upon completion of the course, you should be able to:

 


Utilize database management systems to parse and explore Big Data

Distribute data processing and statistical summary calculations efficiently

Carry out statistical analysis at different scales using local and distributed computing systems

Use Software Engineering principles to create and share standardized Machine Learning (ML) solutions

• Department: Statistics
• M 6:10 p.m.-8:40 p.m.
• Instructor: Iordan I Slavov
  Info
• 3 points

STRATEGIC COMMUNICATIONS FOR RISK PROFES

STRATEGIC COMM FOR RISK P

Workshop-like course that addresses a variety of communication skills, including listening skills, presentation skills, leadership communications, conflict resolution, management interactions, and professional communication techniques.

• Department: Enterprise Risk Management
• T 8:10 p.m.-10 p.m.
• W 6:10 p.m.-8 p.m.
• W 8:10 p.m.-10 p.m.
• M 6:10 p.m.-8 p.m.
• R 6:10 p.m.-8 p.m.
• W 6:10 p.m.-8 p.m.
• M 6:10 p.m.-8 p.m.
• M 8:10 p.m.-10 p.m.
• R 8:10 p.m.-10 p.m.
• M 8:10 p.m.-10 p.m.
• M 6:10 p.m.-8 p.m.
• W 8:10 p.m.-10 p.m.
• R 2:10 p.m.-4 p.m.
• F 2:10 p.m.-4 p.m.
• W 6:10 p.m.-8 p.m.
• Instructor: Rich Lauria
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• Instructor: Neil Barman
  Info
• 3 points

Corporate Finance

• Department: Finance
• Instructor: Vyacheslav Fos
  Info
• 3 points