APPLIED MACHINE LEARNING II
APPLIED MACHINE LEARNING II
- Department: Applied Analytics
- R 8:10 p.m.-10 p.m.
- M 6:10 p.m.-8 p.m.
- W 8:10 p.m.-10 p.m.
- M 8:10 p.m.-10 p.m.
- T 6:10 p.m.-8 p.m.
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Instructor:
Andrew Assing
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Instructor:
Evgeniya Reshetnyak
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Instructor:
Adrian Heilbut
h-index: 13 Info -
Instructor:
Vinay P Mahadeo
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Instructor:
Zachary Kurtz
Info - 3 points
LINEAR REGRESSION MODELS
Prerequisites: STAT GR5203 and GR5204 or the equivalent. Theory and practice of regression analysis, Simple and multiple regression, including testing, estimation, and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. Extensive use of the computer to analyse data.
STAT COMP & INTRO DATA SCIENCE
STAT COMP & INTRO TO DATA SCI
Corequisites: STAT GR5204 and GR5205 or the equivalent. Introduction to programming in the R statistical package: functions, objects, data structures, flow control, input and output, debugging, logical design, and abstraction. Writing code for numerical and graphical statistical analyses. Writing maintainable code and testing, stochastic simulations, paralleizing data analyses, and working with large data sets. Examples from data science will be used for demonstration.
- Department: Statistics
- F 10:10 a.m.-12:40 p.m.
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Instructor:
Benjamin K Goodrich
Info - 3 points
Foundations of Clinical Medicine: Tutori
FNDATION CLINICAL MED: TU
- Department: Medicine
- 0 points
Mediation: Practitioner Development
On a daily basis we may encounter conflicts and seek to resolve them through negotiations and other forms of conflict resolution. Some of these are simple and easy to resolve, while others are complex and may require the support of a third party, or
mediator
. In this course we will explore mediation from several points of view and approaches, as listed below under the session headings. We will explore the theories that underlie the field of mediation as we concentrate on building the skills necessary to practice mediation professionally.
Note: This course qualifies as the prerequisite for an apprenticeship opportunity in anticipation of
mediation certification
through a number of Community Dispute Resolution Centers statewide. This course is also Part 146A approved, which is necessary to qualify for participation on a roster in the New York State Court System.
- Department: Negotiation & Conflict Resolution
- U 9 a.m.-5 p.m.
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Instructor:
Jenny Besch
Info - 3 points
ELEMENTARY STOCHASTIC PROCESSE
Corequisites: GR5203 or the equivalent. Review of elements of probability theory. Poisson processes. Renewal theory. Walds equation. Introduction to discrete and continuous time Markov chains. Applications to queueing theory, inventory models, branching processes.
- Department: Statistics
- MW 10:10 a.m.-11:25 a.m.
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Instructor:
Richard Davis
CULPA: 3 h-index: 60 Info - 3 points
PYTHON FOR DATA ANALYSIS
PYTHON FOR DATA ANALYSIS ONLNE
- Department: Applied Analytics
- T 8:10 p.m.-10 p.m.
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Instructor:
Max Vilenchik
Info - 3 points
COMMUNICATION AND GLOBAL BRANDS
COMMUNICATION & GLOBAL BRANDS
This course examines the discipline of global marketing communication, including the environmental factors that enabled global marketing. The course assesses early models of communication management and the current factors that enable global communication programs: the identification of global target audiences; the kinds of products and services that lend themselves to global communication and those that don’t; and the characteristics of leadership brands that are preeminent in global communication today. Students consider how levels of development and cultural values affect communication programs and how local differences can be reflected in global programs. Message creation and the available methods of message distribution are evaluated in the context of current and future trends. Students learn how to approach strategy and develop an integrated, holistic global communication program and how to manage such a program.
- Department: Strategic Communication
- T 7:10 p.m.-9 p.m.
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Instructor:
Cheryl Fenelle-Dixon
Info - 3 points
CONFLICT RESOLUTION AND DYNAMICAL SYSTEM
CONF RESOLUTION/DYNAMIC S
Dynamical Systems Theory (DST) is a methodology developed in the hard sciences to understand complex systems—from the weather to the functioning of cells, using mathematical algorithms. We added the lens of social-psychological concepts and practices to better understand how to apply DST to conflict. We are now applying DST to conflict analysis and resolution for larger social problems and conflicts that are protracted, deeply embedded and have multiple complex issues. This DST approach goes beyond linear problem-solving and embraces complexity in new ways. Dynamical Systems and Conflict Resolution (NECR 5210) is a required 3-credit course in the Negotiation and Conflict Resolution Program (NECR). Students are expected to spend on average 20 hours per week on this course, including media, group work, readings, and other assignments. NECR 5210 builds on concepts from Understanding Conflict and Cooperation (NECR 5101), where students became familiar with conflict resolution frames, theories, and models, as well as a basic understanding of the DST approach. This course will further develop and advance student understanding and use of advanced DST concepts and tools that will be useful for scholar-practitioners facing situations that require a systemic approach for more highly complex conflicts. It is a complementary approach that rounds out the other concepts and skills student learn in the program. Throughout this course students will work individually and in groups on multiple case studies, to understand and apply DST methodology, while developing an appreciation for the more fluid and non-linear DST approach.
- Department: Negotiation & Conflict Resolution
- USSU 9 a.m.-5 p.m.
- 3 points
SOLID WASTE MANAGEMENT
- Department: Sustainability Management
- R 6:10 p.m.-8 p.m.
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Instructor:
Haralambos Vasiliadis
Info - 3 points
MICROECONOMIC ANALYSIS II
- Department: Economics
- MW 10:10 a.m.-11:25 a.m.
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Instructor:
Guillaume Haeringer
CULPA: 2 Info - 3 points
MACROECONOMCS ANALYSIS II
- Department: Economics
- MW 2:40 p.m.-3:55 p.m.
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Instructor:
Irasema Alonso
CULPA: 12 Info - 3 points
DIRECTING II
- Department: Film
- T 10 a.m.-1 p.m.
- M 2 p.m.-5 p.m.
- W 2 p.m.-5 p.m.
- M 2 p.m.-5 p.m.
- W 9:30 a.m.-12:30 p.m.
- R 6 p.m.-9 p.m.
- R 10 a.m.-1 p.m.
- M 6 p.m.-9 p.m.
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Instructor:
Bogdan G Apetri
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Instructor:
Ian W Olds
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Instructor:
Hilary Brougher
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Instructor:
Stephen Lee
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Instructor:
Anocha Suwichakornpong
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Instructor:
Carlo Mirabella-Davis
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Instructor:
Anocha Suwichakornpong
Wikipedia Info - 3 points
FOUNDATIONS OF SPORTS MANAGEMENT
FOUNDATIONS OF SPORTS MGM
- Department: Sports Management
- M 2:10 p.m.-4 p.m.
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Instructor:
Laurajean Holmgren
Info - 3 points
SUSTAINABLE ENTREPRENEURSHIP
- Department: Sustainability Management
- M 6:10 p.m.-8 p.m.
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Instructor:
Jonathan Hollander
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
- S 10:10 a.m.-12:40 p.m.
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Instructor:
Franz Rembart
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
- R 6:10 p.m.-8 p.m.
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Instructor:
Luke C Apicella
Info - 3 points
NONPARAMETRIC STATISTICS
Prerequisites: STAT GR5205 Statistical inference without parametric model assumption. Hypothesis testing using ranks, permutations, and order statistics. Nonparametric analogs of analysis of variance. Non-parametric regression, smoothing and model selection.
- Department: Statistics
- MW 10:10 a.m.-11:25 a.m.
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Instructor:
Marco A Avella Medina
Info - 3 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
- TR 7:40 p.m.-8:55 p.m.
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Instructor:
Dobrin Marchev
Info - 3 points
CHANGE MANAGEMENT
Change is a necessary and constant part of any organization. The change may be expected, or it may be in reaction to unanticipated external and/or internal factors. In fact, organizations that do not change do not last.
Change initiatives can be exceedingly complex and disorienting, however. The success of a given changeinitiative often rests on the clarity of vision of an organization’s leaders; an accurate and sensitiveunderstanding of the organization’s culture; the involvement, input and buy-in of multiple internal andexternal stakeholders to the change objectives and process; leaders’ ability to leverage technology tocommunicate and drive change; and an organization’s analytical capabilities to document and measureprogress, and continue to iterate and improve.
In light of these requirements, this course seeks to ask: What is the role of the HCM leader in facilitatingchange within an organization? The aims of this course are not abstract. This course will help studentsdevelop skills to support actual organizations (their own and/or others) through change. Lectures, readings,videos, in-class and asynchronous discussions, and assignments will all focus on the practical application ofchange theory and empirical research to real-world organizational contexts.
This course is an advanced elective within the Master of Science in Human Capital Management program.Prerequisites include “HCMPS5100: Introduction to Human Capital Management,” and “HCMPS5150:Integrated Talent Management Strategies.” Some familiarity with people analytics and digital approaches toHuman Capital Management will also be helpful.
- Department: Human Capital Management
- M 8:30 p.m.-10 p.m.
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Instructor:
Solange Charas
h-index: 4 Info - 3 points
Executive Conflict Coaching
The course offers an in-depth exploration of the foundational principles and skills of conflict coaching. This rigorous curriculum is designed to equip students with the expertise necessary to assist themselves and others in cultivating perspectives, mindsets, and strategies that espouse a constructive and collaborative conflict approach.
Inherent to human experience, conflict has outcomes primarily shaped by one's chosen approach. A confrontational stance often results in hampered communication, obstructionism, and a power struggle. Conversely, a collaborative approach accentuates effective communication, amiability, assistance, trust, and coordinated endeavors. This methodology frames conflicting interests not as adversarial positions but as shared challenges necessitating shared resolution. Central to the ethos of this course is the recognition of conflict as a conduit for growth, its potential for constructive engagement, and the imperative for adaptive and visionary leadership embodying executive presence.
Conflicts typically evoke a spectrum of emotions in individuals, making them one of the most demanding challenges a coach might assist clients with. Such moments of contention serve as a litmus test for what renowned executive coach John Mattone terms the "Inner Core" of an astute leader. Properly navigated, conflicts become avenues for leaders to refine their "Inner Core" and bolster their executive maturity.
This course is specifically designed for graduate students who aim to expand their expertise in navigating and mediating conflicts within the workplace. Additionally, it is well-suited for students seeking to enhance their interpersonal skills, develop a more profound understanding of conflict dynamics, and foster a more collaborative and harmonious work environment.
- Department: Negotiation & Conflict Resolution
- S 9 a.m.-5 p.m.
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Instructor:
Aldo Civico
Wikipedia Info - 1.5 points
THE EARTH'S CLIMATE SYSTEM
- Department: Sustainability Management
- T 4:10 p.m.-6 p.m.
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Instructor:
Benjamin I Cook
CULPA: 3 h-index: 56 Info - 3 points
SURVIVAL ANALYSIS
Prerequisites: STAT GR5205 Survival distributions, types of censored data, estimation for various survival models, nonparametric estimation of survival distributions, the proportional hazard and accelerated lifetime models for regression analysis with failure-time data. Extensive use of the computer.
- Department: Statistics
- TR 2:40 p.m.-3:55 p.m.
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Instructor:
Ashley Datta
Info - 3 points
SAMPLE SURVEYS
Prerequisites: STAT GR5204 Introductory course on the design and analysis of sample surveys. How sample surveys are conducted, why the designs are used, how to analyze survey results, and how to derive from first principles the standard results and their generalizations. Examples from public health, social work, opinion polling, and other topics of interest.
- Department: Statistics
- TR 2:40 p.m.-3:55 p.m.
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Instructor:
Rongning Wu
CULPA: 1 Info - 3 points
DIRECTING ACTORS II
- Department: Film
- M 2 p.m.-5 p.m.
- M 6 p.m.-9 p.m.
- R 2 p.m.-5 p.m.
- T 6 p.m.-9 p.m.
- R 6 p.m.-9 p.m.
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Instructor:
Shira-Lee Shalit
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Instructor:
Emil Benjamin
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Instructor:
Jodie Markell
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Instructor:
Lisa Milinazzo
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Instructor:
Abigail Zealey Bess
Info - 3 points
BUSINESS INTELLIGENCE
BUSINESS INTELLIGENCE IN SPRTS
- Department: Sports Management
- M 6:10 p.m.-8 p.m.
- W 6:10 p.m.-8 p.m.
- 3 points
STATISTICAL MACHINE LEARNING
Prerequisites: STAT GR5206 or the equivalent. The course will provide an introduction to Machine Learning and its core models and algorithms. The aim of the course is to provide students of statistics with detailed knowledge of how Machine Learning methods work and how statistical models can be brought to bear in computer systems - not only to analyze large data sets, but to let computers perform tasks that traditional methods of computer science are unable to address. Examples range from speech recognition and text analysis through bioinformatics and medical diagnosis. This course provides a first introduction to the statistical methods and mathematical concepts which make such technologies possible.
- Department: Statistics
- MW 10:10 a.m.-11:25 a.m.
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Instructor:
Genevera Allen
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
- W 6:10 p.m.-8:40 p.m.
- R 6:10 p.m.-8:40 p.m.
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Instructor:
Alex Pijyan
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Instructor:
Haiyuan Wang
Info - 3 points
PURPOSE-DRIVEN COMMUNICATION
- Department: Strategic Communication
- W 5:10 p.m.-7 p.m.
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Instructor:
Hayley Berlent
Info - 3 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 6:10 p.m.-8 p.m.
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Instructor:
Henry A Gordon-Smith
Info - 3 points
Industrial Organization
This is a topics course in industrial organization intended for MA students. The focus of the class is to familiarize students with the way economists in academic, antitrust regulatory and private sector settings approach research questions related to topics such as conduct, pricing, competition or ownership and control in various market structures (e.g., homogenous product, differentiated product, two-sided, vertical markets). The goal of the course is threefold. For each of the market structures considered:
(i)
familiarize you with the foundational economic theories;
(ii)
provide you with the empirical tools you can apply in the future to conduct your own research; and
(iii)
introduce to you key antitrust issues regulators have been focusing on and approaches used in practice to analyze these issues by antitrust economists.
- Department: Economics
- W 6:10 p.m.-8 p.m.
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Instructor:
Ildiko Magyari
Info - 3 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
- W 8:10 p.m.-10 p.m.
- T 8:10 p.m.-10 p.m.
- M 6:10 p.m.-8 p.m.
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Instructor:
Rich Lauria
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Instructor:
Rajat Baijal
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Instructor:
Chris Lee
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Instructor:
Muriel Rachid Campomizzi
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Instructor:
Kerry Gendron
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Instructor:
Anthony Wong
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Instructor:
Violet Diamant
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Instructor:
Melody Feinberg
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Instructor:
Chris Lee
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Instructor:
Juan Echeverri
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Instructor:
R S Forston
Info - 3 points
Open Source Intelligence: Research for C
OSINT: Res for Confl Anal
The elective "Open Source Intelligence: Research for Conflict Analysis"
course is designed to introduce students to key practical insights, tips, and professional skills necessary for any successful conflict resolution practitioner. In this course, students will be required to practically apply some of the tools and techniques of NECR, and appreciate the importance of combining and reformulating the basic NECR concepts in order to serve their exact needs in the field.
Through this course, students have the opportunity to apply what they have learned in the classroom, learn additional practical research skills, and adjust them to their own very specific professional aspirations in the field. This course also helps students strategize their next professional steps in the field in a concise, methodical way.
It is important to keep in mind that the Conflict Resolution field at large is quite diverse, and our students have unique backgrounds and future aspirations. Therefore, this course is customized in coordination with each student during 1-on-1 sessions that take place at the beginning of the semester, in order for each student to be working on something that is clear, and has practical value for his/her very specific professional interests.
As with many things in life, proactiveness, creativity, and an entrepreneurial spirit are keys to success for our very challenging field. Each student will be having a required 1-on-1 session with the instructor, where the instructor will help the student explore ways to creatively strategize their next professional steps as practitioners and also develop the instructions for the final paper that match the needs of the student.
Overall, the goal is to provide students with an enriching, personal experience that helps them rethink their role as practitioners and strategize better their short/ long term goals in Negotiation and Conflict Resolution.
- Department: Negotiation & Conflict Resolution
- U 9 a.m.-1:30 p.m.
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Instructor:
Nikolas Katsimpras
Info - 1.5-3 points
Building a Sustainable Detroit: A Case
Once known as the arsenal of Democracy, the birthplace of the automobile assembly line, and the model city of America, 21st Century Detroit was emblematic of deindustrialization, decay, and insolvency. Following the largest municipal bankruptcy in US history, Detroit is now being reframed in both local and national media as a comeback city with opportunity and possibility for all - urban pioneers, global investors, a creative class of new professionals, and suburbanites seeking a return to urban grit.
Despite these narratives, Detroit remains highly segregated - racially, geographically, economically, and socially. While downtown is prospering, neighborhoods are still largely blighted and contaminated with legacy uses that remain unremediated. Over 30,000 houses and other structures have been demolished in the past 8 years, a process that is under-regulated and contributes to both environmental and infrastructure harm. To the extent new investments are improving the condition of housing and infrastructure in some strategic areas, these investments are displacing long term residents who remain at risk of eviction or foreclosure from their homes. Detroit remains one of the poorest big city in America and the poverty that remains is seemingly intractable. At present, only 36% of residents earn a living wage.
Detroit’s present condition is rooted in a protracted history of racist laws, policies, and practices that deny full citizenship to Black Detroiters, undermine Democracy, and position the city as a poor colony within a thriving metropolis. Racism has disfigured the social, physical and economic landscape of Detroit to produce profound levels of neglect, abuse, and exploitation of its residents, resulting in wealth extraction, housing insecurity, healthy food and water scarcity, educational malpractice, and environmental destruction, all within the framework of wealth attraction, tax incentives, subsidized growth and capital accumulation in the greater downtown.
Through this course, we will examine the thesis that sustainability and racism cannot co-exist; that sustainability is rooted in inclusive social wellbeing now and in future generations, whereas racism is rooted in hoarding of power and resources for one dominant group. This hoarding of resources for a favored population impairs preservation for future generations. Furthermore, environmental racism disconnects the consequences of environmental destruction from its beneficiari
- Department: Sustainability Management
- W 6:10 p.m.-8 p.m.
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Instructor:
Donna Givens Davidson
Info - 3 points
Applied Integrative Seminar for Duals II
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
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Instructor:
Alix Schroder
Info - 0.5 points
DATA ANALYSIS AND VISUALIZATION IN SUSTA
DATA ANALYSIS & VIS IN SU
- Department: Sustainability Management
- T 6:10 p.m.-8 p.m.
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Instructor:
Gregory G Yetman
Info - 3 points
ORGANIZATIONAL STRATEGY AND LEARNING
ORG STRAT AND LEARNING
- Department: Human Capital Management
- M 6:10 p.m.-8 p.m.
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Instructor:
Sara Daly-Padron
Info - 3 points
PROG FOR QUANT & COMP FINANCE
This course covers programming with applications to finance. The applications may include such topics as yield curve building and calibration, short rate models, Libor market models, Monte Carlo simulation, valuation of financial instruments such as options, swaptions and variance swaps, and risk measurement and management, among others. Students will learn about the underlying theory, learn coding techniques, and get hands-on experience in implementing financial models and systems.
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
- S 10:10 a.m.-12:40 p.m.
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Instructor:
Zhiliang Ying
CULPA: 1 Wikipedia h-index: 62 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 4:10 p.m.-5:25 p.m.
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Instructor:
Steven Campbell
Info - 3 points
VISUAL COMMUNICATION & DESIGN
STRATEGIC VISUAL COMM
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
- T 12:10 p.m.-2 p.m.
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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
- TR 6:10 p.m.-7:25 p.m.
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Instructor:
Graeme Baker
Info - 3 points
GLOBAL COMMUNICATION FOR PROFESSIONALS
GLOBAL COMM FOR PROFESSIONALS
The global knowledge economy, cross-border market permeability, and worldwide talent mobility have accelerated the rise of multinational and domestic organizations comprised of individuals from many different cultural and linguistic backgrounds. As these trends strengthen, so, too, does the likelihood that the 21st-century worker will spend a significant part of her/his professional career in a multicultural workplace. While such diversity affords great benefits to organizations, their employees and clients, it is often accompanied by a rise in communication misfires and misunderstandings that can undermine individual, team, and organizational performance.
- Department: Strategic Communication
- MTWRF 9 a.m.-5 p.m.
- R 10:10 a.m.-noon
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Instructor:
Kristine Billmyer
Info - 3 points
DIGITAL HR AND THE DIGITAL WORKPLACE
DIGITAL HR & DIGITAL WORK
- Department: Human Capital Management
- W 6:10 p.m.-8 p.m.
- W 8:30 p.m.-10 p.m.
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Instructor:
Nabeel Ahmad
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Instructor:
Joshua Bellis
Info - 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
- W 8:10 a.m.-10 a.m.
- W 10:10 a.m.-noon
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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 10:10 a.m.-12:40 p.m.
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Instructor:
Gabriel Young
CULPA: 7 Info - 3 points
TOPICS IN MODERN STATISTICS
INTERPTBLE MACHN LEARNING
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
- TR 2:40 p.m.-3:55 p.m.
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Instructor:
Joyce T Robbins
CULPA: 5
Info
- 3 points
TOPICS IN MODERN STATISTICS
Generative AI using Large
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 4:10 p.m.-6:40 p.m.
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Instructor:
Parijat Dube
Info - 3 points
TOPICS IN MODERN STATISTICS
DESIGN&ANALY/ONLINE EXPRM
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
TOPICS IN MODERN STATISTICS
BAYES REGRESSN MULTILEVEL
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
- MW 2:40 p.m.-3:55 p.m.
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Instructor:
Andrew Gelman
CULPA: 6 Wikipedia h-index: 112 Info - 3 points
RESEARCH DESIGN
- Department: Applied Analytics
- T 8:10 a.m.-10 a.m.
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Instructor:
Siddhartha Dalal
Info - 3 points
RESEARCH DESIGN
RESEARCH DESIGN ONLINE
- Department: Applied Analytics
- R 6:10 p.m.-8 p.m.
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Instructor:
David Shilane
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
- M 6:10 p.m.-8 p.m.
- W 8:10 p.m.-10 p.m.
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Instructor:
Harish Dakshina
Info -
Instructor:
Neil Barman
Info - 3 points
QUALITATIVE METHODS & RESEARCH
- Department: Narrative Medicine
- M 6:10 p.m.-9 p.m.
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Instructor:
Carlos Decena
Info - 4 points
SCULPTURE I
- Department: Visual Arts
- F 10 a.m.-4 p.m.
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Instructor:
Jon Kessler
CULPA: 5 Wikipedia Info - 3 points
INVESTMENT PLANNING
The Investment Planning course explores the essential principles of investing and how to apply them wisely as
wealth advisors. Students will examine how investment wisdom and theory has evolved – from the insights of
Benjamin Graham to Modern Portfolio Theory, the Capital Asset Pricing Model, factor-based investing and more --
and identify how these theories can be utilized as a framework for understanding and using investments of the present and future. Students will calculate and apply mathematical formulas to learn how to manage risk and return in investment portfolios. This course will compare and contrast each of the major asset classes, ranging from cash and near-cash investments to public and private equity, debt and alternative investments. Students will learn how to apply investment skills to deliver and demonstrate value to clients, net of fees and adjusted risk. In addition, this course will emphasize the parallel development of investment knowledge and communication and counseling skills to conduct investment relationships with clients effectively.
- Department: Wealth Management
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Instructor:
Stephen Bodurtha
Info - 3 points
Foundations of Valuations
Foundations of valuation is an introductory finance course required for all MBA students. It is designed to cover those areas of finance that are important to all managers, whether they specialize in finance or not. At the end of the course, you will be familiar with the most common financial instruments (stocks, bonds, options) and the methods to value them. More specifically, we will cover the following topics:
1. General framework for valuation (present value formula)
2. Bond and bond valuation (spot rates, yield to maturity, duration, convexity)
3. Stocks (stock valuation, dividend growth model)
4. Basic concepts of risk and return and the CAPM
5. Options (Black-Scholes formula)
The course will be a mix of lectures and cases. Students are expected to come prepared to class since the course relies on several in-class exercises students will solve in excel.
- Department: Finance
- F 12:30 p.m.-3:30 p.m.
- F 3:45 p.m.-6:45 p.m.
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Instructor:
Andrew Hertzberg
h-index: 7 Info - 1.5 points
INTERNATIONAL ENVIRONMENTAL LAW
INTL ENVIRONMENTAL LAW
International Environmental Law is a fascinating field that allows students to consider some of the most important questions of the 21st century – questions that have profound ramifications for the quality of life for our generation as well as future generations. Global environmental problems are real and urgent. Their resolution requires creative and responsible thought and action from many different disciplines.
Sustainability practitioners must understand global environmental issues and their effects on what they are charged to do. At one level, this course will consider the massive challenge of the 21st century: how to alleviate poverty on a global scale and maintain a high quality of life while staying within the bounds of an ecologically limited and fragile biosphere -- the essence of sustainable development. From a more practical perspective, the course will provide students with an understanding of international environmental policy design and the resulting body of law in order to strengthen their ability to understand, interpret and react to future developments in the sustainability management arena.
After grounding in the history and foundational concepts of international environmental law and governance, students will explore competing policy shapers and the relevant law in the areas of stratospheric ozone protection, climate change, chemicals and waste management, and biodiversity. The course satisfies the public policy course requirement for the M.S. in Sustainability Management program.
- Department: Sustainability Management
- W 4:10 p.m.-6 p.m.
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Instructor:
Eran Sthoeger
Info - 3 points
SCULPTURE II
- Department: Visual Arts
- T 10 a.m.-4 p.m.
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Instructor:
Sable Smith
Wikipedia Info - 3 points
Corporate Finance (Half-Term)
Corporate finance is an introductory course required for all MBA students. It is designed to cover those areas of finance that are important to all managers, whether they specialize in finance or not. At the end of the course, you will be able to value a firm. To reach this goal, the course covers the following topics:
1. Introduction to frameworks for firm valuation (enterprise DCF and multiples)
2. Multiple valuations
3. Free cash flows (definition, projections)
4. Residual value
5. Weighted average cost of capital
6. Optimal capital structure
The course will consist of approximately one‐half lecture and one‐half in‐class case discussions, for which students should prepare carefully. The course aims to provide students with an understanding of sound theoretical principles of finance and the practical environment in which financial decisions are made.
- Department: Finance
- FSS 12:30 p.m.-3:30 p.m.
- F 8:30 a.m.-1 p.m.
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Instructor:
Andrew Hertzberg
h-index: 7 Info - 1.5 points
SCULPTURE III
- Department: Visual Arts
- T 10 a.m.-4 p.m.
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Instructor:
Sable Smith
Wikipedia Info - 3 points
DIGITAL WORKPLACE AND DIGITAL TRANSFORMA
DIGITAL WRKPLC & DGTL TRA
How do organizational leaders invest in digital technologies and capabilities to catalyze digital transformation? Moreover, how do corporations and institutions create an effective portfolio of digital investments that are aligned — continuously over time — with the organization’s mission and strategy? This course provides an introduction to digital transformation, and the modern (digital) “place” of work, such as intranets, search appliances, analytic dashboards, enterprise social media, mixed reality, and content management. Feeding the digital workplace are “sources of record,” including Enterprise Resource Planning (ERP), HR systems, Customer Relationship Management (CRM), IoT sensors, and digital marketing. Finally, we look at likely future scenarios for work and how organizations can prepare for digital transformation and beyond.
- Department: Information & Knowledge Strat
- W 6:10 p.m.-8 p.m.
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Instructor:
Kate G Bruce
Info - 3 points
DIGITAL WORKPLACE AND DIGITAL TRANSFORMA
DIGITAL WRKPLC & DGTL TRA
How do organizational leaders invest in digital technologies and capabilities to catalyze digital transformation? Moreover, how do corporations and institutions create an effective portfolio of digital investments that are aligned — continuously over time — with the organization’s mission and strategy? This course provides an introduction to digital transformation, and the modern (digital) “place” of work, such as intranets, search appliances, analytic dashboards, enterprise social media, mixed reality, and content management. Feeding the digital workplace are “sources of record,” including Enterprise Resource Planning (ERP), HR systems, Customer Relationship Management (CRM), IoT sensors, and digital marketing. Finally, we look at likely future scenarios for work and how organizations can prepare for digital transformation and beyond.
- Department: Information & Knowledge Strat
- W 6:10 p.m.-8 p.m.
- 3 points
BUSINESS ANALYTICS
OVERVIEW: Business analytics (BA), in essence, is the discipline of using data analysis - ranging from simple descriptive statistics to advanced, AI-based predictions - to illuminate all quantitative aspects relevant to a specific organization, from its own performance, to the behavior of its customers, and challenges from competitors. This course covers the entire value chain of a BA process, including formulating the question, collecting and managing the relevant data, analyzing said data to answer the question, and finally effectively communicating the results (e.g., data visualization) to stakeholders. While the course teaches some hands-on data analysis/statistics (e.g., database structures, conditional averages, correlations, confidence intervals), the emphasis of the course is on educating users and managers of BA, and as such includes stakeholder engagement and implementation planning.
CONTENT: Following an introduction to the history of BA, weekly lectures and associated assignments (some spreadsheet-based, others in essay format) teach all above elements of the BA value chain one by one. Accompanying readings cover academic foundations and practitioner commentary, from Alan Turing's work (1912-1952) to latest advances in quantum computing. A short individual presentation and a group white paper allow students to combine and hone the various acquired skills in an end-end application. As an overarching objective, upon successful completion of the course, students will be able to devise and "pitch" an innovate BA process to an organization, including strategic recommendations on its business value and implementation.
LOGISTICS: Required course for IKNS students, open to all Columbia University graduate students; no prerequisites other than beginner's familiarity with spreadsheet software and simple statistics (e.g., average, error margin). Online course meets once a week (live via zoom) for the duration of the semester.
- Department: Information & Knowledge Strat
- W 4:10 p.m.-6 p.m.
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Instructor:
Christoph J Meinrenken
h-index: 15 Info - 3 points