Honors Statistical Inference
A rigorous introduction to the theory of statistics. Topics covered include elementary decision theory, distribution of the sample mean, point estimation methods and asymptotic properties, the bias variance tradeoff, exponential families, sufficiency and minimal sufficiency, completeness, Lehmann Scheffe, UMVUE and BLUE, Bayes inference, Neyman-Pearson theory, hypothesis testing, most powerful unbiased tests, likelihood ratio tests, confidence sets.
- Department: Statistics
- TR 3:10 p.m.-5:55 p.m.
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Instructor:
Bodhisattva Sen
h-index: 24 Info - 3 points
Honors Linear Regression Models
Hon Linear Regression Mod
This high-level course in linear regression delves deeply into the theoretical and geometric aspects of regression analysis, offering a comprehensive exploration of its foundational principles and advanced topics. Students will study regression within vector space contexts, emphasizing the role of inner products and orthogonal projections. The analysis of projection matrices will include their properties, such as idempotence and symmetry, and their implications for regression diagnostics and metrics. Students will explore why various test statistics follow t- and F-distributions, with careful attention to degrees of freedom and their derivations. As the course progresses, it will address the complexities of high dimensional regression scenarios.
COMPANY FAILURES
Examination of areas critical to an organization’s success from strategic, operational, financial, and insurance perspectives, and examines why many companies fail in spite of the vast knowledge of factors driving success. Several case studies examined in depth.
- Department: Enterprise Risk Management
- T 6:10 p.m.-8 p.m.
- W 8:10 p.m.-10 p.m.
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Instructor:
Adam H Litke
Info -
Instructor:
Steve Lindo
Info - 3 points
MAFN FIELDWORK
Prerequisites: all 6 MAFN core courses, at least 6 credits of approved electives, and the instructors permission. See the MAFN website for details. This course provides an opportunity for MAFN students to engage in off-campus internships for academic credit that counts towards the degree. Graded by letter grade. Students need to secure an internship and get it approved by the instructor.
- Department: Mathematics
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Instructor:
Jaehyuk Choi
Info - 1-3 points
Archive Photography
When millions of images are made every day, how can a photographer create an original body of work? This class proposes that parsing humanity’s existing shared archive of images is more relevant than generating new images. Following models such as Nepal Picture Library, Magnum Foundation, Drik/Majority World, and Arab Image Foundation, contemporary photography has remapped its practice around the reimagining and explanation, of the archival object. This class explores many archives–family albums, historical photographs, government records, fragile maps, musical albums, and flea market collectibles. We will use a series of lens-based technologies, starting from the flatbed scanner and Photoshop retouching and radiating outward. We will explore archive concerns, including consent, ownership, privacy, circulation, respect, and political impact. Students will explore display forms, including slide shows, zines, books, and exhibitions. There will be a strong complement of reading and writing in this class around the theory and practice of archives from the Western North and Global South.
- Department: Visual Arts
- MW 1 p.m.-3:30 p.m.
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Instructor:
Ben Salesse
Info - 3 points
MATHEMATICS FOR AI
This course equips students with essential mathematical foundations for understanding and working with artificial intelligence (AI) algorithms. After a brief introduction to the historical and social context that numbers arise in, students will learn about:
- Linear Algebra: Matrices, matrix-vector multiplication, linear models, change of basis, dimensionality, spectral decomposition, and principal component analysis (PCA).
- Calculus: Rates of change, derivatives, optimization techniques like gradient descent, with a brief touch upon linear approximation.
- Probability and Statistics: Mathematically deriving complex probability distributions out of simpler ones, mathematically deriving statistical testing methods
- Graph Theory: How graphs are used to find relationships between data as well as being a setting for AI-driven problem solving.
- Problem Solving and Algorithms: Applying mathematical concepts to find problem solutions.
Students will learn about search methods like uninformed search, informed search with the A* algorithm, and greedy algorithms.
- Computational Theory and Automata: Answering questions about what is computable, what is needed in order to compute something, and using this framework to state how much “information” is contained in a mathematical object.
By the end of this course, students will possess a strong mathematical toolkit to confidently tackle the complexities of modern AI algorithms.
- Department: Applied Analytics
- T 6:10 p.m.-8 p.m.
- M 6:10 p.m.-8 p.m.
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Instructor:
Thilan Arachchi Appuhamillage
Info -
Instructor:
Robert Simione
Info - 3 points
RISK REGULATIONS IN FINANCE
This course examines post-financial crisis regulations including Basel III, Fundamental Review of the Trading Book (FRTB), Dodd-Frank Act, Supervision and Regulation Letter 11-7 (SR 11-7), and others. Case studies will explore the technical details of these new rules; and guest lectures from industry experts will bring the material to life. Areas of focus include: model risk management, stress testing, derivatives, and insurance. By the end of this course students will be able to:
Evaluate the purpose and limitations of risk regulations in finance.
Identify and communicate weaknesses in a financial firm.
Communicate with regulators.
Understand Recovery and Resolution Plans or “Living Wills” for a financial firm.
- Department: Enterprise Risk Management
- R 6:10 p.m.-8 p.m.
- T 8:10 p.m.-10 p.m.
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Instructor:
Jean Michel Ngalikpima
Info -
Instructor:
Juan Echeverri
Info - 3 points
Career Development for Quantitative Fina
Career Develop for Quant
This course helps the students understand the job search process and develop the professional skills necessary for career advancement. The students will not only learn the best practices in all aspects of job-seeking but will also have a chance to practice their skills. Each class will be divided into two parts: a lecture and a workshop.
In addition, the students will get support from Teaching Assistants who will be available to guide and prepare the students for technical interviews.
- Department: Mathematics
- MW 10:10 a.m.-11:25 a.m.
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Instructor:
Jaehyuk Choi
Info - 0 points
ESG AND ERM
ESG will be a driving force in risk management in upcoming years. ERM / Risk professionals need a solid understanding of emerging ESG trends and regulations and how they apply to day-to-day job responsibilities. The ESG and ERM course begins with an overview of the ESG landscape and framework. After a foundational understanding is established, the course focuses on incorporating ESG into enterprise risk management, including identification, quantification, decision making, and reporting of ESG-related risks.
- Department: Enterprise Risk Management
- M 6:10 p.m.-8 p.m.
- T 8:10 p.m.-10 p.m.
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Instructor:
Helda Rock
Info -
Instructor:
Tahmina Day
Info - 3 points
ANALYTICS FOR BUSINESS OPERATIONS MANAGE
Operations Management (OM) is responsible for the efficient production and delivery of goods and services, serving as a cornerstone of successful organizations. This course emphasizes how analytical techniques, such as forecasting, queuing theory, and linear programming, provide critical tools for optimizing operational decision-making, improving efficiency, and addressing real-world challenges in operations management. In this course, you will gain essential skills to optimize processes, manage resources, and enhance productivity across various industries. The course will be delivered through a combination of interactive lectures, case studies, and hands-on coding exercises to ensure a balance between conceptual learning and practical application.
Through lectures, you will gain a solid foundation in OM principles and analytical techniques. Case studies will help illustrate real-world applications of OM in industries such as manufacturing, healthcare, retail, and logistics, allowing you to see how the concepts are applied in diverse contexts. This course will integrate the principles of OM with hands-on analytical techniques using Python, allowing you to model and solve real-world OM problems. You will learn to run simulations, perform optimizations, and analyze data to make data-driven decisions that enhance efficiency and overall performance.
OM practices are tailored to meet the specific needs of various sectors. In manufacturing, OM helps streamline production lines and minimize waste; in healthcare, it enhances patient flow and optimizes resource allocation; in retail, it improves inventory management and supply chain operations; and in logistics, it ensures timely deliveries while reducing transportation costs. This course will equip you with the skills to apply OM practices effectively in different industries.
Analytics for Business Operations Management is an elective that is intended for students who are interested in pursuing a career using analytics and operational insights to drive organizational success in a competitive global marketplace across various industries.
- Department: Applied Analytics
- M 8:10 p.m.-10 p.m.
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Instructor:
Andy W Chen
Info - 3 points
DERIVATIVES RISK MANAGEMENT
This course explores financial derivatives across different asset classes with in-depth analysis of several popular trades including block trades, program trades, vanilla options, digital options, and variance swaps. Their dynamics and risks are explored through Monte Carlo simulation using Excel and Python. The daily decisions and tasks of a frontline risk manager are recreated and students have the opportunity to see which trades they would approve or reject. Students will gain a working knowledge of financial derivatives and acquire technical skills to answer complex questions on the trading floor.
- Department: Enterprise Risk Management
- M 6:10 p.m.-8 p.m.
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Instructor:
Alberto Scalari
Info - 3 points
FINANCIAL SEC ANALYSIS AND PORTFOLIO MGT
FIN SEC ANALYSIS AND PORT
Financial securities analysis and portfolio management is the study of analyzing information to evaluate financial securities and design investment strategies. Studying the subject can provide a foundation for students entering the fields of investment analysis or portfolio management. This course provides an intensive introduction to major topics in investments. Part I of the course lays the theoretical foundation by introducing the Portfolio Theory and Equilibrium Asset Pricing models. Part II covers the valuation models and analysis of major asset classes: equity, fixed-income, and derivatives. Topics include bond valuation and interest rate models, equity valuation and financial statement analysis, options valuation, other derivatives, and risk management. Part III of the course focuses on the practice of active portfolio management.
TOOLS FOR RISK MANAGEMENT
Tools for Risk Management examines how risk technology platforms assess risks. These platforms gather, store, and analyze data; and transform that data to actionable information. This course explores how the platforms are implemented, customized, and evaluated. Topics include business requirements specification, data modeling, risk analytics and reporting, systems integration, regulatory issues, visualization, and change processes. Hands-on exercises using selected vendor tools will give students the opportunity to see what these tools can offer.
- Department: Enterprise Risk Management
- W 6:10 p.m.-8 p.m.
- R 8:10 p.m.-10 p.m.
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Instructor:
Barney Baldwin
Info -
Instructor:
Viktor Ziskin
Info - 3 points
MODEL RISK MANAGEMENT
Given the ever growing reliance on models, Model risk affects financial institutions at almost every level of their organization including pricing, risk, finance, and marketing. Model risk management (MRM) is now one of the primary focuses of operational risk management at modern financial institutions. In this class, the ERM skill sets of risk identification, risk quantification, and risk decision making are applied to the kinds of models seen in large, complex financial institutions. Through readings, lecture, assignments, and in-class discussions, students learn the principles and concepts that a robust MRM function uses to manage model risk.
- Department: Enterprise Risk Management
- T 8:10 p.m.-10 p.m.
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Instructor:
Peter L Russo
Info - 3 points
SPORTS SPONSORSHIP & SALES
- Department: Sports Management
- M 6:10 p.m.-8 p.m.
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Instructor:
Ishwara Glassman Chrein
Info - 3 points
Fieldwork/Internship Course in African A
Fieldwork/Internship AFAM
Course Description
This course provides an opportunity for students in the AAADS Master’s program to engage in off-campus internships for academic credit that will count towards their requirements for the degree.
Open only to AAADS Master’s students and only with prior written approval of the Director of Graduate Studies.
Prerequisites: All core courses
and
at least 6 credits of approved electives AAADS Master’s program. This course is not open to students in their first two semesters.
Grading: Letter grade. This course may not be taken P/F or for R credit.
Credits: 1 (variable credit, to be determined by the department). The student is expected to do at least 70 hours of work total, under the condition that the student may not work more than 20 hours in any one week; during the summer term, in which case it can be full time).
The course may be repeated in a following semester or semesters for a maximum total of 3 credits. The course may not be taken more than once per semester.
The student must have received a grade of at least A (as posted in SSOL) for all previous internship courses. Should the student repeat AFAM GR5555, this rule will apply to the original internship course and all subsequent internship courses.
Students who have already completed all requirements for the degree may not take AFAM GR5555.
- Department: African American and African Diaspora
- 1 points
Fieldwork Course in Modern Asia-Pacific
Fieldwork Mod Asia-Pacifi
This course provides an opportunity for students in the MA in Regional Studies: East Asia (MARSEA) Program to engage in off-campus internships for academic credit that will count towards their requirements for the degree. Open only to MARSEA students and only with prior written approval of the program director or academic director. Prerequisites: Both semesters of the required MARSEA Core Seminar. This course is not open to students in their first two semesters.
To apply for EARS GR5555, tsubmit a complete application and an official offer letter from the internship sponsor to the MARSEA program office as early as possible (must be submitted at least 21 days before the start of the internship). The application should include:
• Student’s full name and UNI
• Name of organization sponsoring or employing the student; department within the organization (if applicable); and address
• Expected start and end dates of internship
• Number of credits requested. Students should be aware that the application may be approved for a lower number of credits than requested.
• Number of hours of work per week
•Name, title, phone number, and email address for the student’s supervisor in the organization or the organization’s Human Resources office (HR)
• An official letter from the employer/internship sponsor offering employment. It must include the following information: Beginning and ending dates of employment, number of hours of work per week, a description of appropriate duties, location of employment
• All international students in F-1 status applying for EARS GR5555 are required to submit an application for CPT.
- Department: East Asian Regional Studies
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Instructor:
Sau-Yi Fong
Info - 1 points
MACHINE LEARNING FOR RISK MANAGEMENT
The exponentially increasing availability of data and the rapid development of information technology and computing power have inevitably made Machine Learning part of the risk manager’s toolkit. But, what are these tools? This class provides the driving intuitions for machine learning. Students will see how many of the algorithms are extensions of what we already do with our human minds. These algorithms include regularized regression, cluster analysis, naive bayes, apriori algorithm, decision trees, random forests, and boosted ensembles.
Through practical and real-life applications of ML to Risk Management, students will learn to identify the best technique to apply to a particular risk management problem, from credit risk measurement, fraud detection, portfolio selection to climate change, and ESG applications.
- Department: Enterprise Risk Management
- M 6:10 p.m.-8 p.m.
- 3 points
Oral History & Justice: Memory, Power, a
HUM RGTS & ORAL HIST
This course will explore the ethics and politics of using oral history methods for documenting injustice, oppression, and human rights issues. The course is open to graduate students of oral history, human rights, journalism, and related fields; no prior experience with oral history interviewing is required. Oral history can be a powerful means of documenting oppression, human rights abuses, and crisis “from the bottom up” and facilitating the understanding and possible transformation of conditions of injustice. It can open the space for people and narratives that have been marginalized to challenge official narratives and complicate narrow accounts of injustice and crisis. The course will first explore what is distinct about oral history as a response to harm or injustice, comparing it to more familiar forms of testimony and narrative used within the realm of human rights, social justice organizations and courts of law. With its commitment to life narrative interviews and archival preservation, oral history situates injustice within the broader context of a life, a historical trajectory, and a political and cultural setting. Weaving together conceptual and practical approaches, we will examine different potential goals of oral history, such as documenting the experiences of people who have been marginalized; seeking justice; fostering dialogue and healing; and/or supporting activism and advocacy. The course covers interviewing skills and project planning specifically for oral history projects about injustice and human rights, and explores various dimensions of how power, politics, and ethics come into play — how politics and power shape the way a narrative is heard; the challenges of realizing ideals of collaboration and shared authority amid uneven power dynamics; contending with the effects of trauma on both narrators and interviewers; and critical considerations for projects produced with activist and advocacy aims. We will explore how oral history can work alongside other forms of memory and witnessing that go beyond words, such as activism, film, and memorials.
- Department: Oral History
- T 2:10 p.m.-4 p.m.
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Instructor:
Suchitra Vijayan
Info - 4 points
APPLIED GENERATIVE AI
This course offers a comprehensive introduction to a branch of machine learning called generative modeling, focusing on the underlying concepts, theoretical techniques, and practical applications. The defining property of Generative AI models is their ability to generate new data similar to a given dataset. In recent years, Generative AI has seen rapid advancement, revolutionizing various industries by enabling machines to create realistic and novel content, ranging from images, videos, and music to text and complex simulations.
Students will learn to use, fine-tune, and programmatically interface with high-level APIs and open-source foundational models, allowing them to leverage state-of-the-art tools in Generative AI. Additionally, the course delves into the theory and practice of low-level implementations, empowering students to train their own models on their own data and understand these models from first principles. The course covers various types of generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformers with their applications to text, image, audio, and video generation.
By combining these approaches, this course provides a robust foundation in both the practical application and deep theoretical knowledge required to develop innovative AI solutions.
- Department: Applied Analytics
- M 6:10 p.m.-8 p.m.
- T 6:10 p.m.-8 p.m.
- W 8:10 p.m.-10 p.m.
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Instructor:
Maryam Fazel-Zarandi
Info -
Instructor:
Prachi Patel
h-index: 3 Info -
Instructor:
Gurgen Hayrapetyan
Info - 3 points
SPORTS MARKETING
- Department: Sports Management
- T 6:10 p.m.-8 p.m.
- R 4:10 p.m.-6 p.m.
- T 4:10 p.m.-6 p.m.
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Instructor:
Grant J Son
Info - 3 points
INFORMATION TECHNOLOGY RISK MANAGEMENT
IT RISK MANAGEMENT
Students will learn how to better identify and manage a wide range of IT risks as well as better inform IT investment decisions that support the business strategy. Students will develop an instinct for where to look for technological risks, and how IT risks may be contributing factors toward key business risks. This course includes a review of IT risks, including those related to governance, general controls, compliance, cybersecurity, data privacy, and project management. Students will learn how to use a risk-based approach to identify and mitigate cybersecurity and privacy related risks and vulnerabilities. No prior experience or technical skills required to successfully complete this course.
- Department: Enterprise Risk Management
- M 8:10 p.m.-10 p.m.
- R 8:10 p.m.-10 p.m.
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Instructor:
Mike Wilkes
Info -
Instructor:
Adam Ely
Info - 3 points
SPORTS BUSINESS COMMUNICATIONS & PR
SPORTS BUSINESS COMM & PR
- Department: Sports Management
- R 2:10 p.m.-4 p.m.
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Instructor:
Joseph Favorito
Info - 3 points
SPORTS BUSINESS COMMUNICATIONS & PR
- Department: Sports Management
- R 6:10 p.m.-8 p.m.
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Instructor:
Joseph Favorito
Info - 3 points
CYBERSECURITY RISK MANAGEMENT
TBA
- Department: Enterprise Risk Management
- R 6:10 p.m.-8 p.m.
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Instructor:
Corey Hirsch
Info - 3 points
THIRD-PARTY RISK MANAGEMENT
As organizations increasingly rely on external vendors and service providers, managing third-party risks becomes paramount to ensure operational resilience, regulatory compliance, and strategic success. Challenges include:
The evolving nature of technology risks.
The impact of geopolitical tensions.
The lessons learned from disruptive events like pandemics.
By offering a comprehensive curriculum covering everything from the basics of vendor management to advanced predictive TPRM models and emphasizing regulatory requirements specific to the financial services sector, the course equips professionals with the knowledge and tools needed to navigate the intricate web of third-party relationships.
Students taking this course are prohibited from taking Supply Chain Risk Management for Non-Financials (ERMC PS5585) at any time. Contact your advisor for more information.
- Department: Enterprise Risk Management
- W 6:10 p.m.-8 p.m.
- M 8:10 p.m.-10 p.m.
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Instructor:
Shahryar Shaghaghi
Info -
Instructor:
Sara Ricci
Info - 3 points
DIGITAL SPORTS MEDIA & MARKETING
DIG SPRTS MEDIA & MRKTG
- Department: Sports Management
- W 4:10 p.m.-6 p.m.
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Instructor:
Tom Richardson
Info - 3 points
SUPPLY CHAIN RISK MANAGEMENT FOR NON-FIN
SUPPLY CHAIN RM FOR NON-F
The Pandemic made us all aware of the fragility of supply chains and how significant the consequences of failure of our supply chains can be. It is paramount to note that global and local economies can break down, and scarcity of essential resources can foment wars. Risk professionals must know what best practices bring security to supply chains and related companies, governments, and other institutions.
Students taking this course are prohibited from taking Third-Party Risk Management (ERMC PS5575) at any time. Contact your advisor for more information.
- Department: Enterprise Risk Management
- R 6:10 p.m.-8 p.m.
- T 8:10 p.m.-10 p.m.
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Instructor:
Dan Cahill
Info -
Instructor:
Bryce Wolf
Info - 3 points
COGNITIVE BIAS & ERM
Explores key concepts of behavioral economics and cognitive psychology, how to identify key cognitive biases in ERM activities, and how to apply techniques to address these, enhancing the quality and integrity of an ERM program. The course also includes best practices in leveraging analytic models to improve decision making.
- Department: Enterprise Risk Management
- W 8:10 p.m.-10 p.m.
- W 6:10 p.m.-8 p.m.
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Instructor:
Rich Lauria
Info -
Instructor:
Tatiana Dmitrieva
Info - 3 points
STRATEGY AND ANALYTICS
n/a
- Department: Applied Analytics
- T 8:10 p.m.-10 p.m.
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Instructor:
Ranjit Kumble
Info - 3 points
STRATEGY AND ANALYTICS
STRATEGY AND ANALYTICS ONLINE
n/a
- Department: Applied Analytics
- M 8:10 p.m.-10 p.m.
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Instructor:
Diahanna L Post
Info - 3 points
CAPSTONE
The Capstone Project is an opportunity for students to synthesize and apply learnings from throughout the Strategic Communication program. Under the guidance of expert advisers, you’ll investigate a real-world communication issue, devising solutions and strategies that bridge the gap between theory and practice.
- Department: Strategic Communication
- R 3:10 p.m.-6 p.m.
- R 5:10 p.m.-8 p.m.
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Instructor:
Lilian Ore
Info -
Instructor:
Elyse Hoelzer
Info - 3 points
ROLE OF FINANCE IN INSURANCE
- Department: Insurance Management
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Instructor:
Robert Percopo
Info - 3 points
Intro Darkroom Photography
Introductory course to analog photographic tools, techniques, and photo criticism. This class explores black & white, analog camera photography and darkroom processing and printing. Areascovered include camera operations, black and white darkroom work, 8x10 print production, and critique. With an emphasis on the student’s own creative practice, this course will explore the basics of photography and its history through regular shooting assignments, demonstrations, critique, lectures, and readings. No prior photography experience is required.
- Department: Visual Arts
- MW 9:30 a.m.-noon
- MW 1 p.m.-3:30 p.m.
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Instructor:
Delphine A Fawundu
Wikipedia Info -
Instructor:
Matthew E Buckingham
Wikipedia Info - 3 points
STRATEGIC COMMUNICATION INDEPENDENT STUD
STRAT COMM INDEPENDENT ST
The Independent Study in Strategic Communication is a 3-credit course offered by special arrangement with the Strat Comm program on a case-by-case basis.
- Department: Strategic Communication
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Instructor:
Andrea Legnani
Info - 3 points
SPORTS FACILITIES & EVENTS MANAGEMENT
SPORT FACILITY & EVENT
- Department: Sports Management
- R 4:10 p.m.-6 p.m.
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Instructor:
William Squires
Info - 3 points
INVESTMENT & ALM FOR ACTUARIES
The goal of this elective course is to provide you with a broad understanding of fixed income securities and how they are used for asset liability management (ALM) in financial institutes. This course is designed for individuals who currently work or plan to work as insurance and financial professionals such as actuaries, traders, and quants. The course builds on concepts introduced in several of the program’s core courses and emphasizes the application of theories. The course covers content adapted from the SOA syllabus for fellowship exams and is split into four parts: interest rate risk measurements, interest rate management—ALM strategy, ALM decision-based asset allocation, and value-based management. In this course, you will learn several ALM techniques related to mitigating interest rate risks, managing risk and return trade-offs, and setting strategic asset allocation (SAA) to achieve an optimized risk/return portfolio. Additionally, you will be introduced to the concepts of value-based management and economic value of liabilities. Completing this course will give you a fundamental basis for understanding ALM in financial organizations and further prepare you to apply these concepts in real-life situations under both generally accepted accounting principles (GAAP) and market consistent approaches.
- Department: Actuarial Science
- MW 7:40 p.m.-8:55 p.m.
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Instructor:
Dariush Akhtari
Info - 3 points
SOLAR PROJECT DEVELOPMENT
TBA
- Department: Sustainability Management
- R 6:10 p.m.-8 p.m.
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Instructor:
Daniel J Giuffrida
Info - 3 points
SOLAR PROJECT DEVELOPMENT
TBA
- Department: Auditing
- R 6:10 p.m.-8 p.m.
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Instructor:
Daniel J Giuffrida
Info - 0 points
Infrastructure Development for Sustainab
Sustainable and resilient cities require integrated networks of transportation, water, waste, stormwater, energy, parks, housing, and communication infrastructure to support a low-carbon society and lifestyles. This course, led by two experienced practitioners and civic leaders, examines climate solutions at the city level through the lens of capital programs and policies, including responses to Hurricane-related challenges in New York City. Class modules cover key topics such as program development, stakeholder engagement, public support, project finance, contracting, and public-private partnerships, alongside sector-specific challenges, technologies, and initiatives. Grounded in real-world case studies, the course features guest lectures from city agencies and private-sector experts, as well as a field trip offering a behind-the-scenes look at an infrastructure facility. Designed for future sustainability leaders, this course equips students with the knowledge and skills to shape the cities of tomorrow.
- Department: Sustainability Management
- W 6:10 p.m.-8 p.m.
- 3 points
Infrastructure Development for Sustainab
Sustainable and resilient cities require integrated networks of transportation, water, waste, stormwater, energy, parks, housing, and communication infrastructure to support a low-carbon society and lifestyles. This course, led by two experienced practitioners and civic leaders, examines climate solutions at the city level through the lens of capital programs and policies, including responses to Hurricane-related challenges in New York City. Class modules cover key topics such as program development, stakeholder engagement, public support, project finance, contracting, and public-private partnerships, alongside sector-specific challenges, technologies, and initiatives. Grounded in real-world case studies, the course features guest lectures from city agencies and private-sector experts, as well as a field trip offering a behind-the-scenes look at an infrastructure facility. Designed for future sustainability leaders, this course equips students with the knowledge and skills to shape the cities of tomorrow.
- Department: Auditing
- W 6:10 p.m.-8 p.m.
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Instructor:
Carter H Jr Strickland
Info - 0 points
ANALYTICS & LEADING CHANGE
ANALYTICS & LEADING CHNG
- Department: Applied Analytics
- T 6:10 p.m.-8 p.m.
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Instructor:
Ashoke S Bhattacharjya
Info - 3 points
ANALYTICS & LEADING CHANGE
- Department: Auditing
- M 6:10 p.m.-8 p.m.
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Instructor:
David Kreutter
Info - 0 points
ANALYTICS & LEADING CHANGE
ANALYTICS & LEADNG CHNG ONLINE
- Department: Applied Analytics
- M 6:10 p.m.-8 p.m.
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Instructor:
David Kreutter
Info - 3 points
CINEMA/MEDIA HISTORIOGRAPHY
An introduction to issues and cases in the study of cinema century technologies. This class takes up the definition of the historiographic problem and the differences between theoretical empirical solutions. Specific units on the history of film style, genre as opposed to authorship, silent and sound cinemas, the American avant-garde, national cinemas (Russia and China), the political economy of world cinema, and archival poetics. The question of artificial intelligence approached as a question of the “intelligence of the machine.” A unit on research methods is taught in conjunction with Butler and C.V. Starr East Asian Libraries. Writing exercises on a weekly basis culminate in a digital historiography research map which becomes the basis of final written “paper” posted in Courseworks in video essay format. Students present this work at a final conference. Topics in the past include:
Cultural Transactions: Across Media and Continents, Genre: Repetition and Difference, and Bang, Bang, Crash, Crash: Canon-Busting and Paradigm-Smashing
- Department: Film
- W 10:10 a.m.-12:40 p.m.
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Instructor:
Jane Gaines
CULPA: 4 Info - 3 points
Statistics - Graduate Recitation
Statistics - Grad Recitat
This course is an introduction to probability and statistics for data science. Topics
include probability theory, probability distributions, simulations, parameters estima-
tion, hypothesis testing, simple regression. Python examples will be used throughout
the course for illustrations.
- Department: Statistics
- M 10:10 a.m.-11:25 a.m.
- T 7:10 p.m.-8:25 p.m.
- W 4:10 p.m.-5:25 p.m.
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Instructor:
Dobrin Marchev
Info - 0 points
Sustainability Ethics and Financial Capi
The World Bank has estimated that the global cost of corruption is at least $2.6 trillion, or 5% of the global gross domestic product (GDP). Businesses and individuals pay over $1 trillion in bribes annually, which does not account for billions of dollars of both humanitarian and development aid that pass clandestinely from public to private hands, billions lost to tax evasion, and billions funneled to and from illegal trafficking. In addition, it does not account for billions enmeshed in conflicts of interest, ranging from campaign donations to regulatory loopholes and in general, “private gain from public office”. All such transactions occur in globally widespread arenas of corrupt practices. In this money-based environment, “what is just” in the distribution of programmatic goods and services needs continually to be determined, and depends upon whose participation will be allowed, counted and verified in decision processes. Some voices are heard, others are unheard, and the difference often depends upon the existing distribution of wealth, including the access wealth facilitates to these processes.
In this complex situation, which results significantly from unethical practices, the process and success of sustainability, including the UN Sustainable Development Goals (SDGs), depend upon the positive inroads and disruptions made by ethical practices. What are the features of these practices? What kinds of ethics are necessary and integral to the process and success of sustainability? Many new practical ethics, framed by scholars and practitioners since the 1980s, are promoted today by individuals and organizations, including national and international governmental organizations, civil society organizations (CSOs, also called NGOs), corporations, and even loosely structured grassroots movements. In what forms and at what levels of sustainability management are the new ethics to be adopted and pursued? This course seeks to identify, explain and consider such “sustainability ethics” and the ways in which sustainability managers can activate them, largely through issue-framing, agenda-setting, and policy, program and project design, inspection and review.
The course material is divided into three sections: challenges, pathways and practices. Challenges include the worldwide dimensions of ethical problems today; and the three particular problems of corruption, conflict and climate, which undercut economy, society and ec
- Department: Sustainability Management
- W 6:10 p.m.-8 p.m.
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Instructor:
Adela J Gondek
CULPA: 2 Info - 3 points
ESTATE PLANNING
This course examines the key concepts and skills a wealth management
professional must understand to support making critical decisions with respect to
estate planning. Students will first be introduced to the fundamental characteristics
and consequences of property titling, before studying the components of estate
planning documentation. This course will explore the various strategies used to
transfer property and all of the factors impacting and related to the transfer
process, including gift and estate tax compliance and tax calculation, estate
liquidity, marital deduction, non-traditional relationships, and the types, features,
and taxation of trusts. Students will also explore the various techniques for
postmortem estate planning and techniques for intra-family and other business
transfers of property. The course will also begin to explore estate planning in a
global context, addressing issues and considerations that may arise.
PROBABILITY & STAT FOR DATA SC
This course covers the following topics: Fundamentals of probability theory and statistical inference used in data science; Probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem; Statistical inference; point and confidence interval estimation, hypothesis tests, linear regression.
- Department: Statistics
- MW 6:10 p.m.-7:25 p.m.
- TR 4:10 p.m.-5:25 p.m.
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Instructor:
Dobrin Marchev
Info - 3 points
EXPLORATORY DATA ANALYSIS/VISUAL
This course is covers the following topics: fundamentals of data visualization, layered grammer of graphics, perception of discrete and continuous variables, intreoduction to Mondran, mosaic pots, parallel coordinate plots, introduction to ggobi, linked pots, brushing, dynamic graphics, model visualization, clustering and classification.
- Department: Statistics
- MW 4:10 p.m.-5:25 p.m.
- TR 4:10 p.m.-5:25 p.m.
- None None-None
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Instructor:
Joyce T Robbins
CULPA: 5
Info
- 3 points
Intro to Digital Photography
Since Walter Benjamin’s concept of “work of art in the age of mechanical reproduction” (1935), photography has been continuously changed by mechanical, and then digital, means of image capture and processing. This class explores the history of the image, as a global phenomenon that accompanied industrialization, conflict, racial reckonings, and decolonization. Students will study case studies, read critical essays, and get hands-on training in capture, workflow, editing, output, and display formats using digital equipment (e.g., DSLR camera) and software (e.g., Lightroom, Photoshop, Scanning Software). Students will complete weekly assignments, a midterm project, and a final project based on research and shooting assignments. No Prerequisites and no equipment needed. All enrolled students will be able to check out Canon EOS 5D DSLR Camera; receive an Adobe Creative Cloud license; and get access to Large Format Print service.
- Department: Visual Arts
- TR 1 p.m.-3:30 p.m.
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Instructor:
Dana N Buhl
Info - 3 points
STAT INFERENCE & MODELING
Prerequisites: (STAT GR5701) working knowledge of calculus and linear algebra (vectors and matrices), STAT GR5701 or equivalent, and familiarity with a programming language (e.g. R, Python) for statistical data analysis. In this course, we will systematically cover fundamentals of statistical inference and modeling, with special attention to models and methods that address practical data issues. The course will be focused on inference and modeling approaches such as the EM algorithm, MCMC methods and Bayesian modeling, linear regression models, generalized linear regression models, nonparametric regressions, and statistical computing. In addition, the course will provide introduction to statistical methods and modeling that addresses various practical issues such as design of experiments, analysis of time-dependent data, missing values, etc. Throughpout the course, real-data examples will be used in lecture discussion and homework problems. This course lays the statistical foundation for inference and modeling using data, preparing the MS in Data Science students, for other courses in machine learning, data mining and visualization.
- Department: Statistics
- TR 6:10 p.m.-7:25 p.m.
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Instructor:
Hammou El Barmi
CULPA: 8
Info
- 3 points
Electrification and Decarbonization
Electricity is the lifeblood of human society. Decarbonization of global economies through electrification is seen as the most viable path for reducing GHG emissions and addressing the worst effects of climate change. Though generally accepted as the best path forward, an understanding of the operational parameters of the electric system is essential to understanding both the benefits and limitations of current and future actions. This includes the highly visible investments in renewable energy generation, less visible but equally important investments in transmission and distribution infrastructure, and the largely personal, private choices of individual households and businesses.
The course will examine pathways for the transition from fossil fuel-based electricity generation to one dominated by electricity generated by renewable energy. Students will examine the drivers of past energy transitions and various factors influencing current energy systems. At the conclusion of the course, students will be able understand the drivers of past energy transitions, the impact of those drivers on the overall energy supply chain, and how new technologies (e.g. distributed energy resources, smart meters, internet of things (IOT), etc.), consumer adoption of mass market products (e.g. Electric Vehicles, battery backup, etc,) and evolving consumer expectations (e.g. fast charging) are altering long held assumptions about energy production and use. Through this work, students will be able to infer practical steps to support current efforts to decarbonize and the potential impacts of those actions on the modern energy supply chain.
- Department: Sustainability Management
- T 4:10 p.m.-6 p.m.
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Instructor:
Rory Christian
Info - 3 points
Electrification and Decarbonization
Electricity is the lifeblood of human society. Decarbonization of global economies through electrification is seen as the most viable path for reducing GHG emissions and addressing the worst effects of climate change. Though generally accepted as the best path forward, an understanding of the operational parameters of the electric system is essential to understanding both the benefits and limitations of current and future actions. This includes the highly visible investments in renewable energy generation, less visible but equally important investments in transmission and distribution infrastructure, and the largely personal, private choices of individual households and businesses.
The course will examine pathways for the transition from fossil fuel-based electricity generation to one dominated by electricity generated by renewable energy. Students will examine the drivers of past energy transitions and various factors influencing current energy systems. At the conclusion of the course, students will be able understand the drivers of past energy transitions, the impact of those drivers on the overall energy supply chain, and how new technologies (e.g. distributed energy resources, smart meters, internet of things (IOT), etc.), consumer adoption of mass market products (e.g. Electric Vehicles, battery backup, etc,) and evolving consumer expectations (e.g. fast charging) are altering long held assumptions about energy production and use. Through this work, students will be able to infer practical steps to support current efforts to decarbonize and the potential impacts of those actions on the modern energy supply chain.
- Department: Auditing
- T 4:10 p.m.-6 p.m.
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Instructor:
Rory Christian
Info - 0 points
SUMA PS5715 Intelligent Energy Systems:
Modernizing energy systems is essential for achieving a sustainable future. This course provides an in-depth exploration of intelligent energy systems, emphasizing the transformative role of Artificial Intelligence (AI) and Battery Energy Storage Systems (BESS) in advancing grid operations and sustainability objectives. Designed to integrate theoretical foundations with real-world application, the course highlights innovative methodologies and cutting-edge tools, enabling students to tackle critical challenges such as renewable energy intermittency, curtailment, grid reliability, and resilience. Through a challenging yet approachable curriculum, students will develop both the knowledge and hands-on expertise required to lead innovations in the renewable energy industry within an increasingly complex and interconnected world. This elective course is designed for students interested in the intersection of renewable energy, technology, and environmental stewardship. This course will emphasize transformative technologies in grid modernization, highlighting the pivotal roles of AI and BESS.
- Department: Sustainability Management
- M 6:10 p.m.-8 p.m.
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Instructor:
Jingbo Hao
Info - 3 points
POLICY AND LEGAL CONTEXT FOR SUSTAINABIL
POLICY & LEGAL CNTXT FOR
- Department: Sustainability Management
- W 6:10 p.m.-8 p.m.
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Instructor:
Seth Kerschner
Info - 3 points
Advanced Topics in Financial Economics
Advanced Financial Econom
This is a topics course in financial economics intended for Economics MA students. The focus of the course is on applied methods, including training with financial data, estimation methods, and identification strategies for causal analysis in finance research. Students will cover papers and gain practical experience in execution algorithms, machine learning in asset pricing, asset demand systems, financial intermediaries, trading costs, and passive investments. Prior coding and programming experience in a specific language are not strictly necessary, but basic knowledge of R or Python are helpful.
- Department: Economics
- T 6:30 p.m.-8:20 p.m.
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Instructor:
Arkaprava Bandyopadhyay
Info - 3 points
Sustainable Management of Forests
The course intends to give an overview of forests – how they function, and how they can be managed sustainably. The course addresses both the ecology and economics of forests. Combining the study of these two disciplines is necessary to understand and develop management actions and solutions to deforestation. The emphasis in integrating ecology and economics is going to be on learning tools and techniques for managing forests. The course accounts both for North American and forests in other countries, including tropical ones. Current typical conceptions of forests are somewhat paradoxical: forests are considered marginal in sustainability, and yet they connect with many issues of central concern such as biodiversity, climate change, household energy for the poor, homelands for indigenous people, water and human shelter, to name a few. More specifically, forests provide a fruitful line of inquiry into many environmental issues, such as the complex balances within ecosystems, global cycling of elements, such as carbon, the nature of sustainability, and interactions between economic development and the conservation of nature. For example, we will study biodiversity in forests. Much biodiversity is found outside of forests, but our study will provide an understanding of the ecological dynamics involved with biodiversity, the possible management options, and its importance for human survival. The course is going to emphasize the role of forests in the carbon cycle and the contribution of deforestation to climate change.
- Department: Sustainability Management
- R 4:10 p.m.-6 p.m.
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Instructor:
Ralph C Schmidt
Info - 3 points
STORYTELLING WITH DATA AND AI
STORYTELLING WITH DATA AI
Data does not have meaning without context and interpretation. Being able to effectively present data analytics in a compelling narrative to a particular audience will differentiate you from others in your field. This course takes students through the lifecycle of an analytical project from a communication perspective. Students develop written, verbal, and visual deliverables for three major audiences: data experts (e.g., head of analytics); consumer and presentation experts (e.g., chief marketing officer); and executive leadership (e.g., chief executive officer).
Students get ample practice in strategic interactions in relevant social and professional contexts (e.g., business meetings, team projects, and one-on-one interactions); active listening; strategic storytelling; and creating persuasive professional spoken and written messages, reports, and presentations. Throughout the course, students create and receive feedback on data storytelling while sharpening their ability to communicate complex analytics to technical and nontechnical audiences with clarity, precision, and influence.
- Department: Applied Analytics
- M 8:10 p.m.-10 p.m.
- T 8:10 p.m.-10 p.m.
- M 6:10 p.m.-8 p.m.
- T 8:10 a.m.-10 a.m.
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Instructor:
Javid B Huseynov
Info -
Instructor:
Benjamin Kinsella
Info -
Instructor:
Christian Klose
Info -
Instructor:
Scott A Spencer
Info - 3 points
STORYTELLING WITH DATA AND AI
Data does not have meaning without context and interpretation. Being able to effectively present data analytics in a compelling narrative to a particular audience will differentiate you from others in your field. This course takes students through the lifecycle of an analytical project from a communication perspective. Students develop written, verbal, and visual deliverables for three major audiences: data experts (e.g., head of analytics); consumer and presentation experts (e.g., chief marketing officer); and executive leadership (e.g., chief executive officer).
Students get ample practice in strategic interactions in relevant social and professional contexts (e.g., business meetings, team projects, and one-on-one interactions); active listening; strategic storytelling; and creating persuasive professional spoken and written messages, reports, and presentations. Throughout the course, students create and receive feedback on data storytelling while sharpening their ability to communicate complex analytics to technical and nontechnical audiences with clarity, precision, and influence.
- Department: Applied Analytics
- W 8:10 p.m.-10 p.m.
- F 2:10 p.m.-4 p.m.
- W 8:10 p.m.-10 p.m.
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Instructor:
Benjamin Kinsella
Info -
Instructor:
Gert de Geyter
Info -
Instructor:
Scott A Spencer
Info - 3 points
STORYTELLING WITH DATA AND AI
STORYTELLING WITH DATA ONLINE
Data does not have meaning without context and interpretation. Being able to effectively present data analytics in a compelling narrative to a particular audience will differentiate you from others in your field. This course takes students through the lifecycle of an analytical project from a communication perspective. Students develop written, verbal, and visual deliverables for three major audiences: data experts (e.g., head of analytics); consumer and presentation experts (e.g., chief marketing officer); and executive leadership (e.g., chief executive officer).
Students get ample practice in strategic interactions in relevant social and professional contexts (e.g., business meetings, team projects, and one-on-one interactions); active listening; strategic storytelling; and creating persuasive professional spoken and written messages, reports, and presentations. Throughout the course, students create and receive feedback on data storytelling while sharpening their ability to communicate complex analytics to technical and nontechnical audiences with clarity, precision, and influence.
- Department: Applied Analytics
- W 8:10 p.m.-10 p.m.
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Instructor:
Bum Chul Kwon
h-index: None Info - 3 points
INTERNSHIP
- Department: Film
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Instructor:
Hanna Seifu
Info -
Instructor:
Maureen A Ryan
Info -
Instructor:
Jack Lechner
Info -
Instructor:
Mynette Louie
Wikipedia Info - 1-3 points
Topics in Nursing Practice
TOPICS IN NURSING PRACTIC
This course is designed to introduce pre-licensure students to relevant and emergent topics which affect the practice of nursing in the national and international healthcare system. The focus will be on issues confronting professional nurses including global health, cultural awareness, gender identity, and evidence-based wellness. State mandated topics for licensure will be covered.
- Department: Nursing
- T 8 a.m.-9 a.m.
- 0 points
Topics in Nursing Practice
TOPICS IN NURSING PRACTIC
This course is designed to introduce pre-licensure students to relevant and emergent topics which affect the practice of nursing in the national and international healthcare system. The focus will be on issues confronting professional nurses including global health, cultural awareness, gender identity, and evidence-based wellness. State mandated topics for licensure will be covered.
- Department: Nursing
- T 8 a.m.-9 a.m.
- 0 points
VISITING ARTIST LECT. I
The component includes scheduled studio critiques with some of New York’s most distinguished art practitioners, and is meant to offer multiple perspectives relevant to the training of contemporary artists. The Visual Arts program invites 20-25 artists and critics a semester, and each student sees at least two Visiting Critics per semester.
- Department: Visual Arts
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Instructor:
Miya Masaoka
Wikipedia Info - 0 points
SPECIAL TOPICS IN WEALTH MANAGEMENT: DIS
DISRUPTIVE TRENDS
Columbia SPS is on the forefront of leading issues in the Wealth Management
profession. This course is designed to explore disruptive trends in the Wealth
Management industry and the opportunities and challenges that may result. As the
profession evolves, our graduates will be prepared to be leaders within all business
models across wealth management. Topics include, but are not limited to,
technology, client psychology, ESG/sustainable investing, financial products,
evolving fee structures, shifting demographics, increased regulatory burdens,
democratization of financial advice, and more.
- Department: Wealth Management
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Instructor:
Matthias Kuhlmey
Info - 3 points
INTERNSHIP
- Department: Writing
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Instructor:
Deborah Paredez
CULPA: 3
Wikipedia
Info
- 1-3 points
Wisdom and Happiness
- Department: Business
- MTWRF 9 a.m.-5 p.m.
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Instructor:
Faculty
h-index: 37 Info - 3 points
ACTUARIAL METHODS
The Actuarial Methods course explores models for evaluating and managing risks of life contingent contracts, their theoretical basis and applications. Topics include survival models, life insurance and annuity benefits, premium and reserve calculations related to policies on a single life, as well as option pricing. This course also covers materials relevant to the long-term section of the Fundamentals of Actuarial Mathematics (FAM) exam of the Society of Actuaries. This is a core course of the M.S. in Actuarial Science program.
ADVANCED ACTUARIAL METHODS
The purpose this class is to develop the student’s knowledge of the theoretical basis of certain actuarial models and the application of those models to insurance and other financial risks. A thorough knowledge of calculus, probability, and interest theory is assumed. Knowledge of risk management at the level of Exam P is also assumed.
The combination of these two classes covers the material for the FAM-L and ALTAM examinations of the Society of Actuaries. This is a core class of the Actuarial Science program. Students who have already taken and passed the MLC or LTAM exam for SOA are exempted from this class and can substitute an elective.
- Department: Actuarial Science
- MW 1:10 p.m.-2:25 p.m.
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Instructor:
Abraham Weishaus
Info - 3 points
ACTUARIAL MODELS
This course provides an introduction to the tools for pricing and reserving for short term insurance. We will discuss methods for calculating IBNR reserves, ratemaking, frequency and severity models used for modeling coverage modifications, statistical methods for fitting, evaluating, and selecting parametric models for frequency and severity, and three credibility methods.
This class covers the short-term material of Exam FAM and also covers the material of Exam ASTAM of the Society of Actuaries, and some of the material on Exams MAS I, MAS II, and 5 of the Casualty Actuarial Society. This is a core class of the Actuarial Science program. Students who have already taken and passed the FAM exam (or its short term portion) and the ASTAM exam administered by the SOA are exempted from this class and can substitute an elective.
- Department: Actuarial Science
- MW 11:40 a.m.-12:55 p.m.
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Instructor:
Abraham Weishaus
Info - 3 points