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
• Instructor: Christakis Droussiotis
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• Instructor: Steve Lindo
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• 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
• Instructor: Jaehyuk Choi
  Info
• 1-3 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
• Instructor: Thilan Arachchi Appuhamillage
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• Instructor: Robert Simione
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• 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
• Instructor: Jean Michel Ngalikpima
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• Instructor: Eric Goetz
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• 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
• Instructor: Jaehyuk Choi
  Info
• 0 points

Topics in Mathematical Finance

The purpose of this course is for MA in Mathematics of Finance students to gain knowledge and practical skills that are essential in the finance industry. The course will run as a series of lectures and discussions on various relevant topics, such as business communications and career talks that may feature guest speakers from the industry as well as the full-time faculty members. This will prepare the students for their job search, networking, and in their industry jobs in the future.

• Department: Mathematics
• 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
• Instructor: Helda Rock
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• Instructor: Jean Michel Ngalikpima
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• Instructor: Tahmina Day
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• 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
• Instructor: Arash Khatibi
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• Instructor: Andy W Chen
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• 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
• Instructor: Alberto Scalari
  Info
• 3 points

MANAGING STAKEHOLDER RISK IN THE ORGANIZ

In this course, students study major concepts of management and organization theory to understand human behavior in an organizational context, and then learn how to apply this to better manage interactions with key ERM stakeholders. Students will learn how to accomplish key ERM activities effectively while preserving and enhancing key internal relationships.

The course provides a deep dive into how enterprise risk functions operate within organizations, blending theoretical frameworks with practical, real-world applications. Topics include individual and organizational psychology, risk culture, organizational structure and governance, and the dynamics of managing risk in complex institutions. Through case studies and class discussions, students explore the behavioral and structural dimensions that shape ERM practices.


This elective is open only to students within the ERM program. This course (MSRO) is analogous to Managing Human Behavior in the Organization (MHBO), but customized for an ERM role. As a result, ERM students may not register for MHBO and those that have already taken MHBO may not register for MSRO.

• Department: Enterprise Risk Management
• Instructor: Kristen Walters
  Info
• 3 points

MANAGING STAKEHOLDER RISK IN THE ORGANIZ

In this course, students study major concepts of management and organization theory to understand human behavior in an organizational context, and then learn how to apply this to better manage interactions with key ERM stakeholders. Students will learn how to accomplish key ERM activities effectively while preserving and enhancing key internal relationships.

The course provides a deep dive into how enterprise risk functions operate within organizations, blending theoretical frameworks with practical, real-world applications. Topics include individual and organizational psychology, risk culture, organizational structure and governance, and the dynamics of managing risk in complex institutions. Through case studies and class discussions, students explore the behavioral and structural dimensions that shape ERM practices.


This elective is open only to students within the ERM program. This course (MSRO) is analogous to Managing Human Behavior in the Organization (MHBO), but customized for an ERM role. As a result, ERM students may not register for MHBO and those that have already taken MHBO may not register for MSRO.

• Department: Enterprise Risk Management
• 3 points

MANAGING STAKEHOLDER RISK IN THE ORGANIZ

In this course, students study major concepts of management and organization theory to understand human behavior in an organizational context, and then learn how to apply this to better manage interactions with key ERM stakeholders. Students will learn how to accomplish key ERM activities effectively while preserving and enhancing key internal relationships.

The course provides a deep dive into how enterprise risk functions operate within organizations, blending theoretical frameworks with practical, real-world applications. Topics include individual and organizational psychology, risk culture, organizational structure and governance, and the dynamics of managing risk in complex institutions. Through case studies and class discussions, students explore the behavioral and structural dimensions that shape ERM practices.


This elective is open only to students within the ERM program. This course (MSRO) is analogous to Managing Human Behavior in the Organization (MHBO), but customized for an ERM role. As a result, ERM students may not register for MHBO and those that have already taken MHBO may not register for MSRO.

• Department: Enterprise Risk Management
• Instructor: Ann Breslawski
  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.

• Department: Applied Analytics
• Instructor: Alexandr Belyakov
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• Instructor: Lei Yu
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• 3 points

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
• Instructor: Viktor Ziskin
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• Instructor: Barney Baldwin
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• 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
• Instructor: Peter L Russo
  Info
• 3 points

SPORTS SPONSORSHIP & SALES

• Department: Sports Management
• Instructor: Ishwara Glassman Chrein
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• 3 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
• 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
• Instructor: Suchitra Vijayan
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• 4 points

Internship Experience 2 - ACTU

This asynchronous, 1.5-credit elective combines a supervised professional internship with guided analysis of workplace culture, ethics, and feedback practices. Students evaluate organizational values, inclusivity, and ethical decision-making while developing the skills needed to navigate professional environments and identify the workplace cultures in which they will thrive.

• Department: Actuarial Science
• 1.5 points

Internship Experience 2 - APAN

This asynchronous, 1.5-credit elective combines a supervised professional internship with guided analysis of workplace culture, ethics, and feedback practices. Students evaluate organizational values, inclusivity, and ethical decision-making while developing the skills needed to navigate professional environments and identify the workplace cultures in which they will thrive.

• Department: Applied Analytics
• 1.5 points

Internship Experience 2 - CNAD

Internship in CNAD

This asynchronous, 1.5-credit elective combines a supervised professional internship with guided analysis of workplace culture, ethics, and feedback practices. Students evaluate organizational values, inclusivity, and ethical decision-making while developing the skills needed to navigate professional environments and identify the workplace cultures in which they will thrive.

• Department: Construction Administration
• 1.5 points

Internship Experience 2 - COMM

This asynchronous, 1.5-credit elective combines a supervised professional internship with guided analysis of workplace culture, ethics, and feedback practices. Students evaluate organizational values, inclusivity, and ethical decision-making while developing the skills needed to navigate professional environments and identify the workplace cultures in which they will thrive.

• Department: Strategic Communication
• 1.5 points

Internship Experience 2 - ERM

This asynchronous, 1.5-credit elective combines a supervised professional internship with guided analysis of workplace culture, ethics, and feedback practices. Students evaluate organizational values, inclusivity, and ethical decision-making while developing the skills needed to navigate professional environments and identify the workplace cultures in which they will thrive.

• Department: Enterprise Risk Management
• 1.5 points

Internship Experience 2 - HCM

Internship in HCM

This asynchronous, 1.5-credit elective combines a supervised professional internship with guided analysis of workplace culture, ethics, and feedback practices. Students evaluate organizational values, inclusivity, and ethical decision-making while developing the skills needed to navigate professional environments and identify the workplace cultures in which they will thrive.

• Department: Human Capital Management
• 1.5 points

Internship Experience 2 - IKNS

Internship in IKNS

This asynchronous, 1.5-credit elective combines a supervised professional internship with guided analysis of workplace culture, ethics, and feedback practices. Students evaluate organizational values, inclusivity, and ethical decision-making while developing the skills needed to navigate professional environments and identify the workplace cultures in which they will thrive.

• Department: Information & Knowledge Strat
• 1.5 points

Internship Experience 2 - NECR

Internship in NECR

This asynchronous, 1.5-credit elective combines a supervised professional internship with guided analysis of workplace culture, ethics, and feedback practices. Students evaluate organizational values, inclusivity, and ethical decision-making while developing the skills needed to navigate professional environments and identify the workplace cultures in which they will thrive.

• Department: Negotiation & Conflict Resolution
• 1.5 points

Internship Experience 2 - NMED

Internship in NMED

This asynchronous, 1.5-credit elective combines a supervised professional internship with guided analysis of workplace culture, ethics, and feedback practices. Students evaluate organizational values, inclusivity, and ethical decision-making while developing the skills needed to navigate professional environments and identify the workplace cultures in which they will thrive.

• Department: Narrative Medicine
• 1.5 points

Internship Experience 2 - NOPM

Internship in NOPM

This asynchronous, 1.5-credit elective combines a supervised professional internship with guided analysis of workplace culture, ethics, and feedback practices. Students evaluate organizational values, inclusivity, and ethical decision-making while developing the skills needed to navigate professional environments and identify the workplace cultures in which they will thrive.

• Department: Non Profit Management
• 1.5 points

nternship Experience 2 - PMGT

This asynchronous, 1.5-credit elective combines a supervised professional internship with guided analysis of workplace culture, ethics, and feedback practices. Students evaluate organizational values, inclusivity, and ethical decision-making while developing the skills needed to navigate professional environments and identify the workplace cultures in which they will thrive.

• Department: Project Management
• 1.5 points

Internship Experience 2 - POAN

Internship in POAN

This asynchronous, 1.5-credit elective combines a supervised professional internship with guided analysis of workplace culture, ethics, and feedback practices. Students evaluate organizational values, inclusivity, and ethical decision-making while developing the skills needed to navigate professional environments and identify the workplace cultures in which they will thrive.

• Department: Political Analytics
• 1.5 points

Internship Experience 2 - SPRT

This asynchronous, 1.5-credit elective combines a supervised professional internship with guided analysis of workplace culture, ethics, and feedback practices. Students evaluate organizational values, inclusivity, and ethical decision-making while developing the skills needed to navigate professional environments and identify the workplace cultures in which they will thrive.

• Department: Sports Management
• 1.5 points

Internship Experience 2 - SUMA

Internship in SUMA

This asynchronous, 1.5-credit elective combines a supervised professional internship with guided analysis of workplace culture, ethics, and feedback practices. Students evaluate organizational values, inclusivity, and ethical decision-making while developing the skills needed to navigate professional environments and identify the workplace cultures in which they will thrive.

• Department: Sustainability Management
• 1.5 points

Internship Experience 2 - SUSC

Internship in SUSC

This asynchronous, 1.5-credit elective combines a supervised professional internship with guided analysis of workplace culture, ethics, and feedback practices. Students evaluate organizational values, inclusivity, and ethical decision-making while developing the skills needed to navigate professional environments and identify the workplace cultures in which they will thrive.

• Department: Sustainability Science
• 1.5 points

Internship Experience 2 - TMGT

This asynchronous, 1.5-credit elective combines a supervised professional internship with guided analysis of workplace culture, ethics, and feedback practices. Students evaluate organizational values, inclusivity, and ethical decision-making while developing the skills needed to navigate professional environments and identify the workplace cultures in which they will thrive.

• Department: Technology Management
• 1.5 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
• 3 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
• Instructor: Prachi Patel
  h-index: 3 Info
• Instructor: Gurgen Hayrapetyan
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• 3 points

SPORTS MARKETING

• Department: Sports Management
• Instructor: Grant J Son
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• Instructor: Thomas Cerny
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• Instructor: Laurajean Holmgren
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• 3 points

ARTIFICIAL INTELLIGENCE OF THINGS

AI OF THINGS

Over the past decade, the Internet of Things (IoT) has transformed industries by enabling real-time data collection, analysis, and automated control through interconnected devices. Advancements in networking, cloud computing, and robotics have expedited IoT adoption, impacting a wide range of fields from home safety and industrial automation to healthcare and autonomous driving. Additionally, the rise of artificial intelligence (AI) led to the emergence of the Artificial Intelligence of Things (AIoT), which combines IoT connectivity with AI-driven decision-making to enhance smart systems.

This course provides a comprehensive understanding of IoT technologies and their integration with AI and robotic systems. Students will explore IoT architecture, key components, and communication protocols while gaining hands-on experience with IoT platforms, sensors, and data acquisition devices. The curriculum emphasizes practical AIoT applications for real-time decision-making in manufacturing, public safety, smart cities, healthcare, etc., and addresses the ethical considerations of these technologies.

• Department: Applied Analytics
• 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
• Instructor: Leslie Weinstein
  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
• 3 points

SPORTS BUSINESS COMMUNICATIONS & PR

SPORTS BUSINESS COMM & PR

• Department: Sports Management
• Instructor: Joseph Favorito
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• 3 points

CYBERSECURITY RISK MANAGEMENT

Cyber losses, reflected in daily headlines on data breaches, state-sponsored attacks on critical infrastructure, and ransomware incidents, have grown to exceed other major categories of operating risk in terms of total cost, driving increased regulatory activity in response.

This means risk management professionals need a solid understanding of cyber-risk management programs, techniques, mitigation strategies, architectures, frameworks, and procedures, which this course provides.  Some frameworks covered include ISO27001, NIST CSF, CIS 18 Critical Security Controls, etc.  Effective management of cyber-risks is an Enterprise-wide activity addressing immediate risks requiring attention while building a mature foundation for a resilient and proactive cybersecurity risk management program; a Technology Risk Management foundation is therefore a prerequisite for enrollment; however, IT expertise is not.

The course provides practical, hands-on, cases and exercises for the application of cyber-risk management principles, equipping course graduates to help lower the probability of a risk event in their organization, and to enhance organizational resilience for effective incident response and recovery.

• Department: Enterprise Risk Management
• Instructor: Corey Hirsch
  Info
• 3 points

CYBERSECURITY RISK MANAGEMENT

Cyber losses, reflected in daily headlines on data breaches, state-sponsored attacks on critical infrastructure, and ransomware incidents, have grown to exceed other major categories of operating risk in terms of total cost, driving increased regulatory activity in response.

This means risk management professionals need a solid understanding of cyber-risk management programs, techniques, mitigation strategies, architectures, frameworks, and procedures, which this course provides.  Some frameworks covered include ISO27001, NIST CSF, CIS 18 Critical Security Controls, etc.  Effective management of cyber-risks is an Enterprise-wide activity addressing immediate risks requiring attention while building a mature foundation for a resilient and proactive cybersecurity risk management program; a Technology Risk Management foundation is therefore a prerequisite for enrollment; however, IT expertise is not.

The course provides practical, hands-on, cases and exercises for the application of cyber-risk management principles, equipping course graduates to help lower the probability of a risk event in their organization, and to enhance organizational resilience for effective incident response and recovery.

• Department: Enterprise Risk Management
• 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
• Instructor: Shahryar Shaghaghi
  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
• 3 points

ANALYTICS FOR CYBERSECURITY MANAGEMENT

ANALYTICS FR CYBERSECURIT

This course is designed to immerse students in the intersection of cybersecurity and data analytics. The course explores how modern data-driven approaches are revolutionizing the way organizations detect and manage cyber threats. Students will engage deeply with core cybersecurity concepts, such as network security, vulnerability management, and threat intelligence, while also learning to leverage cutting-edge data analytics and artificial intelligence to solve real-world security problems. Through hands-on exercises, coding assignments, and case studies, students will gain practical skills in analyzing logs and telemetries, building detection systems, and applying machine learning to security operations.

• Department: Applied Analytics
• 3 points

ANALYTICS FOR CYBERSECURITY MANAGEMENT

ANALYTICS FR CYBERSECURIT

This course is designed to immerse students in the intersection of cybersecurity and data analytics. The course explores how modern data-driven approaches are revolutionizing the way organizations detect and manage cyber threats. Students will engage deeply with core cybersecurity concepts, such as network security, vulnerability management, and threat intelligence, while also learning to leverage cutting-edge data analytics and artificial intelligence to solve real-world security problems. Through hands-on exercises, coding assignments, and case studies, students will gain practical skills in analyzing logs and telemetries, building detection systems, and applying machine learning to security operations.

• Department: Applied Analytics
• Instructor: Muhammed Bulut
  Info
• 3 points

DIGITAL SPORTS MEDIA & MARKETING

DIG SPRTS MEDIA & MRKTG

• Department: Sports Management
• 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
• Instructor: Dan Cahill
  Info
• Instructor: Bryce Wolf
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• 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
• Instructor: Rich Lauria
  Info
• Instructor: Tatiana Dmitrieva
  Info
• 3 points

STRATEGY AND ANALYTICS

n/a

• Department: Applied Analytics
• Instructor: Ranjit Kumble
  Info
• 3 points

STRATEGY AND ANALYTICS

STRATEGY AND ANALYTICS ONLINE

n/a

• Department: Applied Analytics
• 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
• Instructor: Mark Truss
  Info
• Instructor: Elyse Hoelzer
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• Instructor: Thomas J Groppe
  Info
• 3 points

ROLE OF FINANCE IN INSURANCE

• Department: Insurance Management
• 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
• Instructor: Matthew E Buckingham
  Wikipedia Info
• Instructor: Dana N Buhl
  Info
• 3 points

LEADERSHIP, PRESENCE, AND INFLUENCE

LEADERSHIP, PRESENCE, & I

This highly experiential course helps students design, launch, and sustain a successful career. Blending scholarly foundations with practical tools and hands-on coaching, this course guides students through identifying their personal strengths and professional identity, developing a compelling personal pitch, and building the skills needed to navigate interviewing, networking, teamwork, organizational culture and change. Each session integrates theory, applied practice, and structured role-play with peer feedback, enabling students to move beyond a job search mentality toward a proactive, values-aligned approach to career development and long-term professional success.

• Department: Enterprise Risk Management
• Instructor: David Burkus
  Info
• 1.5 points

SPORTS FACILITIES & EVENTS MANAGEMENT

SPORT FACILITY & EVENT

• Department: Sports Management
• 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
• Instructor: Dariush Akhtari
  Info
• 3 points

The Actuary s Toolkit

The Actuarys Toolkit

This course is designed to equip students in the Columbia Actuarial Program with the technical software skills essential for modern actuarial work, with a special focus on Casualty Actuarial Science. Through this course, students will gain proficiency in Excel and R—two foundational tools used in data analysis, reserving, ratemaking, and simulation modeling. Excel will be explored as a powerful and accessible tool for structuring actuarial models, performing sensitivity analysis, and managing large data sets. R will be taught as a robust statistical programming language that supports reproducible actuarial analysis, including the use of GLMs, bootstrapping methods, and data visualization. The course will also include an introductory segment on Python, highlighting its growing relevance in automating workflows, handling large-scale data, and integrating with machine learning frameworks that may be increasingly relevant in pricing and predictive modeling.

 

The broader aim of this course is to bridge the gap between theoretical actuarial concepts and practical implementation through programming. By learning these software tools, students will be able to operationalize core actuarial principles—such as risk modeling, claim development, and stochastic analysis—within real-world business contexts. The course aligns with the Actuarial Program’s mission to produce industry-ready professionals who can not only understand the mathematical underpinnings of risk but also communicate and deliver insights through modern analytics platforms. It supports the development of computational thinking, data fluency, and technical agility, which are increasingly critical in actuarial practice as the industry becomes more data-driven and technologically complex.

 

This is an elective course available exclusively to students enrolled in the Columbia Actuarial Program. No prior experience with Excel, R, or Python is required, making it an ideal entry point for students new to programming or applied analytics. The course will be conducted fully online over the course of a full academic semester, providing flexibility while maintaining rigorous engagement through weekly assignments, project-based learning, and applied actuarial case studies. Whether students aim to pursue traditional actuarial roles or explore emerging areas like InsurTech, this course will provide the software toolkit needed to succeed in a modern actuarial environment.

• Department: Actuarial Science
• Instructor: Mark Hoffmann
  Info
• 3 points

SOLAR PROJECT DEVELOPMENT

At the end of this course, students will be prepared to fully evaluate the technical and financial aspects of a solar project. They will be equipped with skills allowing them to either develop or rigorously vet solar project proposals. The course introduces and provides students with a holistic understanding of the end-to-end solar development process. The course has two goals: 


To provide students a deep understanding of the dozens of critical interrelated steps critical to developing a successful operating solar project. 

To equip the students with the tools and understanding of the skills necessary to develop a solar project beginning with site selection encompassing the entire process to commissioning and operations.

• Department: Sustainability Management
• Instructor: Daniel J Giuffrida
  Info
• 3 points

Infrastructure Development for Sustainab

ENVIRO INFRSTRCTURE SUS CITIES

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
• 3 points

ANALYTICS & LEADING CHANGE

ANALYTICS & LEADING CHNG

• Department: Applied Analytics
• Instructor: Aaron Foster
  Info
• 3 points

ANALYTICS & LEADING CHANGE

ANALYTICS & LEADNG CHNG ONLINE

• Department: Applied Analytics
• Instructor: David Kreutter
  Info
• 3 points

AI AND ETHICS: IN THE CLINIC

This course examines the ethical dimensions of artificial intelligence in healthcare, emphasizing translational ethics—the movement from identifying ethical concerns to proposing and defending justifiable, real-world solutions. Intended for clinicians, administrators, leaders, and researchers, the course explores how AI is impacting
patient care, clinical decision-making, health system operations, and clinical research. Students will engage with a broad spectrum of use cases, including AI in imaging specialties, predictive models for diagnoses and future health states, intraoperative support, large language models, mental health chatbots, and digital twins. Core themes include transparency, accuracy, impact, safety, governance, privacy, accountability, and the distribution of burdens and benefits. Seminar-style sessions are built around presentations, discussions, and interactive analysis, enabling participants to apply ethical frameworks to real clinical contexts. By course completion, students will be equipped to critically evaluate AI applications and contribute to strategies and policies that ensure safe, effective, and ethically
responsible deployment.

As part of a nascent but rapidly developing field, this course situates health AI ethics within the broader principles of patient-centered clinical ethics, equipping students to think critically about how emerging technologies intersect with core values of patient care. By engaging with both current applications and future concerns, the course helps learners bridge foundational ethical concepts with the novel challenges posed by AI solutions in diverse clinical settings—from imaging and predictive analytics to mental health support and operational tools. Within the program curriculum, the course complements broader training in Bioethics by focusing specifically on applied, translational ethics, ensuring students are prepared not only to identify ethical issues but also to propose and defend actionable solutions that can guide clinical practice, institutional policy, and system-level decision-making.

This course is offered as an elective within the program and is open to Bioethics students and students from other fields or Columbia University programs with permission. No specific competencies, prerequisite coursework, or prior knowledge in the discipline are required; students from diverse professional and academic backgrounds are welcome. The course is delivered fully online in a seminar format, fostering interac

• Department: Bioethics
• Instructor: Charles Binkley
  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
• 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
• Instructor: Dobrin Marchev
  Info
• 0 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
• Instructor: Dobrin Marchev
  Info
• 0 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
• Instructor: Dobrin Marchev
  Info
• 0 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.

• Department: Wealth Management
• Instructor: David Oh
  Info
• 3 points

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
• Instructor: Dobrin Marchev
  Info
• 3 points

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
• 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
• Instructor: Joyce T Robbins
  CULPA: 5 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
• 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
• Instructor: Ben Salesse
  Info
• Instructor: Karen Dias
  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
• Instructor: Dobrin Marchev
  Info
• 3 points

GLOBAL FIELDWORK IN COMMUNICATION

This course gives students a chance to explore topics of global importance while gaining competence in cross-cultural communication and collaboration. The centerpiece is a 10-day study abroad experience, in which students travel internationally to engage with local actors and organizations who are doing strategic communication work. The course focuses on communication related to climate resilience, public health, politics, government, and culture. Students gain global perspective on the impact of strategic communication across sectors. See country brochure for additional details.

• Department: Strategic Communication
• Instructor: Kristine Billmyer
  Info
• 3 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
• Instructor: Jingbo Hao
  Info
• 3 points

POLICY AND LEGAL CONTEXT FOR SUSTAINABIL

PLCY/LGL CNTX FOR SUSTAINABLTY

• Department: Sustainability Management
• 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
• 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
• 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
• Instructor: Javid B Huseynov
  Info
• Instructor: Christian Klose
  Info
• Instructor: Paula Darling
  Info
• 3 points