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 6:10 p.m.-8 p.m.
-
Instructor:
Tatiana Dmitrieva
Info - 3 points
STRATEGY AND ANALYTICS
n/a
- Department: Applied Analytics
- T 8:10 p.m.-10 p.m.
-
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.
-
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 6:10 p.m.-9 p.m.
-
Instructor:
Lilian Ore
Info -
Instructor:
Elyse Hoelzer
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
- MW 9:30 a.m.-noon
- MW 1 p.m.-3:30 p.m.
-
Instructor:
Delphine A Fawundu
Wikipedia Info -
Instructor:
Matthew E Buckingham
Wikipedia Info - 3 points
PENSIONS
This course is a workshop in ERISA and Taxation Rules for Actuaries. Actuarial science can be applied and cover a number of welfare benefit arrangements (such as life insurance, medical, disability, severance etc.), qualified plans and nonqualified deferred compensation plans. The services and products that are developed in the actuarial field may be governed by certain federal laws. In the U.S., these arrangements are governed by the Employee Retirement Income Security Act ("ERISA"). In addition, certain federal taxation and reporting rules may apply. To be successful in the field will require an understanding of these rules, reporting requirements, taxation rules and the government agencies (Internal Revenue Service, Department of Labor and Pension Benefit Guarantee Corporation) responsible for oversight of such arrangements. Other topics covered will include SEPs, Simple Plans, 403(b) plans, 457 plans and Nonqualified Deferred Compensation Plans.
- Department: Actuarial Science
- T 6:10 p.m.-8:55 p.m.
- 3 points
SPORTS FACILITIES & EVENTS MANAGEMENT
SPORT FACILITY & EVENT
- Department: Sports Management
- R 4:10 p.m.-6 p.m.
-
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.
- 3 points
SOLAR PROJECT DEVELOPMENT
TBA
- Department: Sustainability Management
- R 6:10 p.m.-8 p.m.
-
Instructor:
Daniel J Giuffrida
Info - 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: Sustainability Management
- W 6:10 p.m.-8 p.m.
- 3 points
ANALYTICS & LEADING CHANGE
ANALYTICS & LEADING CHNG
- Department: Applied Analytics
- S 10:10 a.m.-noon
- T 6:10 p.m.-8 p.m.
-
Instructor:
Paul J Bailo
Info -
Instructor:
Ashoke S Bhattacharjya
Info - 3 points
ANALYTICS & LEADING CHANGE
ANALYTICS & LEADNG CHNG ONLINE
- Department: Applied Analytics
- M 6:10 p.m.-8 p.m.
-
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.
-
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 12:10 p.m.-1:25 p.m.
- T 6:10 p.m.-7:25 p.m.
- W 4:10 p.m.-5:25 p.m.
- 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.
-
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.
-
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.
-
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.
-
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.
-
Instructor:
Marco A Avella Medina
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
- M 6:10 p.m.-8 p.m.
- 3 points
POLICY AND LEGAL CONTEXT FOR SUSTAINABIL
POLICY & LEGAL CNTXT FOR
- Department: Sustainability Management
- W 6:10 p.m.-8 p.m.
-
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
- 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.
-
Instructor:
Ralph C Schmidt
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
- 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.
- W 8:10 p.m.-10 p.m.
- F 2:10 p.m.-4 p.m.
- W 8:10 p.m.-10 p.m.
-
Instructor:
Javid B Huseynov
Info -
Instructor:
Benjamin Kinsella
Info -
Instructor:
Christian Klose
Info -
Instructor:
Paula Darling
Info -
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.
-
Instructor:
Bum Chul Kwon
h-index: None Info - 3 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
-
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
-
Instructor:
Matthias Kuhlmey
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.
-
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.
-
Instructor:
Abraham Weishaus
Info - 3 points
Advanced Predictive Modeling Application
ACTUARIAL MODELS II
This course discusses Bayesian methods for estimating linear models. We discuss three methods for estimating the Bayesian posterior: grid approximation, quadratic approximation, and Markov Chain Monte Carlo (MCMC) methods. Bayesian methods are used to estimate linear regression models and generalized linear models. We also use Bayesian methods to estimate multilevel models, also known as linear mixed models. We also estimate linear mixed models using non-Bayesian methods. We learn how to build, estimate, and evaluate these models and how to select the best one.
This class covers most of the material of Exam MAS II of the Casualty Actuarial Society. This is a core class of the Actuarial Science program. Students may take either this class or Actuarial Methods II. Those who have already taken and passed the MAS II exam for CAS are exempted from this class and can substitute an elective.
- Department: Actuarial Science
- MW 2:40 p.m.-3:55 p.m.
-
Instructor:
Abraham Weishaus
Info - 3 points
CRITICAL ISSUES I
- Department: Visual Arts
- T 3 p.m.-5 p.m.
-
Instructor:
Ivana Dama
Info - 3 points
PREDICTIVE MODELING IN FINANCE & INSURAN
PREDICT MODLNG IN FIN & INSRNC
This course introduces to the students, generalized linear models (GLM), time series models, and some popular statistical learning models such as decision trees models as well as random forests and boosting trees. The aim for GLM is to provide a flexible framework for the analysis and model building using the likelihood techniques for almost any data type. The aim for the statistical learning models is to build and predict or understand data structure (if unsupervised) using statistical learning methods such as tree-based for supervised learning and the Principle Component Analysis and Clustering for unsupervised learning. It develops a student’s knowledge of the theoretical basis in predictive modeling, computational implementation of the models and their application in finance and insurance. Tools such as cross-validation and techniques such as regularization and dimension reduction for fitting and selecting models are explored. We also implement these models using a combination of Excel and R.
The class covers the material of Exams, Statistics for Risk Modeling (SRM) and Predictive Analytics (PA) of Society of Actuaries, and some material of Exams, Modern Actuarial Statistics I (MAS-I) and MAS II by the Casualty Actuarial Society. This is a core course for the Actuarial Science students. Students who have already taken and passed the SRM and PA exams administered by the SOA are exempted from this class and can substitute an elective.
GRADUATE STUDIO I
- Department: Visual Arts
- R noon-4 p.m.
-
Instructor:
Miya Masaoka
Wikipedia Info - 3 points
GRADUATE STUDIO I
- Department: Visual Arts
- MR 4 p.m.-10 p.m.
-
Instructor:
Miya Masaoka
Wikipedia Info - 3 points
DATA SCIENCE IN FINANCE AND INSURANCE
DATA SCIENCE IN FIN & INSURANC
This course explores machine learning models, their theoretical basis, computing implementation and applications in finance and insurance. It discusses machine learning models for regression, classification and unsupervised learning; tools such as cross validation and techniques such as regularization, dimension reduction and ensemble learning; and select algorithms for fitting machine learning models. This course offers students an intensive hands-on experience where they combine theoretical understanding, domain knowledge and coding skills to better inform data-driven decision making.
Some topics covered are relevant to the statistical learning portion of the Society of Actuaries (SOA) and the Casualty Actuarial Society (CAS) curricula, and the quantitative methods section of the Chartered Financial Analyst (CFA) Institute curriculum. This is a core course of the Actuarial Science program.
Advanced Data Science Applications in Fi
Adv Data Sci App in Fin a
The Advanced Data Science Applications in Finance and Insurance course covers topics in database navigation, select advanced predictive analytics models and model interpretability. Topics include relational databases, generalized additive models, deep learning models, linear mixed models, Bayesian approaches, and interpretable machine learning.
Course discussions help students develop an understanding of the models and methodologies, as well as the ability to implement these models in R or python using opensource packages. Course assignments help students practice applying these models to financial, insurance and other data, as well as gain additional insights through validating aspects of the models. After taking this course, students will be able to apply these advanced predictive analytics models to financial and insurance data to better inform data-driven decision making by combining their theoretical understanding, domain knowledge and coding skills.
Some topics covered are relevant to the Advanced Topics in Predictive Analytics (ATPA) exam of the Society of Actuaries, and (with a more analytical emphasis) to the quantitative methods section of the CFA Program Level II exam by the CFA Institute.
Familiarity with machine learning models covered in the Data Science in Finance and Insurance course is helpful. Prior exposure to linear algebra, calculus, statistics, and a working knowledge of python, R and spreadsheets are necessary.
GLOBAL CAPITAL MARKET & INVESTMENT
GLOBAL CAPITAL MRKT & INVSTMNT
This course will introduce students to major issues currently of concern to all investors. It can give you the skills to conduct a sophisticated assessment of current issues and debates covered by the popular media as well as more-specialized finance journals. These skills are essential for people who pursues a financial service career, especially in today’s rapidly evolving environment. The material presented in this course are both practical important and intellectually interesting.
This course is consistent with and relevant to Chartered Financial Analyst (CFA) curriculum. It covers all subjects in CFA test and most of problems are in the same format as the CFA examination questions. This course will also provide a foundation for further study in Financial Risk Management and Financial market related courses.
- Department: Actuarial Science
- W 2:40 p.m.-5:25 p.m.
- 3 points
QUANTITATIVE RISK MANAGEMENT
QUANTITATIVE RISK MGMT
Risk Management becomes more and more important in the financial industry especially after the global financial crisis. Large financial institutions are facing high regulatory pressure from the government and public. In response to this pressure, risk management in the financial industry has been transformed dramatically over the past decade. Today, about 50 percent of the function’s staff are dedicated to risk-related operational processes such as credit administration, while 15 percent work in analytics. McKinsey research suggests that by 2025, these numbers will reach 25 and 40 percent, respectively.
This course is designed to provide students with a high-level overview of modern risk management. This is then followed by an in-depth examination of the techniques and management structures used to assess and control risk, including a detailed discussion on the implementation of Value-at-Risk, which is becoming the de facto standard for measuring risk across all the major classes: market, credit, liquidity and operational.
This course is consistent with and relevant to Financial Risk Manager (FRM) curriculum. It covers majority of FRM learning objectives in the test and it is deeper in the quantitative modelling and analysis.
- Department: Actuarial Science
- F 10:10 a.m.-12:55 p.m.
- 3 points
RISK MANAGEMENT IN P&C INSURANCE
RISK MGMT IN P&C INSURANCE
Insurance company risk management practices and requirements have evolved significantly over the last ten years, with the advances in regulation (e.g., Solvency II, NAIC ORSA) and rating agency oversight. This elective course is designed for individuals interested in moving into risk analysis roles within property and casualty (P&C) insurance, also known as general insurance. It provides a practical review of leading quantitative risk assessment and analysis practices at P&C insurance companies. The course will give you a sound understanding of quantitative risk analysis principles that will help you expand your influence in your organization and improve the way you communicate about risk to regulators, rating agencies, and boards. The course focuses on current industry practices, critical analysis skills of risk, and the development and delivery of professional work products, to influence decision makers.
The course is divided into three parts:
Introduction to P&C Insurance: you will review the unique characteristics of P&C insurers, including underwriting, claims, premiums, policy wordings, insurance law, and regulation;
Risk Analysis: you will gain a deep understanding of the key principles underlying the implementation and application of risk management within an organization, including qualitative aspects such as framework, governance and processes, as well as quantitative methods of risk measurement and modeling; and
Application: through a real life case study, you will work in a group to synthesize the quantitative risk analysis concepts with the realities of P&C insurance company information sources, develop and present a professional consulting work product to a real guest business leader from the insurance risk management community.
- Department: Actuarial Science
- M 6:10 p.m.-8:55 p.m.
- 3 points
ORAL COMMUNICATIONS FOR ACTUARIAL PROFES
ORAL COMM FOR ACTUARIAL P
This course is a workshop in communication techniques and professional development. Students make presentations individually and in teams. Actuarial science can be complex and to be successful in the field will require effective communication skills to simplify and explain the complex. The course covers communicating effectively, professional development, structuring presentations, delivery techniques and presentations. The main objective for the course is to help students take the complex including business trends and communicate it in a manner that can be understood by the target audience. We will focus on improving communication skills, networking, interview skills, job opportunities and career development.
- Department: Actuarial Science
- M 3:10 p.m.-5:55 p.m.
- 3 points
THESIS DEVELOPMENT SEMINAR
Incoming M.A. students aiming for the thesis-based program are guided through the process of defining a research question, finding an advisor, and preparing a research proposal. By the end of the semester the students will have a written research proposal to submit to potential advisors for revision. Subject to a positive review of the research proposal, students are allowed to continue with the thesis-based program and will start working with their advisor. The course will also provide an opportunity to develop basic skills that will facilitate the reminder of the student's stay at E3B and will help in their future careers.
- Department: Ecology, Evolution, and Environmental Biology
- W 2:10 p.m.-4 p.m.
-
Instructor:
Bekka Brodie
Info - 3 points
PUBLIC RELATIONS AND CORPORATE COMMUNICA
PR & CORPORATE COMMUNICATION
This elective is designed for students looking to launch careers in public relations and corporate communications across organizations, from corporate, non-profit, start-up and/or governmental institutions. Course content will provide students with a broad overview of the PR and corporate communications function and foundational communication theory, along with hands-on, tactical training in modern public relations practice. Topics covered include strategic messaging and storytelling, working with the press to generate media coverage, leveraging social media and managing reputations online, crisis communication, public relations ethics and media law, engaging internal and external audiences, and evaluating corporate communications efforts.
- Department: Strategic Communication
- T 12:10 p.m.-2 p.m.
-
Instructor:
Michael Kaye
Info - 3 points
ARTIST MENTORSHIP / VISITING ARTIST LECT
ARTIST MENTORSHIP/ VALS
This required Visual Arts core MFA curriculum course, comprising two parts, allows MFA students to deeply engage with and learn directly from a wide variety of working artists who visit the program each year.
Lecture Series
The lecture component, taught by an adjunct faculty member with a background in art history and/or curatorial studies, consists of lectures and individual studio visits by visiting artists and critics over the course of the academic year. The series is programmed by a panel of graduate Visual Arts students under the professor's close guidance. Invitations are extended to artists whose practice reflects the interests, mediums, and working methods of MFA students and the program. Weekly readings assigned by the professor provide context for upcoming visitors. Other course assignments include researching and preparing introductions and discussion questions for each of the visitors. Undergraduate students enrolled in Visual Arts courses are encouraged to attend and graduate students in Columbia's Department of Art History are also invited. Following each class-period the conversation continues informally at a reception for the visitor. Studio visits with Visual Arts MFA students take place on or around the week of the artist or critic's lecture and are coordinated and assigned by lottery by the professor.
Artist Mentorship
The Artist-Mentor component allows a close and focused relationship to form between a core group of ten to fifteen students and their mentor. Students are assigned two mentors who they meet with each semester in two separate one-week workshops. The content of each workshop varies according to the Mentors’ areas of expertise and the needs of the students. Mentor weeks can include individual critiques, group critiques, studio visits, visits to galleries, other artist's studios, museums, special site visits, readings, and writing workshops. Here are a few descriptions from recent mentors:
• During Mentor Week we will individually and collectively examine our assumptions and notions about art. What shapes our needs and expectations as artists and the impact of what we do?
• Our week will include visits to exhibition spaces to observe how the public engages the art. Throughout, we will consider art's ability to have real life consequences and the public's desire to personally engage with and experience art without mediation.
• The week will be conducted in two parts, f
- Department: Visual Arts
- T 6 p.m.-9:30 p.m.
-
Instructor:
Miya Masaoka
Wikipedia Info - 3 points
Interpersonal Dynamics: Collaboration, F
Interpersonal Dynamics: Collaboration, Facilitation and Reflective Practice
develops students’ capacity to act as reflective practitioners of
collaborative conflict resolution. Building on theories presented in
Introduction to Negotiation, the course provides students with many
opportunities to understand the interpersonal dynamics of conflict and to
practice the skills of negotiation, mediation, and facilitation.
To intervene as skilled practitioners, conflict-resolution professionals
need to understand how their worldview shapes the lens through which they
view and respond to conflict. Likewise, they need to grasp their
counterpart’s worldview and understand how the dynamics of these differing
narratives influence both sides’ perception, emotions, and responses. As a
result of their reflective practice, students can learn to make more
strategic choices as negotiators, mediators, and facilitators.
Students bring their own unique experiences, insights, and communicative
strengths to the learning process. This course seeks to build on these
contributions, providing (1) tools for deepening self-awareness as a means
of advancing connection to others, (2) opportunities for strengthening
their face-to-face communication skills as negotiators and as mediators,
and (3) techniques for developing their skills as third-party facilitators.
- Department: Negotiation & Conflict Resolution
- SUSU 9 a.m.-5 p.m.
-
Instructor:
Robert Anderson
h-index: 77 Info - 3 points
Geographies of Environmental Justice and
The origin of the American Environmental Justice Movement can be traced back to the emergence of the American
Civil Rights Movement of the 1960s, and more specifically to the U.S. Civil Rights Act of 1964. These historical
moments set the stage for a movement that continues to grow with present challenges and widening of economic,
health and environmental disparities between racial groups and socioeconomic groups. The environmental justice
movement builds upon the philosophy and work of environmentalism, which focuses on humanity’s adverse impact
upon the environment, entailing both human and non-human existence. However, environmental justice stresses the
manner in which adversely impacting the environment in turn adversely impacts the population of that environment.
At the heart of the environmental justice movement are the issues of racism and socioeconomic injustice.
This course will examine the intersections of race, equity, and the environment – focusing on history and the
growing role and impact of the environmental justice movement in shaping new sustainability discourses, ethics,
policies, and plans for the twenty-first century. Environmental Justice embeds various disciplines into its analytical
framework ranging from human geography and history to urban studies, economics, sociology, environmental
science, public policy, community organizing, and more. Drawing from these disciplines, as well as from recent
policies, advocacy, and regulations, students will develop a deeper understanding of equity, sustainability, social
impact, and environmental justice in places and spaces across the nation.
Building on the broadness of environmental justice and sustainability, this course will use the geography lens and
frameworks, building on the concept that geography brings together the physical and human dimensions of the
world in the study of people, places, and environments. Geography will set the stage for us to explore a variety of
environmental justice topics and issues in different regions across the nation, from the Black Belt South to the Rust
Belt to Cancer Alley, New Orleans, and Atlanta; then back to New York City and the metropolitan area, introducing
students to initiatives, policies, stakeholders, research, community groups, and advocacy involved in the
development and implementation of environmental laws, policies, practices, equity-based solutions, and sustainable
infrastructure.
- Department: Sustainability Management
- T 6:10 p.m.-8 p.m.
-
Instructor:
John E Williams
Info - 3 points
PROSEMINAR IN ACTUARIAL SCIENCE
Industry representatives conduct a series of noncredit seminar sessions designed to expose students to the actuarial profession as well as to address a range of topics in actuarial science.
ERM CAPSTONE
Teams will work through a case assignment, demonstrating mastery of key learnings gained throughout the program on an integrated basis. A simulated case study is used: this is a combination of publicly-available information of an actual company and simulated ERM program details, based on a blend of current ERM programs and practices in the marketplace. Each team will assess the case study and recommend enhancements.
- Department: Enterprise Risk Management
- M 6:10 p.m.-8 p.m.
- R 6:10 p.m.-8 p.m.
- T 6:10 p.m.-8 p.m.
- T 8:10 p.m.-10 p.m.
- W 8:10 p.m.-10 p.m.
- W 6:10 p.m.-8 p.m.
- R 8:10 p.m.-10 p.m.
- M 8:10 p.m.-10 p.m.
- R 6:10 p.m.-8 p.m.
-
Instructor:
Xue Bai
Info -
Instructor:
Aristargos Christodoulou
Info -
Instructor:
Bruce Fox
Info -
Instructor:
Benjamin M Perlman
Info -
Instructor:
Jiajing Wang
Info -
Instructor:
Mobarka F Ansari-Sheikh
Info -
Instructor:
Narae Lee
Info -
Instructor:
Richard R. Ranade
Info -
Instructor:
Rich Lauria
Info - 3 points
INSURANCE RISK MANAGEMENT
- Department: Insurance Management
-
Instructor:
Kenneth Radigan
Info - 3 points
Capstone in Political Analytics
The capstone course is the culminating experience for students in the Political Analytics program. Students will have the opportunity to tackle a complex, real-world political analytics challenge for a sponsoring organization. The capstone provides students with analytics experience in a “live” setting and is intended to expose students to the problems, timelines, and communications needs of actual political decision-makers. Working in small teams while being mentored by a program faculty member, students will apply core knowledge, concepts, and frameworks acquired during the program and practice the hands-on skills they have developed in their classes. Throughout the semester, student project teams will interact with the sponsoring organizations as virtual consultants, scoping the problem, acquiring the data, conducting analyses, and ultimately presenting their findings and recommendations to the project sponsor.
- Department: Political Analytics
- T 4:10 p.m.-6 p.m.
- 3 points
CAPSTONE
In the fourth semester, students will develop a comprehensive financial plan based
upon a specific client case, taking all elements of a client’s financial profile into
consideration. Students will be expected to demonstrate foundational knowledge
of all aspects of the wealth management curriculum in this course in order to
develop this financial plan. The capstone experience for this program is a dualfocus
on both the written as well as the oral components of the financial plan.
- Department: Wealth Management
-
Instructor:
Jason R Papier
Info - 3 points
DATA SCIENCE CONSULTING
- Department: Applied Analytics
- F 2:10 p.m.-4 p.m.
- M 6:10 p.m.-8 p.m.
-
Instructor:
David Shilane
Info - 3 points
APPLIED DEEP LEARNING AND AI
Deep Learning has become a cornerstone of Artificial Intelligence (AI), with applications in finance, healthcare, sports, autonomous vehicles, chatbots, national security, and artistic creations using elements of Natural Language Processing, Computer Vision, and Speech Recognition. Students will gain a solid foundation in Deep learning and its applications, starting with a compressed review of some Statistical Learning models followed by much deeper dive into Deep Neural Networks. Topics covered include Neural Networks, Convolutional Neural Networks (CNN), word embeddings, attention mechanisms, transformers, encoder-decoder architectures and Generative Adversarial Networks (GAN). Students will also learn training of agents to make optimal decisions in complex environments using Reinforcement Learning. Practical applications will demonstrate how to prepare, train, test, and validate these models.
- Department: Applied Analytics
- T 6:10 p.m.-8 p.m.
- T 8:10 p.m.-10 p.m.
-
Instructor:
Siddhartha Dalal
Info -
Instructor:
Adrian Heilbut
h-index: 13 Info - 3 points
INDEPENDENT STUDY
- Department: Visual Arts
-
Instructor:
Seth Cluett
h-index: 4 Info -
Instructor:
Miya Masaoka
Wikipedia Info - 1-6 points
INDEPENDENT STUDY
- Department: Visual Arts
-
Instructor:
Matthew E Buckingham
Wikipedia Info -
Instructor:
David A Cruz
Info -
Instructor:
Delphine A Fawundu
Wikipedia Info -
Instructor:
Jon Kessler
CULPA: 5 Wikipedia Info -
Instructor:
Naeem Mohaiemen
Wikipedia h-index: 8 Info -
Instructor:
Sable Smith
Wikipedia Info -
Instructor:
Rirkrit Tiravanija
Wikipedia Info -
Instructor:
Tomas Vu Daniel
CULPA: 4
Info
-
Instructor:
Daphne Arthur
Info -
Instructor:
Kenny Rivero
Info -
Instructor:
Miya Masaoka
Wikipedia Info - 1-3 points
SUPERVISED INTERSHIP IN SPORTS MANAGEMEN
INTERNSHIP IN SPORTS MGMT
- Department: Sports Management
-
Instructor:
Leonard Elmore
Info - 3 points
AI-ENHANCED FINDABILITY & SEMANTIC SYSTE
AI-EnhancedFindabil&Seman
The fusion of traditional information science principles with advanced AI technologies is revolutionizing knowledge management. This course explores how artificial intelligence, particularly Large Language Models (LLMs), is transforming information organization, retrieval, and utilization in digital environments.
Students explore the world of AI-enhanced findability, learning to architect intelligent knowledge platforms that maximize the value of structured and unstructured data. The course covers:
Large Language Models (LLMs) and their game-changing applications in information retrieval
Vector databases and cognitive search capabilities
Advanced Natural Language Processing (NLP) and semantic technologies
Knowledge graphs and how they are being used to add meaning to LLMs
Machine learning for sophisticated classification and categorization
Ethical considerations in AI-powered information systems
Through hands-on projects culminating in the design of real-world applications, students will gain practical experience in architecting AI-enhanced information systems. You'll learn to make informed decisions about integrating AI technologies into information architecture, balancing traditional methods with cutting-edge solutions.
This course is ideal for future knowledge strategists, information architects, and AI enthusiasts who want to lead the next generation of intelligent information systems. Join us to explore the intersection of AI and information science and prepare to shape the future of knowledge work.
No programming experience is required—just your curiosity and readiness to engage with the forefront of information technology.
- Department: Information & Knowledge Strat
- W 8:10 p.m.-10 p.m.
-
Instructor:
Ralph Poole
h-index: 0 Info - 3 points
APPLIED WRITING
- Department: Narrative Medicine
- W 3:10 p.m.-6 p.m.
- W 6:10 p.m.-9 p.m.
-
Instructor:
Jenessa R Abrams
Info - 4 points
SUPERVISED PROJECT IN SPORTS MANAGEMENT
FC BAYERN MUNCH
Projects are research intensive and vary according to partners and specialty.
Advanced standing in the Sports Management program, with at least 12 points/credits (4 courses) completed is required. A student may not exceed 6 points/credits (2 courses) of Supervised Projects, or take more than 3 points/credits (1 course) per semester.
- Department: Sports Management
- M 4:10 p.m.-6 p.m.
-
Instructor:
Grant J Son
Info - 3 points
SUPERVISED PROJECT IN SPORTS MANAGEMENT
SUPERVISED PROJ IN SPORTS
Projects are research intensive and vary according to partners and specialty.
Advanced standing in the Sports Management program, with at least 12 points/credits (4 courses) completed is required. A student may not exceed 6 points/credits (2 courses) of Supervised Projects, or take more than 3 points/credits (1 course) per semester.
- Department: Sports Management
- T 12:10 p.m.-2 p.m.
- 3 points
MASTER THESIS WORKSHOP
TAKEN WITH BIET 5992 Master Thesis (2-credit).
The Workshop meets six times over four months. These sessions will assist students in starting to focus more fully on a topic and approach. During the Thesis Workshop, students will first speak informally for five minutes about a possible topic, followed by a more formal five-minute presentation and a draft of a one-page outline or abstract, proceeding to a more finalized outline or abstract. At each of these stages, students will receive feedback from the course director as well as fellow students.
- Department: Bioethics
- F 11:30 a.m.-1 p.m.
-
Instructor:
Arthur Kuflik
Info - 1 points
LEADING LARGE COMPLEX PROJECTS
Project management has been important to many types of missions, projects, and activities for many years; however, it has been especially critical to the success of large complex projects across decades and centuries. Large complex projects span the globe across all industries and sectors. They also span concepts, product design, development, manufacturing, operations, and logistics, etc. Products may include hardware, software, services, product support, systems, and systems of systems, etc.
The primary focus of this course will be around project leadership as projects are planned and executed (project management). The course will start by recognizing the need and benefits of project management for large complex global projects, explore characteristics of project managers, and study the commonality and differences in types of projects. The course will continue with understanding the essential capabilities of project management, and analyze the variations in project lifecycles. The course will address managing risk throughout the project lifecycle, controls, and performance measurement, and maximizing the use of knowledge. Lastly, the course will visualize the future of projects and project management structure and core capabilities.
Our fundamental goal is to better prepare leaders for large complex global projects. This will be gained via readings; real-world case studies; and study, research, analysis, and exploration by the students. Therefore, the course will require students to engage in reflection, discussion, activities, and assignments aimed at personal unlearning and learning. The assignment and class discussions will be quite provocative to drive maximum learning.
- Department: Information & Knowledge Strat
- M 6:10 p.m.-8 p.m.
-
Instructor:
Gregory Robinson
Info - 3 points