ERM MODELING

Equips students with the basics of risk measurement and simulation using a hands-on approach to ERM modeling. Using industry-standard simulation software, students build systems of risk drivers for finance and insurance companies. Topics include risk correlations, VaR and TVaR, capital modeling, capital allocation, and parameter, process, and model Risk. Students acquire both quantitative experience building models and qualitative appreciation for model weaknesses.

• Department: Enterprise Risk Management
• W 6:10 p.m.-8 p.m.
• Instructor: Tom Passante
  Info
• 3 points

SPORTS SPONSORSHIP & SALES

• Department: Sports Management
• M 6:10 p.m.-8 p.m.
• Instructor: Michael Neuman
  Info
• 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
• M 6:10 p.m.-8 p.m.
• 3 points

Oral History & Justice: Memory, Power, a

OH and Justice

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
• M 2:10 p.m.-4 p.m.
• Instructor: Suchitra Vijayan
  Info
• 4 points

SYSTEMIC RISK

A study of financial, economic, and engineering disasters from a common systems engineering perspective, to better understand and model risk in complex process systems. Course topics will introduce process systems engineering concepts and tools such as digraphs, fault trees, probabilistic risk assessment, HAZOP, FMEA, etc., for modeling enterprise-wide risk. We will develop risk models and analyze them for real-life inspired examples and case studies. Several disaster case studies will also be discussed.

• Department: Enterprise Risk Management
• F 10:10 a.m.-noon
• Instructor: Venkat Venkatasubramanian
  h-index: 58 Info
• 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
• R 8:10 p.m.-10 p.m.
• None None-None
• Instructor: Javid B Huseynov
  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
• T 6:10 p.m.-8 p.m.
• M 8:10 p.m.-10 p.m.
• T 8:10 p.m.-10 p.m.
• R 6:10 p.m.-8 p.m.
• Instructor: Shahryar Shaghaghi
  Info
• Instructor: Mike Wilkes
  Info
• Instructor: Thomas Kartanowics
  Info
• 3 points

CYBERSECURITY RISK MANAGEMENT

TBA

• Department: Enterprise Risk Management
• W 6:10 p.m.-8 p.m.
• Instructor: Corey Hirsch
  Info
• 3 points

THIRD-PARTY RISK MANAGEMENT

As organizations increasingly rely on external vendors and service providers, managing third-party risks becomes paramount to ensure operational resilience, regulatory compliance, and strategic success. Challenges include:


The evolving nature of technology risks.

The impact of geopolitical tensions.

The lessons learned from disruptive events like pandemics.


By offering a comprehensive curriculum covering everything from the basics of vendor management to advanced predictive TPRM models and emphasizing regulatory requirements specific to the financial services sector, the course equips professionals with the knowledge and tools needed to navigate the intricate web of third-party relationships. 
Students taking this course are prohibited from taking Supply Chain Risk Management for Non-Financials (ERMC PS5585) at any time. Contact your advisor for more information.

• Department: Enterprise Risk Management
• R 6:10 p.m.-8 p.m.
• Instructor: Rod Aday
  Info
• 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
• M 8:10 a.m.-10 a.m.
• Instructor: Muhammed Bulut
  Info
• 3 points

DIGITAL SPORTS MEDIA & MARKETING

• Department: Sports Management
• W 4:10 p.m.-6 p.m.
• Instructor: Tom Richardson
  Info
• 3 points

SUPPLY CHAIN RISK MANAGEMENT FOR NON-FIN

SUPPLY CHAIN RM FOR NON-F

The Pandemic made us all aware of the fragility of supply chains and how significant the consequences of failure of our supply chains can be. It is paramount to note that global and local economies can break down, and scarcity of essential resources can foment wars. Risk professionals must know what best practices bring security to supply chains and related companies, governments, and other institutions. 
Students taking this course are prohibited from taking Third-Party Risk Management (ERMC PS5575) at any time. Contact your advisor for more information.

• Department: Enterprise Risk Management
• R 6:10 p.m.-8 p.m.
• Instructor: Dan Cahill
  Info
• 3 points

STRATEGY AND ANALYTICS

n/a

• Department: Applied Analytics
• W 6:10 p.m.-8 p.m.
• M 6:10 p.m.-8 p.m.
• T 8:10 p.m.-10 p.m.
• F 2:10 p.m.-4 p.m.
• F 10:10 a.m.-noon
• R 6:10 p.m.-8 p.m.
• R 8:10 p.m.-10 p.m.
• Instructor: Ron Papka
  Info
• Instructor: Arunava Bhattacharyya
  Info
• Instructor: Ranjit Kumble
  Info
• Instructor: Peter Went
  Info
• Instructor: Lisa Godek
  Info
• Instructor: Ashoke S Bhattacharjya
  Info
• Instructor: Huan Liu
  Info
• 3 points

STRATEGY AND ANALYTICS

STRATEGY AND ANALYTICS ONLINE

n/a

• Department: Applied Analytics
• T 6:10 p.m.-8 p.m.
• Instructor: David Kreutter
  Info
• 3 points

CANCER BIOETHICS

Cancer is the second leading cause of death in the US and most affluent countries. It represents a complex group of
diseases with heterogeneity in biology, incidence, prevalence, and outcomes. And unlike other complex and
potentially life-threatening maladies, a cancer diagnosis is regularly received by patients as a death sentence. It
elicits dread, despair, hopelessness, and sometimes stigma in patients and their families. The emotional toll of a
cancer diagnosis and vulnerability that ensues, can profoundly impact patients’ approaches to research and
treatment.
The Cancer Bioethics course offers a comprehensive exploration of the diverse ethical issues that arise in the context
of cancer care and research, providing students with an analytic framework for complex moral dilemmas faced by
patients, clinicians, researchers, and policymakers. Through case studies, theoretical discussions, and critical
analysis, students will employ key bioethical principles and their application to cancer care - from diagnosis to
survivorship or decisions about end-of-life care. Topics will include unique ethical challenges in oncology such as
the “right not to know” and other challenges associated with disclosures of genomic testing of cancer; the moral
uncertainty of conducting research in minors diagnosed with cancer and the downstream use of their oncologic data;
the unbridled optimism and therapeutic illusion among a number of cancer specialists; therapeutic misconception
among participants in oncologic clinical trials; and the increasing integration of artificial intelligence into new
models of cancer care and prognostication. The course will also examine the intersection of social determinants of
health, cultural beliefs and value systems, and healthcare access, addressing the ethical concerns of disparities in
cancer care.
The course incorporates curricular elements of clinical ethics, law and bioethics, research ethics, reproductive ethics,
pediatric ethics, global ethics, environmental ethics, race and bioethics, organ transplant ethics, health policy and
bioethics, etc. This is an elective course open to cross-registrants from other fields and/or Columbia University
programs. Students do not need to have medical background to take this course. Students pursuing a degree in
medicine or pre-medicine, nursing, behavioral sciences, biotechnology, genetics, data science, public policy,
journalism, law, and governme

• Department: Bioethics
• M 5:45 p.m.-7:45 p.m.
• 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 6:10 p.m.-9 p.m.
• Instructor: Mark Truss
  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
• TR 10 a.m.-12:40 p.m.
• Instructor: Dana N Buhl
  Info
• 3 points

AI & PHOTOGRAPHY

As far back as Walter Benjamin’s “work of art in the age of mechanical reproduction” (1935), photography has always been challenged by
mechanical means
of image processing. Photographers and Institutions have first resisted and then (mostly) embraced each of these changes. This class explores Artificial Intelligence Photography as the latest in a series of earthquakes in the history of the photographic image, accompanying the desires of business, globalization, and science. This class seeks an ethically guided, globally representative model for photography and artificial intelligence. Debates around authorship and creativity (e.g., Supreme Court case with Andy Warhol) now face a radically new context of an “authorless” photograph. As crowdsourced imagemaking begins, the bias of massive datasets have taken techno-utopians by surprise, underlining that the task of building an equitable image-bank of the world cannot be left to algorithms and entrepreneurs. This class will explore the ethics and aesthetics of Artificial Intelligence and Imagery. There will be equal emphasis on reading and writing papers, as there will be on learning new software and tools.

• Department: Visual Arts
• MW 1:10 p.m.-3:40 p.m.
• 3 points

PROPERTY/CASUALTY

PROPERTY & CASUALTY

• Department: Actuarial Science
• W 6:10 p.m.-8:55 p.m.
• Instructor: Mark Hoffmann
  Info
• 3 points

SOLAR PROJECT DEVELOPMENT

TBA

• Department: Sustainability Management
• W 6:10 p.m.-8 p.m.
• Instructor: Daniel J Giuffrida
  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

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 10:10 a.m.-12:40 p.m.
• 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
• TR 6:10 p.m.-7:25 p.m.
• Instructor: Dobrin Marchev
  Info
• 3 points

Advanced Darkroom Photography

Advanced analog photography & darkroom printing. Students will work with analog cameras and learn how to refine black-and-white printing techniques, produce larger prints, etc. Emphasis will be placed on the editing, sequencing, and display of images while cultivating a theoretical and historical context to situate the work. Students will engage with an array of photographic practices through presentations, critiques, guest artist lectures and printing assignments. This course will explore critical issues in contemporary photography and advanced camera and production techniques through regular shooting assignments, demonstrations, critique, lectures, readings, and field trips. Prerequisites:
Intro Darkroom Photography
(Columbia) or equivalent experience

• Department: Visual Arts
• MW 10:10 a.m.-12:40 p.m.
• Instructor: Delphine A Fawundu
  Wikipedia Info
• 3 points

Electrification and Decarbonization

Electricity is the lifeblood of human society. Decarbonization of global economies through electrification is seen as the most viable path for reducing GHG emissions and addressing the worst effects of climate change. Though generally accepted as the best path forward, an understanding of the operational parameters of the electric system is essential to understanding both the benefits and limitations of current and future actions. This includes the highly visible investments in renewable energy generation, less visible but equally important investments in transmission and distribution infrastructure, and the largely personal, private choices of individual households and businesses.

The course will examine pathways for the transition from fossil fuel-based electricity generation to one dominated by electricity generated by renewable energy. Students will examine the drivers of past energy transitions and various factors influencing current energy systems. At the conclusion of the course, students will be able understand the drivers of past energy transitions, the impact of those drivers on the overall energy supply chain, and how new technologies (e.g. distributed energy resources, smart meters, internet of things (IOT), etc.), consumer adoption of mass market products (e.g. Electric Vehicles, battery backup, etc,) and evolving consumer expectations (e.g. fast charging) are altering long held assumptions about energy production and use. Through this work, students will be able to infer practical steps to support current efforts to decarbonize and the potential impacts of those actions on the modern energy supply chain.

• Department: Sustainability Management
• T 6:10 p.m.-8 p.m.
• Instructor: Rory Christian
  Info
• 3 points

FINANCIAL ECONOMICS

• Department: Economics
• R 6:10 p.m.-8:40 p.m.
• Instructor: Arkaprava Bandyopadhyay
  Info
• 3 points

Business and Climate Change: Law, Policy

This course will provide students with an understanding of the ways and extent to which climate change law and policy is relevant to businesses, as well as the role of sustainability professionals in practical implementation. The course is divided into several core topics, including: (i) an overview of international and U.S. climate change policy and law, including the Paris Agreement, the Inflation Reduction Act and energy transition policy support, and human rights/environmental justice; (ii) market-led, voluntary initiatives such as the Task Force on Climate-related Financial Disclosures (TCFD), and related developments including the mainstreaming of ESG investing, sustainable finance, and the proliferation of corporate net zero goals; (iii) corporate governance, shareholder activism, and the emergence of mandatory regulation on climate disclosures, such as the E.U.’s Taxonomy Regulation and the U.S. Securities & Exchange Commission’s proposed climate disclosure rule; (iv) carbon pricing, carbon markets, and “offsets”; (v) greenhouse gas emissions accounting and data challenges; and (vi) climate-related litigation and enforcement actions against corporations and financial institutions in the U.S. and other key markets, including “greenwashing” litigation and “anti- energy company boycott” investigations by several U.S. states.

• Department: Sustainability Management
• T 4:10 p.m.-6 p.m.
• 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
• W 6:10 p.m.-8 p.m.
• Instructor: Scott A Spencer
  Info
• 3 points

Topics in Nursing Practice

TOPICS IN NURSING PRACTIC

This course is designed to introduce pre-licensure students to relevant and emergent topics which affect the practice of nursing in the national and international healthcare system. The focus will be on issues confronting professional nurses including global health, cultural awareness, gender identity, and evidence-based wellness. State mandated topics for licensure will be covered.

• Department: Nursing
• 0 points

VISITING ARTIST LECT. II

Visiting artists and critics are invited over the course of the academic year to give a one-hour lecture followed by discussion, and conduct 3 40-minute studio visits. These lecturers will join the previously listed Visiting Critics and will be available as one of your allotted studio visits each semester.

• Department: Visual Arts
• Instructor: Miya Masaoka
  Wikipedia Info
• 0 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.

• Department: Actuarial Science
• TR 8:40 a.m.-9:55 a.m.
• Instructor: Yubo Wang
  Info
• 3 points

Finance and Sustainability

• Department: Finance
• MTWRF 9 a.m.-6:30 p.m.
• Instructor: Faculty
  h-index: 37 Info
• 3 points

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 10:10 a.m.-11:25 a.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 1:10 p.m.-2:25 p.m.
• Instructor: Regina Dolgoarshinnykh
  CULPA: 2 Info
• 3 points

Building a Scalable Business

• Department: Business
• MTWRF 9 a.m.-5 p.m.
• Instructor: Faculty
  h-index: 37 Info
• 3 points

CRIT ISSUES IN ART II

N/A

• Department: Visual Arts
• T 3 p.m.-5 p.m.
• Instructor: Luba Drozd
  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.

• Department: Actuarial Science
• TR 2:40 p.m.-3:55 p.m.
• Instructor: Lina Xu
  Info
• 3 points

DATA SCIENCE IN FINANCE AND INSURANCE

DATA SCIENCE IN FIN & INSURANC

The Data Science in Finance and Insurance course explores machine learning models, their theoretical basis, computing implementation and applications in finance and insurance.  Topics include 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.  Prior exposure to linear algebra, calculus and statistics is helpful.  A working knowledge of a spreadsheet program and R is a plus.  Students will use spreadsheets and R for validation and prototyping and Python to implement algorithms and apply models to applicable data. 

Some topics covered are also 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.

• Department: Actuarial Science
• TR 10:10 a.m.-11:25 a.m.
• Instructor: Yubo Wang
  Info
• 3 points

GRADUATE STUDIO II

N/A

• Department: Visual Arts
• Instructor: Miya Masaoka
  Wikipedia Info
• 3 points

GRADUATE STUDIO II

• Department: Visual Arts
• M 4 p.m.-10 p.m.
• Instructor: Miya Masaoka
  Wikipedia Info
• 3 points

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 object-oriented programming with python, the relational theory and relational database navigation with SQL, deep learning (including traditional machine learning tasks, computer vision, recurrent networks, natural language processing, and large language models), as well as interpretable machine learning. 

Some topics covered are relevant to the Advanced Topics in Predictive Analytics (ATPA) exam of the Society of Actuaries.  Topics in deep learning are also relevant to the statistical learning portion of the Casualty Actuarial Society (CAS) curriculum, and the quantitative methods section of the Chartered Financial Analyst (CFA) Institute curriculum.

Familiarity with machine learning models covered in the Data Science in Finance and Insurance (ACTU PS5841) course is helpful.  Prior exposure to linear algebra, calculus, statistics, and a working knowledge of python and spreadsheets are necessary.

• Department: Actuarial Science
• TR 2:40 p.m.-3:55 p.m.
• Instructor: Yubo Wang
  Info
• 3 points

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.
• Instructor: Leland Wei
  Info
• 3 points

THESIS DEVELOPMENT SEMINAR

Prerequisites: EEEB G4850. 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 students stay at E3B and will help in their future careers.

• Department: Ecology, Evolution, and Environmental Biology
• W 10:10 a.m.-noon
• Instructor: Bekka Brodie
  Info
• 3 points

Grad Sem II in Sound Art & Related Media

The Graduate Seminar in Sound Art and Related Media is designed to create a space that is inclusive yet focused on sound as an art form and a medium. Class time is structured to support, reflect, and challenge students as individual artists and as a community. The course examines the medium and subject of sound in an expanded field, investigating its constitutive materials, exhibition and installation practices, and its ethics in the 21st century. The seminar will focus on the specific relations between tools, ideas, and meanings and the specific histories and theories that have arisen when artists engage with sound as a medium and subject in art. The seminar combines discussions of readings and artworks with presentations of students' work and research, as well as site visits and guest lectures.

 

While the Columbia Visual Arts Program is dedicated to maintaining an interdisciplinary learning environment where students are free to use and explore different mediums while also learning to look at, and critically discuss, artwork in any medium, we are equally committed to providing in-depth knowledge concerning the theories, histories, practices, tools and materials underlying these different disciplines. We offer Graduate Seminars in different disciplines, or combinations of disciplines, including moving image, new genres, painting, photography, printmaking, sculpture, as well as in Sound Art in collaboration with the Columbia Music Department through their Computer Music Center. These Discipline Seminars are taught by full-time and adjunct faculty, eminent critics, historians, curators, theorists, writers, and artists.

• Department: Visual Arts
• Instructor: Miya Masaoka
  Wikipedia Info
• 3 points

GROUP CRITIQUE II

• Department: Visual Arts
• W 5:30 p.m.-9:30 p.m.
• 3 points

ARTIST MENTORSHIP / VALS II

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 p.m.
• Instructor: Miya Masaoka
  Wikipedia 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.

• Department: Actuarial Science
• TR 4:10 p.m.-6 p.m.
• Instructor: Lina Xu
  Info
• 2 points

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
• T 6:10 p.m.-8 p.m.
• W 8:10 p.m.-10 p.m.
• Instructor: Xue Bai
  Info
• Instructor: David P McCarthy
  Wikipedia Info
• 3 points

SOUTH ASIA RESEARCH SEMINAR

• Department: Graduate School of Arts and Sciences
• Instructor: Syantani Chatterjee
  Info
• 2-4 points

CAPSTONE: NARRATIVE MEDICINE SCHOLARSHIP

CAPSTONE: NMED SCHOLARSHI

The Narrative Medicine Scholarship Capstone advances students’ scholarly writing on a topic of their choosing in a
dynamic collaborative working group setting. Given the interdisciplinary nature of the field, there are a range of
subjects, methodological approaches, and writing styles, and we will discuss the best approach to to each paper. We
can take up participants’ manuscripts at any stage of the writing process—from scratch, or a paper from another
course or context that you’d like to develop further. You do not have to have a set idea of which writing project you
wish to develop in advance; we can brainstorm together. All students will give and receive constructive feedback to
one another, and all students will receive instructor suggestions/comments/edits throughout the semester as well.

• Department: Narrative Medicine
• T 6:10 p.m.-8 p.m.
• 3 points

DATA SCIENCE CONSULTING

DATA SCIENCE CONSULTING ONLINE

• Department: Applied Analytics
• R 8:10 p.m.-10 p.m.
• Instructor: David Shilane
  Info
• 3 points

CREATIVE CAPSTONE SEMINAR

This capstone seminar supports M.S. students as they develop and complete a substantial creative project in their chosen form (e.g., memoir, fiction, poetry, playwriting, dance, visual, graphic narrative, video, multimedia, hybrid work). Through workshops emphasizing both individual process and community exchange, students will receive peer support and critique. Readings will include essays on creative process and theory and will be assigned as relevant to the individual projects within the group.  Students consider matters of process, form, audience, embodiment, and artistic responsibility.  In addition to the final project, students will be asked to keep a process journal, occasionally to post to prompts on Courseworks, and to provide a short artist’s statement that situates your work within personal, disciplinary, and social frameworks.

• Department: Narrative Medicine
• W 2:10 p.m.-4 p.m.
• 3 points

Equity Investment Management

• Department: Finance
• MTWRF 9 a.m.-6:30 p.m.
• Instructor: Faculty
  h-index: 37 Info
• 3 points

INTERNATIONAL ECONOMICS

• Department: Economics
• M 6:10 p.m.-8 p.m.
• Instructor: Sebastian Heise
  Info
• 3 points

INDEPENDENT STUDY

N/A

• Department: Visual Arts
• Instructor: Seth Cluett
  h-index: 4 Info
• Instructor: Miya Masaoka
  Wikipedia Info
• 1-6 points

INDEPENDENT STUDY

• Department: Visual Arts
• Instructor: Daphne Arthur
  Info
• 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: Miya Masaoka
  Wikipedia Info
• Instructor: Naeem Mohaiemen
  Wikipedia h-index: 8 Info
• Instructor: Kenny Rivero
  Info
• Instructor: Shelly Silver
  CULPA: 1 Info
• Instructor: Sable Smith
  Wikipedia Info
• Instructor: Rirkrit Tiravanija
  Wikipedia Info
• Instructor: Tomas Vu Daniel
  CULPA: 4 Info
• 1-3 points

Banking & Monetary Policy in the Global

• Department: Business Economics
• MTWRF 9 a.m.-6:30 p.m.
• Instructor: Faculty
  h-index: 37 Info
• 3 points

SUPERVISED INTERSHIP IN SPORTS MANAGEMEN

• Department: Sports Management
• Instructor: Kelly Carroll
  Info
• 3 points

SPECIAL TOPICS: Getting AI Right for Tec

AI + Techno-Social Change

OVERVIEW:

Artificial Intelligence (AI) is rapidly transforming every sector of human activity—from healthcare and education to defense and diplomacy—presenting both risks and opportunities and raising urgent questions about ethics, governance, global stability, and even civilizational churn. This course explores AI as both a technological and geopolitical force, one that has considerable societal impact in reshaping institutions, economies, and even civilization itself. Rather than approaching the topic from a technical perspective, the course is designed for students across disciplines who seek to understand how AI and convergent technologies can be developed and governed responsibly in a time of unprecedented change.

 

CONTENT:

Students will examine AI not as a set of tools but as an evolving infrastructure of global order. The course integrates ethical reasoning, policy analysis, and systems thinking to investigate how to get AI right in an era of rapid techno-social transformation. Students will explore foundational frameworks for AI governance and regulation (e.g., EU AI Act); analyze how the convergence of emerging technologies drives innovation and reshapes industries (e.g., autonomous vehicles); understand the role of AI within the larger context of human innovation; debate the rise of sovereign AI as a geopolitical force (e.g., UAE’s ‘Falcon’, Singapore’s ‘SEA-LION’ models.); evaluate AI’s potential role in both civilizational collapse and societal resilience; and apply methods of strategic foresight to anticipate future emerging technologies (e.g., the work of Sachs, Acemoglu & Robinson, Spengler etc.). Final projects will synthesize technical, ethical, and geopolitical perspectives to design a Responsible AI framework relevant to students’ personal and professional contexts.

 

LOGISTICS:

The course meets once per week in person. No technical background or coding experience is required. Open to all Columbia University graduate students.

• Department: Information & Knowledge Strat
• M 6:10 p.m.-8 p.m.
• 3 points

Human-Centered Design for Data-Driven St

HumCenDesignForDDStrat+De

Within this course, students will explore how practices from human-centered design (HCD) can be applied to the end-to-end data science workflow—problem (use case) definition, data collection & preparation, data exploration, data modeling, and communicating and visualizing the results— in order to build trust in data that is used to drive strategy and decision making and impact organizational change. Students will learn about fundamental human values and how methods from the behavioral sciences and HCD can inspire ethical use of data to drive strategy and change in the modern, data-driven workplace. Students will understand how keeping “humans in the loop” is beneficial, and they will develop a critical eye for assessing whether the data they rely on to make decisions at work is human-centric, particularly as we become more reliant on data science and artificial intelligence (AI) technologies to inform our insights, strategies, and decision making at work.

Content & Goals: Through hands-on, project-based work, students will work individually and in project teams to practice designing human-centric information and communication experiences, leveraging audience-focused data visualizations and storytelling techniques to drive a strategic workplace objective, motivating leaders and employees into action to create traceable organizational impact that benefits people. Students will have an opportunity to practice their writing and presentation skills through practical course assignments.

Logistics: This graduate-level elective course is designed for students in Information & Knowledge Strategy but is open to other students at Columbia University. This course would be relevant to students studying management and technology more broadly. The course will be delivered in person on Columbia’s campus during the spring semester.

No prerequisites.

• Department: Information & Knowledge Strat
• T 6:10 p.m.-8 p.m.
• Instructor: Blake Dicosola
  Info
• 3 points

NAVIGATING THE FUTURE OF WORK

This course is designed to provide an understanding of the critical capabilities necessary for individual, team, and organizational success in the new world of work. Based upon current economic models, students will recognize the intangible factors within teams and organizations that drive decision making, knowledge, and culture as value and valuation of the work of organizations.

Our core question is, how to start, build, and sustain leadership and organization capabilities for successfully navigating the future of work? The course will answer this question by looking at successful case examples who are demonstrably leading the way. We will bring actual leaders and entrepreneurs to the class for exchange with our class. The course will require students to work individually and in teams to build their own future of work models through unlearning and learning.

Students will study modern exemplar organizations and leaders to harness their lessons for staying competitive and successful. We will explore the changing nature of work, provide the means for better understanding what is occurring, and develop strategies for successfully navigating this new world. This course will start by analyzing how platforms, robotics, AI, automation, data, digitization, and the speed of technology has changed work. The capabilities necessary for success require both technological expertise, as well as, human skill centered around leadership, knowledge, and cultures of trust, respect and intentional inclusion. Students will participate in an “intangibles” assessment survey that will measure behaviors associated with leadership, culture, and knowledge for driving performance. This approach allows for exploring how the intangible factors behind each of these change factors impact the world of work, workforces, and workplaces.

Assignments will include determining individual work interests, skills and connecting them to organizational objectives and key results (OKR). Students will work in teams to design a future of work map and negotiate practices for their current organizations and clients.

• Department: Information & Knowledge Strat
• MTWRF 9 a.m.-5 p.m.
• 3 points

APPLIED WRITING

• Department: Narrative Medicine
• R 3:10 p.m.-6 p.m.
• 4 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 2:10 p.m.-4 p.m.
• Instructor: Grant J Son
  Info
• 3 points

SUPERVISED PROJECT IN SPORTS MANAGEMENT

NFL SOCL JUSTC IMPACT PRACTICE

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
• W 12:10 p.m.-2 p.m.
• Instructor: Leonard Elmore
  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
• W 4:10 p.m.-6 p.m.
• Instructor: Leonard Elmore
  Info
• 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.-1:30 p.m.
• Instructor: Arthur Kuflik
  Info
• 1 points

MASTER THESIS (2 CREDIT)

Thesis requirement for Bioethics program. Taken with the Thesis Workshop (BIET K5991).

• Department: Bioethics
• Instructor: Arthur Kuflik
  Info
• 2 points

PERSP ON INT'L & WORLD HISTORY

This course gives students the opportunity to design their own curriculum: To attend lectures, conferences and workshops on historical topics related to their individual interests throughout Columbia University. Students may attend events of their choice, and are especially encouraged to attend those sponsored by the History Department. The Center for International History and the Heyman Center for the Humanities have impressive calendars of events and often feature historians. The goal of this mini-course is to encourage students to take advantage of the many intellectual opportunities throughout the University, to gain exposure to a variety of approaches to history, and at the same time assist them in focusing on a particular area for their thesis topic.

• Department: History
• Instructor: Line Lillevik
  Info
• 1-3 points

A Systems Approach to Knowledge Manageme

A Systems Approach to KM

This course will equip students with skills and strategies on how to plan, design, develop and deploy knowledge management programs for different types of organizations as well as for different sectors of the global economy. A hallmark of the course’s approach is that students will learn the steps from planning to deployment from a systems standpoint, i.e., students will learn how to use systems engineering principles as an analytic and structured framework for designing and implementing knowledge management programs that are responsive to organizational needs. 

The course first provides an overview of the strategic value of institutional and project knowledge when properly managed, shared and applied, or leveraged to support decision making.  Next, a system’s view and analysis of knowledge management (KM) is introduced as critical to business success because of the strategic value of knowledge assets. The knowledge management “system” as used in this course comprises of all the organizational elements that go into formulating a knowledge management strategy and its related implementation programs. Such system is made up of a defined KM strategy, appropriate information technology (IT) tools, processes, teams and leadership engagements, implementation programs delivery, institutional learning, lessons learned, knowledge sharing and transfer methodologies. Further, students will learn how to conduct organizational KM needs assessment, define institutional KM drivers, strategy formulation and knowledge sharing protocols. Students will also acquire skills for developing robust knowledge management practices and programs that support business objectives, enable project success, and sustain improved organizational performance. Additionally, students will apply the structured KM design principles they learned to real-world organizational challenges and opportunities. Assignments comprise a combination of individual exercises, a group project, and a final exam.

Pre-requisites/open to:
There is no pre-requisite knowledge or specific competency required for taking this course, because the instruction will include knowledge management fundamentals as well as systems engineering basics. Open to SPS and SEAS; other students with instructor permission.

• Department: Information & Knowledge Strat
• M 8:10 p.m.-10 p.m.
• Instructor: Moses Adoko
  Info
• 3 points

Independent Study

This course offers students an opportunity to expand their curriculum beyond the established course offerings. Interested parties must consult with the QMSS Program Director before adding the class. This course is intended for personal projects that require a lower time commitment (1-3 hours/week) than a typical independent study and may be taken for 1 point. This class is Pass/Fail option only.

• Department: Quantitative Methods/Social Sciences
• Instructor: Gregory M Eirich
  CULPA: 1 Info
• 1 points

Internship In ACTU

This course requires you to experience firsthand a program-related job in a real working environment. You will engage in personal, environmental and organizational reflection. The ideal Internship will provide you an opportunity to gain tangible and practical knowledge in your chosen field by taking on a position that is closely aligned with your coursework and professional interests. Before registering for this course, you must have completed the Internship Application Form in which you will describe your internship sponsor and provide details about the work that you will be doing. This form must be signed by your internship supervisor and approved by your program director BEFORE you register for this course.

 

To receive instructor approval, the internship:

● Must provide an opportunity for the student to apply course concepts, either at the organizational or team level

● Must fit into the planned future program-related career path of the student

You must identify your own internship opportunities. The internship must involve a commitment to completing a minimum of 210 hours over the semester.

 

At the end of your course, you will submit an evaluation form to your internship supervisor. The evaluation form should be returned directly to the instructor

• Department: Actuarial Science
• Instructor: Robin Wang
  Info
• 3 points

Internship In APAN

This course requires you to experience firsthand a program-related job in a real working environment. You will engage in personal, environmental and organizational reflection. The ideal Internship will provide you an opportunity to gain tangible and practical knowledge in your chosen field by taking on a position that is closely aligned with your coursework and professional interests. Before registering for this course, you must have completed the Internship Application Form in which you will describe your internship sponsor and provide details about the work that you will be doing. This form must be signed by your internship supervisor and approved by your program director BEFORE you register for this course.

 

To receive instructor approval, the internship:

● Must provide an opportunity for the student to apply course concepts, either at the organizational or team level

● Must fit into the planned future program-related career path of the student

You must identify your own internship opportunities. The internship must involve a commitment to completing a minimum of 210 hours over the semester.

 

At the end of your course, you will submit an evaluation form to your internship supervisor. The evaluation form should be returned directly to the instructor

• Department: Applied Analytics
• Instructor: Robin Wang
  Info
• 3 points

Internship In CNAD

This course requires you to experience firsthand a program-related job in a real working environment. You will engage in personal, environmental and organizational reflection. The ideal Internship will provide you an opportunity to gain tangible and practical knowledge in your chosen field by taking on a position that is closely aligned with your coursework and professional interests. Before registering for this course, you must have completed the Internship Application Form in which you will describe your internship sponsor and provide details about the work that you will be doing. This form must be signed by your internship supervisor and approved by your program director BEFORE you register for this course.

 

To receive instructor approval, the internship:

● Must provide an opportunity for the student to apply course concepts, either at the organizational or team level

● Must fit into the planned future program-related career path of the student

You must identify your own internship opportunities. The internship must involve a commitment to completing a minimum of 210 hours over the semester.

 

At the end of your course, you will submit an evaluation form to your internship supervisor. The evaluation form should be returned directly to the instructor

• Department: Construction Administration
• Instructor: Robin Wang
  Info
• 3 points

Internship In ERM

This course requires you to experience firsthand a program-related job in a real working environment. You will engage in personal, environmental and organizational reflection. The ideal Internship will provide you an opportunity to gain tangible and practical knowledge in your chosen field by taking on a position that is closely aligned with your coursework and professional interests. Before registering for this course, you must have completed the Internship Application Form in which you will describe your internship sponsor and provide details about the work that you will be doing. This form must be signed by your internship supervisor and approved by your program director BEFORE you register for this course.

To receive instructor approval, the internship:

● Must provide an opportunity for the student to apply course concepts, either at the organizational or team level

● Must fit into the planned future program-related career path of the student

You must identify your own internship opportunities. The internship must involve a commitment to completing a minimum of 210 hours over the semester.

 

At the end of your course, you will submit an evaluation form to your internship supervisor. The evaluation form should be returned directly to the instructor

• Department: Enterprise Risk Management
• Instructor: Robin Wang
  Info
• 3 points

Internship In HCM

This course requires you to experience firsthand a program-related job in a real working environment. You will engage in personal, environmental and organizational reflection. The ideal Internship will provide you an opportunity to gain tangible and practical knowledge in your chosen field by taking on a position that is closely aligned with your coursework and professional interests. Before registering for this course, you must have completed the Internship Application Form in which you will describe your internship sponsor and provide details about the work that you will be doing. This form must be signed by your internship supervisor and approved by your program director BEFORE you register for this course.

 

To receive instructor approval, the internship:

● Must provide an opportunity for the student to apply course concepts, either at the organizational or team level

● Must fit into the planned future program-related career path of the student

You must identify your own internship opportunities. The internship must involve a commitment to completing a minimum of 210 hours over the semester.

 

At the end of your course, you will submit an evaluation form to your internship supervisor. The evaluation form should be returned directly to the instructor

• Department: Human Capital Management
• Instructor: Robin Wang
  Info
• 3 points

Internship In IKNS

This course requires you to experience firsthand a program-related job in a real working environment. You will engage in personal, environmental and organizational reflection. The ideal Internship will provide you an opportunity to gain tangible and practical knowledge in your chosen field by taking on a position that is closely aligned with your coursework and professional interests. Before registering for this course, you must have completed the Internship Application Form in which you will describe your internship sponsor and provide details about the work that you will be doing. This form must be signed by your internship supervisor and approved by your program director BEFORE you register for this course.

 

To receive instructor approval, the internship:

● Must provide an opportunity for the student to apply course concepts, either at the organizational or team level

● Must fit into the planned future program-related career path of the student

You must identify your own internship opportunities. The internship must involve a commitment to completing a minimum of 210 hours over the semester.

 

At the end of your course, you will submit an evaluation form to your internship supervisor. The evaluation form should be returned directly to the instructor

• Department: Information & Knowledge Strat
• Instructor: Robin Wang
  Info
• 3 points