Internship Experience 2 - SUSC
Internship in SUSC
This asynchronous, 1.5-credit elective combines a supervised professional internship with guided analysis of workplace culture, ethics, and feedback practices. Students evaluate organizational values, inclusivity, and ethical decision-making while developing the skills needed to navigate professional environments and identify the workplace cultures in which they will thrive.
- Department: Sustainability Science
- 1.5 points
Internship Experience 2 - TMGT
This asynchronous, 1.5-credit elective combines a supervised professional internship with guided analysis of workplace culture, ethics, and feedback practices. Students evaluate organizational values, inclusivity, and ethical decision-making while developing the skills needed to navigate professional environments and identify the workplace cultures in which they will thrive.
- Department: Technology Management
- 1.5 points
APPLIED GENERATIVE AI
This course offers a comprehensive introduction to a branch of machine learning called generative modeling, focusing on the underlying concepts, theoretical techniques, and practical applications. The defining property of Generative AI models is their ability to generate new data similar to a given dataset. In recent years, Generative AI has seen rapid advancement, revolutionizing various industries by enabling machines to create realistic and novel content, ranging from images, videos, and music to text and complex simulations.
Students will learn to use, fine-tune, and programmatically interface with high-level APIs and open-source foundational models, allowing them to leverage state-of-the-art tools in Generative AI. Additionally, the course delves into the theory and practice of low-level implementations, empowering students to train their own models on their own data and understand these models from first principles. The course covers various types of generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformers with their applications to text, image, audio, and video generation.
By combining these approaches, this course provides a robust foundation in both the practical application and deep theoretical knowledge required to develop innovative AI solutions.
- Department: Applied Analytics
- 3 points
APPLIED GENERATIVE AI
This course offers a comprehensive introduction to a branch of machine learning called generative modeling, focusing on the underlying concepts, theoretical techniques, and practical applications. The defining property of Generative AI models is their ability to generate new data similar to a given dataset. In recent years, Generative AI has seen rapid advancement, revolutionizing various industries by enabling machines to create realistic and novel content, ranging from images, videos, and music to text and complex simulations.
Students will learn to use, fine-tune, and programmatically interface with high-level APIs and open-source foundational models, allowing them to leverage state-of-the-art tools in Generative AI. Additionally, the course delves into the theory and practice of low-level implementations, empowering students to train their own models on their own data and understand these models from first principles. The course covers various types of generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformers with their applications to text, image, audio, and video generation.
By combining these approaches, this course provides a robust foundation in both the practical application and deep theoretical knowledge required to develop innovative AI solutions.
- Department: Applied Analytics
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Instructor:
Prachi Patel
h-index: 3 Info -
Instructor:
Gurgen Hayrapetyan
Info - 3 points
SPORTS MARKETING
- Department: Sports Management
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Instructor:
Grant J Son
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Instructor:
Thomas Cerny
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Instructor:
Laurajean Holmgren
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
- 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
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Instructor:
Leslie Weinstein
Info - 3 points
INFORMATION TECHNOLOGY RISK MANAGEMENT
IT RISK MANAGEMENT
Students will learn how to better identify and manage a wide range of IT risks as well as better inform IT investment decisions that support the business strategy. Students will develop an instinct for where to look for technological risks, and how IT risks may be contributing factors toward key business risks. This course includes a review of IT risks, including those related to governance, general controls, compliance, cybersecurity, data privacy, and project management. Students will learn how to use a risk-based approach to identify and mitigate cybersecurity and privacy related risks and vulnerabilities. No prior experience or technical skills required to successfully complete this course.
- Department: Enterprise Risk Management
- 3 points
SPORTS BUSINESS COMMUNICATIONS & PR
SPORTS BUSINESS COMM & PR
- Department: Sports Management
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Instructor:
Joseph Favorito
Info - 3 points
CYBERSECURITY RISK MANAGEMENT
Cyber losses, reflected in daily headlines on data breaches, state-sponsored attacks on critical infrastructure, and ransomware incidents, have grown to exceed other major categories of operating risk in terms of total cost, driving increased regulatory activity in response.
This means risk management professionals need a solid understanding of cyber-risk management programs, techniques, mitigation strategies, architectures, frameworks, and procedures, which this course provides. Some frameworks covered include ISO27001, NIST CSF, CIS 18 Critical Security Controls, etc. Effective management of cyber-risks is an Enterprise-wide activity addressing immediate risks requiring attention while building a mature foundation for a resilient and proactive cybersecurity risk management program; a Technology Risk Management foundation is therefore a prerequisite for enrollment; however, IT expertise is not.
The course provides practical, hands-on, cases and exercises for the application of cyber-risk management principles, equipping course graduates to help lower the probability of a risk event in their organization, and to enhance organizational resilience for effective incident response and recovery.
- Department: Enterprise Risk Management
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Instructor:
Corey Hirsch
Info - 3 points
CYBERSECURITY RISK MANAGEMENT
Cyber losses, reflected in daily headlines on data breaches, state-sponsored attacks on critical infrastructure, and ransomware incidents, have grown to exceed other major categories of operating risk in terms of total cost, driving increased regulatory activity in response.
This means risk management professionals need a solid understanding of cyber-risk management programs, techniques, mitigation strategies, architectures, frameworks, and procedures, which this course provides. Some frameworks covered include ISO27001, NIST CSF, CIS 18 Critical Security Controls, etc. Effective management of cyber-risks is an Enterprise-wide activity addressing immediate risks requiring attention while building a mature foundation for a resilient and proactive cybersecurity risk management program; a Technology Risk Management foundation is therefore a prerequisite for enrollment; however, IT expertise is not.
The course provides practical, hands-on, cases and exercises for the application of cyber-risk management principles, equipping course graduates to help lower the probability of a risk event in their organization, and to enhance organizational resilience for effective incident response and recovery.
- Department: Enterprise Risk Management
- 3 points
THIRD-PARTY RISK MANAGEMENT
As organizations increasingly rely on external vendors and service providers, managing third-party risks becomes paramount to ensure operational resilience, regulatory compliance, and strategic success. Challenges include:
The evolving nature of technology risks.
The impact of geopolitical tensions.
The lessons learned from disruptive events like pandemics.
By offering a comprehensive curriculum covering everything from the basics of vendor management to advanced predictive TPRM models and emphasizing regulatory requirements specific to the financial services sector, the course equips professionals with the knowledge and tools needed to navigate the intricate web of third-party relationships.
Students taking this course are prohibited from taking Supply Chain Risk Management for Non-Financials (ERMC PS5585) at any time. Contact your advisor for more information.
- Department: Enterprise Risk Management
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Instructor:
Shahryar Shaghaghi
Info - 3 points
THIRD-PARTY RISK MANAGEMENT
As organizations increasingly rely on external vendors and service providers, managing third-party risks becomes paramount to ensure operational resilience, regulatory compliance, and strategic success. Challenges include:
The evolving nature of technology risks.
The impact of geopolitical tensions.
The lessons learned from disruptive events like pandemics.
By offering a comprehensive curriculum covering everything from the basics of vendor management to advanced predictive TPRM models and emphasizing regulatory requirements specific to the financial services sector, the course equips professionals with the knowledge and tools needed to navigate the intricate web of third-party relationships.
Students taking this course are prohibited from taking Supply Chain Risk Management for Non-Financials (ERMC PS5585) at any time. Contact your advisor for more information.
- Department: Enterprise Risk Management
- 3 points
ANALYTICS FOR CYBERSECURITY MANAGEMENT
ANALYTICS FR CYBERSECURIT
This course is designed to immerse students in the intersection of cybersecurity and data analytics. The course explores how modern data-driven approaches are revolutionizing the way organizations detect and manage cyber threats. Students will engage deeply with core cybersecurity concepts, such as network security, vulnerability management, and threat intelligence, while also learning to leverage cutting-edge data analytics and artificial intelligence to solve real-world security problems. Through hands-on exercises, coding assignments, and case studies, students will gain practical skills in analyzing logs and telemetries, building detection systems, and applying machine learning to security operations.
- Department: Applied Analytics
- 3 points
ANALYTICS FOR CYBERSECURITY MANAGEMENT
ANALYTICS FR CYBERSECURIT
This course is designed to immerse students in the intersection of cybersecurity and data analytics. The course explores how modern data-driven approaches are revolutionizing the way organizations detect and manage cyber threats. Students will engage deeply with core cybersecurity concepts, such as network security, vulnerability management, and threat intelligence, while also learning to leverage cutting-edge data analytics and artificial intelligence to solve real-world security problems. Through hands-on exercises, coding assignments, and case studies, students will gain practical skills in analyzing logs and telemetries, building detection systems, and applying machine learning to security operations.
- Department: Applied Analytics
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Instructor:
Muhammed Bulut
Info - 3 points
DIGITAL SPORTS MEDIA & MARKETING
DIG SPRTS MEDIA & MRKTG
- Department: Sports Management
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Instructor:
Tom Richardson
Info - 3 points
SUPPLY CHAIN RISK MANAGEMENT FOR NON-FIN
SUPPLY CHAIN RM FOR NON-F
The Pandemic made us all aware of the fragility of supply chains and how significant the consequences of failure of our supply chains can be. It is paramount to note that global and local economies can break down, and scarcity of essential resources can foment wars. Risk professionals must know what best practices bring security to supply chains and related companies, governments, and other institutions.
Students taking this course are prohibited from taking Third-Party Risk Management (ERMC PS5575) at any time. Contact your advisor for more information.
- Department: Enterprise Risk Management
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Instructor:
Dan Cahill
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Instructor:
Bryce Wolf
Info - 3 points
COGNITIVE BIAS & ERM
Explores key concepts of behavioral economics and cognitive psychology, how to identify key cognitive biases in ERM activities, and how to apply techniques to address these, enhancing the quality and integrity of an ERM program. The course also includes best practices in leveraging analytic models to improve decision making.
- Department: Enterprise Risk Management
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Instructor:
Rich Lauria
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Instructor:
Tatiana Dmitrieva
Info - 3 points
STRATEGY AND ANALYTICS
n/a
- Department: Applied Analytics
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Instructor:
Ranjit Kumble
Info - 3 points
STRATEGY AND ANALYTICS
STRATEGY AND ANALYTICS ONLINE
n/a
- Department: Applied Analytics
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Instructor:
Diahanna L Post
Info - 3 points
CAPSTONE
The Capstone Project is an opportunity for students to synthesize and apply learnings from throughout the Strategic Communication program. Under the guidance of expert advisers, you’ll investigate a real-world communication issue, devising solutions and strategies that bridge the gap between theory and practice.
- Department: Strategic Communication
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Instructor:
Mark Truss
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Instructor:
Elyse Hoelzer
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Instructor:
Thomas J Groppe
Info - 3 points
ROLE OF FINANCE IN INSURANCE
- Department: Insurance Management
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Instructor:
Robert Percopo
Info - 3 points
Intro Darkroom Photography
Introductory course to analog photographic tools, techniques, and photo criticism. This class explores black & white, analog camera photography and darkroom processing and printing. Areascovered include camera operations, black and white darkroom work, 8x10 print production, and critique. With an emphasis on the student’s own creative practice, this course will explore the basics of photography and its history through regular shooting assignments, demonstrations, critique, lectures, and readings. No prior photography experience is required.
- Department: Visual Arts
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Instructor:
Matthew E Buckingham
Wikipedia Info -
Instructor:
Dana N Buhl
Info - 3 points
LEADERSHIP, PRESENCE, AND INFLUENCE
LEADERSHIP, PRESENCE, & I
This highly experiential course helps students design, launch, and sustain a successful career. Blending scholarly foundations with practical tools and hands-on coaching, this course guides students through identifying their personal strengths and professional identity, developing a compelling personal pitch, and building the skills needed to navigate interviewing, networking, teamwork, organizational culture and change. Each session integrates theory, applied practice, and structured role-play with peer feedback, enabling students to move beyond a job search mentality toward a proactive, values-aligned approach to career development and long-term professional success.
- Department: Enterprise Risk Management
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Instructor:
David Burkus
Info - 1.5 points
SPORTS FACILITIES & EVENTS MANAGEMENT
SPORT FACILITY & EVENT
- Department: Sports Management
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Instructor:
William Squires
Info - 3 points
INVESTMENT & ALM FOR ACTUARIES
The goal of this elective course is to provide you with a broad understanding of fixed income securities and how they are used for asset liability management (ALM) in financial institutes. This course is designed for individuals who currently work or plan to work as insurance and financial professionals such as actuaries, traders, and quants. The course builds on concepts introduced in several of the program’s core courses and emphasizes the application of theories. The course covers content adapted from the SOA syllabus for fellowship exams and is split into four parts: interest rate risk measurements, interest rate management—ALM strategy, ALM decision-based asset allocation, and value-based management. In this course, you will learn several ALM techniques related to mitigating interest rate risks, managing risk and return trade-offs, and setting strategic asset allocation (SAA) to achieve an optimized risk/return portfolio. Additionally, you will be introduced to the concepts of value-based management and economic value of liabilities. Completing this course will give you a fundamental basis for understanding ALM in financial organizations and further prepare you to apply these concepts in real-life situations under both generally accepted accounting principles (GAAP) and market consistent approaches.
- Department: Actuarial Science
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Instructor:
Dariush Akhtari
Info - 3 points
The Actuary s Toolkit
The Actuarys Toolkit
This course is designed to equip students in the Columbia Actuarial Program with the technical software skills essential for modern actuarial work, with a special focus on Casualty Actuarial Science. Through this course, students will gain proficiency in Excel and R—two foundational tools used in data analysis, reserving, ratemaking, and simulation modeling. Excel will be explored as a powerful and accessible tool for structuring actuarial models, performing sensitivity analysis, and managing large data sets. R will be taught as a robust statistical programming language that supports reproducible actuarial analysis, including the use of GLMs, bootstrapping methods, and data visualization. The course will also include an introductory segment on Python, highlighting its growing relevance in automating workflows, handling large-scale data, and integrating with machine learning frameworks that may be increasingly relevant in pricing and predictive modeling.
The broader aim of this course is to bridge the gap between theoretical actuarial concepts and practical implementation through programming. By learning these software tools, students will be able to operationalize core actuarial principles—such as risk modeling, claim development, and stochastic analysis—within real-world business contexts. The course aligns with the Actuarial Program’s mission to produce industry-ready professionals who can not only understand the mathematical underpinnings of risk but also communicate and deliver insights through modern analytics platforms. It supports the development of computational thinking, data fluency, and technical agility, which are increasingly critical in actuarial practice as the industry becomes more data-driven and technologically complex.
This is an elective course available exclusively to students enrolled in the Columbia Actuarial Program. No prior experience with Excel, R, or Python is required, making it an ideal entry point for students new to programming or applied analytics. The course will be conducted fully online over the course of a full academic semester, providing flexibility while maintaining rigorous engagement through weekly assignments, project-based learning, and applied actuarial case studies. Whether students aim to pursue traditional actuarial roles or explore emerging areas like InsurTech, this course will provide the software toolkit needed to succeed in a modern actuarial environment.
- Department: Actuarial Science
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Instructor:
Mark Hoffmann
Info - 3 points
SOLAR PROJECT DEVELOPMENT
At the end of this course, students will be prepared to fully evaluate the technical and financial aspects of a solar project. They will be equipped with skills allowing them to either develop or rigorously vet solar project proposals. The course introduces and provides students with a holistic understanding of the end-to-end solar development process. The course has two goals:
To provide students a deep understanding of the dozens of critical interrelated steps critical to developing a successful operating solar project.
To equip the students with the tools and understanding of the skills necessary to develop a solar project beginning with site selection encompassing the entire process to commissioning and operations.
- Department: Sustainability Management
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Instructor:
Daniel J Giuffrida
Info - 3 points
Indigenous Oral Traditions and Anti-Colo
Through weekly readings, seminar discussions, and independent research, students will be immersed in the discourse, theoretical approaches, methods, and applications of Indigenous oral traditions and oral histories. Students will learn about the nature of oral traditions from multiple Indigenous perspectives; studying them as deeply grounded knowledge systems and world views connected to places and nations. The course will examine how colonialism has acted a great interrupter to the collective memory which is foundational to Indigenous oral traditions and nationhood. Finally, we will consider how contemporary anti-colonial Indigenous narratives are ‘remembering back’ by drawing upon and building from the stories that have (and have not) been passed down through the generations.
- Department: Oral History
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Instructor:
Francine D Spang-Willis
Info - 2 points
Infrastructure Development for Sustainab
ENVIRO INFRSTRCTURE SUS CITIES
Sustainable and resilient cities require integrated networks of transportation, water, waste, stormwater, energy, parks, housing, and communication infrastructure to support a low-carbon society and lifestyles. This course, led by two experienced practitioners and civic leaders, examines climate solutions at the city level through the lens of capital programs and policies, including responses to Hurricane-related challenges in New York City. Class modules cover key topics such as program development, stakeholder engagement, public support, project finance, contracting, and public-private partnerships, alongside sector-specific challenges, technologies, and initiatives. Grounded in real-world case studies, the course features guest lectures from city agencies and private-sector experts, as well as a field trip offering a behind-the-scenes look at an infrastructure facility. Designed for future sustainability leaders, this course equips students with the knowledge and skills to shape the cities of tomorrow.
- Department: Sustainability Management
- 3 points
ANALYTICS & LEADING CHANGE
ANALYTICS & LEADING CHNG
- Department: Applied Analytics
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Instructor:
Aaron Foster
Info - 3 points
ANALYTICS & LEADING CHANGE
ANALYTICS & LEADNG CHNG ONLINE
- Department: Applied Analytics
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Instructor:
David Kreutter
Info - 3 points
AI AND ETHICS: IN THE CLINIC
This course examines the ethical dimensions of artificial intelligence in healthcare, emphasizing translational ethics—the movement from identifying ethical concerns to proposing and defending justifiable, real-world solutions. Intended for clinicians, administrators, leaders, and researchers, the course explores how AI is impacting
patient care, clinical decision-making, health system operations, and clinical research. Students will engage with a broad spectrum of use cases, including AI in imaging specialties, predictive models for diagnoses and future health states, intraoperative support, large language models, mental health chatbots, and digital twins. Core themes include transparency, accuracy, impact, safety, governance, privacy, accountability, and the distribution of burdens and benefits. Seminar-style sessions are built around presentations, discussions, and interactive analysis, enabling participants to apply ethical frameworks to real clinical contexts. By course completion, students will be equipped to critically evaluate AI applications and contribute to strategies and policies that ensure safe, effective, and ethically
responsible deployment.
As part of a nascent but rapidly developing field, this course situates health AI ethics within the broader principles of patient-centered clinical ethics, equipping students to think critically about how emerging technologies intersect with core values of patient care. By engaging with both current applications and future concerns, the course helps learners bridge foundational ethical concepts with the novel challenges posed by AI solutions in diverse clinical settings—from imaging and predictive analytics to mental health support and operational tools. Within the program curriculum, the course complements broader training in Bioethics by focusing specifically on applied, translational ethics, ensuring students are prepared not only to identify ethical issues but also to propose and defend actionable solutions that can guide clinical practice, institutional policy, and system-level decision-making.
This course is offered as an elective within the program and is open to Bioethics students and students from other fields or Columbia University programs with permission. No specific competencies, prerequisite coursework, or prior knowledge in the discipline are required; students from diverse professional and academic backgrounds are welcome. The course is delivered fully online in a seminar format, fostering interac
- Department: Bioethics
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Instructor:
Charles Binkley
Info - 3 points
CINEMA/MEDIA HISTORIOGRAPHY
An introduction to issues and cases in the study of cinema century technologies. This class takes up the definition of the historiographic problem and the differences between theoretical empirical solutions. Specific units on the history of film style, genre as opposed to authorship, silent and sound cinemas, the American avant-garde, national cinemas (Russia and China), the political economy of world cinema, and archival poetics. The question of artificial intelligence approached as a question of the “intelligence of the machine.” A unit on research methods is taught in conjunction with Butler and C.V. Starr East Asian Libraries. Writing exercises on a weekly basis culminate in a digital historiography research map which becomes the basis of final written “paper” posted in Courseworks in video essay format. Students present this work at a final conference. Topics in the past include:
Cultural Transactions: Across Media and Continents, Genre: Repetition and Difference, and Bang, Bang, Crash, Crash: Canon-Busting and Paradigm-Smashing
- Department: Film
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Instructor:
Jane Gaines
CULPA: 4 Info - 3 points
Statistics - Graduate Recitation
Statistics - Grad Recitat
This course is an introduction to probability and statistics for data science. Topics
include probability theory, probability distributions, simulations, parameters estima-
tion, hypothesis testing, simple regression. Python examples will be used throughout
the course for illustrations.
- Department: Statistics
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Instructor:
Dobrin Marchev
Info - 0 points
Statistics - Graduate Recitation
Statistics - Grad Recitat
This course is an introduction to probability and statistics for data science. Topics
include probability theory, probability distributions, simulations, parameters estima-
tion, hypothesis testing, simple regression. Python examples will be used throughout
the course for illustrations.
- Department: Statistics
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Instructor:
Dobrin Marchev
Info - 0 points
Statistics - Graduate Recitation
Statistics - Grad Recitat
This course is an introduction to probability and statistics for data science. Topics
include probability theory, probability distributions, simulations, parameters estima-
tion, hypothesis testing, simple regression. Python examples will be used throughout
the course for illustrations.
- Department: Statistics
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Instructor:
Dobrin Marchev
Info - 0 points
ESTATE PLANNING
This course examines the key concepts and skills a wealth management
professional must understand to support making critical decisions with respect to
estate planning. Students will first be introduced to the fundamental characteristics
and consequences of property titling, before studying the components of estate
planning documentation. This course will explore the various strategies used to
transfer property and all of the factors impacting and related to the transfer
process, including gift and estate tax compliance and tax calculation, estate
liquidity, marital deduction, non-traditional relationships, and the types, features,
and taxation of trusts. Students will also explore the various techniques for
postmortem estate planning and techniques for intra-family and other business
transfers of property. The course will also begin to explore estate planning in a
global context, addressing issues and considerations that may arise.
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
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Instructor:
Dobrin Marchev
Info - 3 points
PROBABILITY & STAT FOR DATA SC
This course covers the following topics: Fundamentals of probability theory and statistical inference used in data science; Probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem; Statistical inference; point and confidence interval estimation, hypothesis tests, linear regression.
- Department: Statistics
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Instructor:
Dobrin Marchev
Info - 3 points
EXPLORATORY DATA ANALYSIS/VISUAL
This course is covers the following topics: fundamentals of data visualization, layered grammer of graphics, perception of discrete and continuous variables, intreoduction to Mondran, mosaic pots, parallel coordinate plots, introduction to ggobi, linked pots, brushing, dynamic graphics, model visualization, clustering and classification.
- Department: Statistics
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Instructor:
Joyce T Robbins
CULPA: 5
Info
- 3 points
EXPLORATORY DATA ANALYSIS/VISUAL
This course is covers the following topics: fundamentals of data visualization, layered grammer of graphics, perception of discrete and continuous variables, intreoduction to Mondran, mosaic pots, parallel coordinate plots, introduction to ggobi, linked pots, brushing, dynamic graphics, model visualization, clustering and classification.
- Department: Statistics
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Instructor:
Joyce T Robbins
CULPA: 5
Info
- 3 points
Intro to Digital Photography
Since Walter Benjamin’s concept of “work of art in the age of mechanical reproduction” (1935), photography has been continuously changed by mechanical, and then digital, means of image capture and processing. This class explores the history of the image, as a global phenomenon that accompanied industrialization, conflict, racial reckonings, and decolonization. Students will study case studies, read critical essays, and get hands-on training in capture, workflow, editing, output, and display formats using digital equipment (e.g., DSLR camera) and software (e.g., Lightroom, Photoshop, Scanning Software). Students will complete weekly assignments, a midterm project, and a final project based on research and shooting assignments. No Prerequisites and no equipment needed. All enrolled students will be able to check out Canon EOS 5D DSLR Camera; receive an Adobe Creative Cloud license; and get access to Large Format Print service.
- Department: Visual Arts
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Instructor:
Ben Salesse
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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
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Instructor:
Dobrin Marchev
Info - 3 points
GLOBAL FIELDWORK IN COMMUNICATION
This course gives students a chance to explore topics of global importance while gaining competence in cross-cultural communication and collaboration. The centerpiece is a 10-day study abroad experience, in which students travel internationally to engage with local actors and organizations who are doing strategic communication work. The course focuses on communication related to climate resilience, public health, politics, government, and culture. Students gain global perspective on the impact of strategic communication across sectors. See country brochure for additional details.
- Department: Strategic Communication
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Instructor:
Kristine Billmyer
Info - 3 points
SUMA PS5715 Intelligent Energy Systems:
Modernizing energy systems is essential for achieving a sustainable future. This course provides an in-depth exploration of intelligent energy systems, emphasizing the transformative role of Artificial Intelligence (AI) and Battery Energy Storage Systems (BESS) in advancing grid operations and sustainability objectives. Designed to integrate theoretical foundations with real-world application, the course highlights innovative methodologies and cutting-edge tools, enabling students to tackle critical challenges such as renewable energy intermittency, curtailment, grid reliability, and resilience. Through a challenging yet approachable curriculum, students will develop both the knowledge and hands-on expertise required to lead innovations in the renewable energy industry within an increasingly complex and interconnected world. This elective course is designed for students interested in the intersection of renewable energy, technology, and environmental stewardship. This course will emphasize transformative technologies in grid modernization, highlighting the pivotal roles of AI and BESS.
- Department: Sustainability Management
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Instructor:
Jingbo Hao
Info - 3 points
POLICY AND LEGAL CONTEXT FOR SUSTAINABIL
PLCY/LGL CNTX FOR SUSTAINABLTY
- Department: Sustainability Management
- 3 points
Advanced Topics in Financial Economics
Advanced Financial Econom
This is a topics course in financial economics intended for Economics MA students. The focus of the course is on applied methods, including training with financial data, estimation methods, and identification strategies for causal analysis in finance research. Students will cover papers and gain practical experience in execution algorithms, machine learning in asset pricing, asset demand systems, financial intermediaries, trading costs, and passive investments. Prior coding and programming experience in a specific language are not strictly necessary, but basic knowledge of R or Python are helpful.
- Department: Economics
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Instructor:
Arkaprava Bandyopadhyay
Info - 3 points
Sustainable Management of Forests
The course intends to give an overview of forests – how they function, and how they can be managed sustainably. The course addresses both the ecology and economics of forests. Combining the study of these two disciplines is necessary to understand and develop management actions and solutions to deforestation. The emphasis in integrating ecology and economics is going to be on learning tools and techniques for managing forests. The course accounts both for North American and forests in other countries, including tropical ones. Current typical conceptions of forests are somewhat paradoxical: forests are considered marginal in sustainability, and yet they connect with many issues of central concern such as biodiversity, climate change, household energy for the poor, homelands for indigenous people, water and human shelter, to name a few. More specifically, forests provide a fruitful line of inquiry into many environmental issues, such as the complex balances within ecosystems, global cycling of elements, such as carbon, the nature of sustainability, and interactions between economic development and the conservation of nature. For example, we will study biodiversity in forests. Much biodiversity is found outside of forests, but our study will provide an understanding of the ecological dynamics involved with biodiversity, the possible management options, and its importance for human survival. The course is going to emphasize the role of forests in the carbon cycle and the contribution of deforestation to climate change.
- Department: Sustainability Management
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Instructor:
Ralph C Schmidt
Info - 3 points
STORYTELLING WITH DATA AND AI
STORYTELLING WITH DATA AI
Data does not have meaning without context and interpretation. Being able to effectively present data analytics in a compelling narrative to a particular audience will differentiate you from others in your field. This course takes students through the lifecycle of an analytical project from a communication perspective. Students develop written, verbal, and visual deliverables for three major audiences: data experts (e.g., head of analytics); consumer and presentation experts (e.g., chief marketing officer); and executive leadership (e.g., chief executive officer).
Students get ample practice in strategic interactions in relevant social and professional contexts (e.g., business meetings, team projects, and one-on-one interactions); active listening; strategic storytelling; and creating persuasive professional spoken and written messages, reports, and presentations. Throughout the course, students create and receive feedback on data storytelling while sharpening their ability to communicate complex analytics to technical and nontechnical audiences with clarity, precision, and influence.
- Department: Applied Analytics
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Instructor:
Javid B Huseynov
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Instructor:
Christian Klose
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Instructor:
Paula Darling
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Instructor:
Gert de Geyter
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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
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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
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Instructor:
Miya Masaoka
Wikipedia Info - 0 points
SPECIAL TOPICS IN WEALTH MANAGEMENT: DIS
DISRUPTIVE TRENDS
Columbia SPS is on the forefront of leading issues in the Wealth Management
profession. This course is designed to explore disruptive trends in the Wealth
Management industry and the opportunities and challenges that may result. As the
profession evolves, our graduates will be prepared to be leaders within all business
models across wealth management. Topics include, but are not limited to,
technology, client psychology, ESG/sustainable investing, financial products,
evolving fee structures, shifting demographics, increased regulatory burdens,
democratization of financial advice, and more.
- Department: Wealth Management
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Instructor:
Matthias Kuhlmey
Info - 3 points
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
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Instructor:
Abraham Weishaus
Info - 3 points
ACTUARIAL MODELS
This course provides an introduction to the tools for pricing and reserving for short term insurance. We will discuss methods for calculating IBNR reserves, ratemaking, frequency and severity models used for modeling coverage modifications, statistical methods for fitting, evaluating, and selecting parametric models for frequency and severity, and three credibility methods.
This class covers the short-term material of Exam FAM and also covers the material of Exam ASTAM of the Society of Actuaries, and some of the material on Exams MAS I, MAS II, and 5 of the Casualty Actuarial Society. This is a core class of the Actuarial Science program. Students who have already taken and passed the FAM exam (or its short term portion) and the ASTAM exam administered by the SOA are exempted from this class and can substitute an elective.
- Department: Actuarial Science
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Instructor:
Regina Dolgoarshinnykh
CULPA: 2 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
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Instructor:
Julia Valuyeva
Info - 3 points
CRIT ISSUES IN ART I
N/A
- Department: Visual Arts
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Instructor:
Miles J Scharff
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
N/A
- Department: Visual Arts
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Instructor:
Miya Masaoka
Wikipedia 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.
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.
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
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Instructor:
Leland Wei
Info - 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.
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
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Instructor:
James Maher
Info - 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
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Instructor:
John N Vitucci
Info - 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
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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
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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
- 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
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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
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Instructor:
Darryl Wagner
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.
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
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Instructor:
Xue Bai
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Instructor:
Aristargos Christodoulou
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Instructor:
Bruce Fox
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Instructor:
Narae Lee
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Instructor:
Richard R. Ranade
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Instructor:
Yi Zuo
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Instructor:
Benjamin M Perlman
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Instructor:
Rich Lauria
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Instructor:
David P McCarthy
Wikipedia Info - 3 points
INSURANCE RISK MANAGEMENT
- Department: Insurance Management
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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
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Instructor:
Sara Yeganeh
Info - 3 points