PROSEMINAR II

• Department: Sociology
• W 4:10 p.m.-6 p.m.
• Instructor: Denise Milstein
  CULPA: 9 h-index: 5 Info
• 1 points

BAYESIAN STATS FOR THE SOC SCI

An introduction to Bayesian statistical methods with applications to the social sciences. Considerable emphasis will be placed on regression modeling and model checking. The primary software used will be Stan, which students do not need to be familiar with in advance. Students in the course will access the Stan library via R, so some experience with R is necessary. Any QMSS student is presumed to have sufficient background. Any non-QMSS students interested in taking this course should have a comparable background to a QMSS student in basic probability. Topics to be covered are a review of calculus and probability, Bayesian principles, prediction and model checking, linear regression models, Bayesian calculations with Stan, hierarchical linear models, nonlinear regression models, missing data, and decision theory.

• Department: Quantitative Methods/Social Sciences
• M 2 p.m.-4 p.m.
• Instructor: Hadi Katesari
  Info
• 3 points

FIELD WORK II

• Department: Sociology
• F 8:10 a.m.-9 a.m.
• Instructor: Denise Milstein
  CULPA: 9 h-index: 5 Info
• 1 points

NATURAL LANG PROCESSING SOCIAL SCIENCES

Social scientists need to engage with natural language processing (NLP) approaches that are found in computer science, engineering, AI, tech and in industry. This course will provide an overview of natural language processing as it is applied in a number of domains. The goal is to gain familiarity with a number of critical topics and techniques that use text as data, and then to see how those NLP techniques can be used to produce social science research and insights. This course will be hands-on, with several large-scale exercises. The course will start with an introduction to Python and associated key NLP packages and github. The course will then cover topics like language modeling; part of speech tagging; parsing; information extraction; tokenizing; topic modeling; machine translation; sentiment analysis; summarization; supervised machine learning; and hidden Markov models. Prerequisites are basic probability and statistics, basic linear algebra and calculus. The course will use Python, and so if students have programmed in at least one software language, that will make it easier to keep up with the course.

• Department: Quantitative Methods/Social Sciences
• M 6:10 p.m.-8 p.m.
• Instructor: Patrick Houlihan
  Info
• 3 points

THESIS SEMINAR II

• Department: Sociology
• T 4:10 p.m.-6 p.m.
• T 10:10 a.m.-noon
• Instructor: Denise Milstein
  CULPA: 9 h-index: 5 Info
• Instructor: Adam Reich
  CULPA: 7 Info
• 3 points

APPLIED DATA SCI FOR SOC SCIENTISTS

• Department: Quantitative Methods/Social Sciences
• T 6:10 p.m.-8 p.m.
• Instructor: Marco A Morales Barba
  Info
• 3 points

GIS & SPATIAL ANALYSIS-SOC SCI

Prerequisites: Undergraduate Statistics This course introduces students to basic spatial analytic skills. It covers introductory concepts and tools in Geographic Information Systems (GIS) and database management. As well, the course introduces students to the process of developing and writing an original spatial research project. Topics to be covered include: social theories involving space, place and reflexive relationships; social demography concepts and databases; visualizing social data using geographic information systems; exploratory spatial data analysis of social data and spatially weighted regression models, spatial regression models of social data, and space-time models. Use of open-source software (primarily the R software package) will be taught as well.

• Department: Quantitative Methods/Social Sciences
• T 12:10 p.m.-2 p.m.
• Instructor: Edwin Y Grimsley
  Info
• 3 points

Machine Learning in Sustainability Scien

This course is for students who wish to participate in a hands-on, project-based, exploration of how machine learning (ML) can be harnessed to tackle challenges in sustainability science.  Students will work collaboratively on a semester-long coding project, applying ML techniques to real sustainability problems in data analysis and visualization. Along the way, they will sharpen their skills in AI-assisted coding with tools such as ChatGPT, using these technologies not just to write and debug code more efficiently, but also to enhance creativity, streamline collaboration, and bring complex ideas to life. By the end of the course, students will have experienced the full arc of designing, building, and refining an ML solution with direct relevance to the future of our planet.

This course equips students with experience using advanced scientific and computational tools for tackling environmental and sustainability challenges. Through AI-assisted coding of ML solutions, students develop creative approaches to analyzing, modeling, and interpreting state-of-the-art observations of Earth systems using rigorous, quantitative methods. Guided readings and discussion will help students achieve mastery of the subject. The class takes place in a project-based setting where students collaborate on a semester-long project, requiring them to plan together, manage tasks, and integrate diverse skills in applying scientific principles to real-world problems. In doing so, the course not only deepens technical expertise but also strengthens students’ ability to provide data-driven insights that inform sustainability decision-making.

• Department: Sustainability Science
• R 6:10 p.m.-8 p.m.
• Instructor: James L Davis
  h-index: 57 Info
• 3-4 points

MODERN DATA STRUCTURES

This course is intended to provide a detailed tour on how to access, clean, “munge” and organize data, both big and small. (It should also give students a flavor of what would be expected of them in a typical data science interview.) Each week will have simple, moderate and complex examples in class, with code to follow. Students will then practice additional exercises at home. The end point of each project would be to get the data organized and cleaned enough so that it is in a data-frame, ready for subsequent analysis and graphing. Therefore, no analysis or visualization (beyond just basic tables and plots to make sure everything was correctly organized) will be taught; and this will free up substantial time for the “nitty-gritty” of all of this data wrangling.

• Department: Quantitative Methods/Social Sciences
• R 6:10 p.m.-8 p.m.
• Instructor: Nicholas L Anderson
  Info
• 3 points

MACHINE LEARNING SOC SCI

Prerequisites: basic probability and statistics, basic linear algebra, and calculus This course will provide a comprehensive overview of machine learning as it is applied in a number of domains. Comparisons and contrasts will be drawn between this machine learning approach and more traditional regression-based approaches used in the social sciences. Emphasis will also be placed on opportunities to synthesize these two approaches. The course will start with an introduction to Python, the scikit-learn package and GitHub. After that, there will be some discussion of data exploration, visualization in matplotlib, preprocessing, feature engineering, variable imputation, and feature selection. Supervised learning methods will be considered, including OLS models, linear models for classification, support vector machines, decision trees and random forests, and gradient boosting. Calibration, model evaluation and strategies for dealing with imbalanced datasets, n on-negative matrix factorization, and outlier detection will be considered next. This will be followed by unsupervised techniques: PCA, discriminant analysis, manifold learning, clustering, mixture models, cluster evaluation. Lastly, we will consider neural networks, convolutional neural networks for image classification and recurrent neural networks. This course will primarily us Python. Previous programming experience will be helpful but not requisite. Prerequisites: basic probability and statistics, basic linear algebra, and calculus.

• Department: Quantitative Methods/Social Sciences
• W 6:10 p.m.-8 p.m.
• R 6:10 p.m.-8 p.m.
• Instructor: Mazen Asaad
  Info
• Instructor: Kyle Horn
  Info
• 3 points

PROJECTS IN ADVANCED MACHINE LEARNING

ADV MACHINE LEARNING

Machine learning algorithms continue to advance in their capacity to predict outcomes and rival human judgment in a variety of settings.  This course is designed to offer insight into advanced machine learning models, including Deep Learning, Recurrent Neural Networks, Adversarial Neural Networks, Time Series models and others.  Students are expected to have familiarity with using Python, the scikit-learn package, and github.  The other half of the course will be devoted to students working in key substantive areas, where advanced machine learning will prove helpful -- areas like computer vision and images, text and natural language processing, and tabular data.  Students will be tasked to develop team projects in these areas and they will develop a public portfolio of three (or four) meaningful projects.  By the end of the course, students will be able to show their work by launching their models in live REST APIs and web-applications.

• Department: Quantitative Methods/Social Sciences
• R 6:10 p.m.-8 p.m.
• Instructor: Alexander Peterhansl
  Info
• 3 points

ORAL HISTORY INTERNSHIP

• Department: Oral History
• R 2:10 p.m.-3 p.m.
• Instructor: Amy A Starecheski
  h-index: 5 Info
• 2-4 points

THE AUTHENTIC LEADER

Effective leaders are able to think critically about problems and opportunities, imagine unexpected futures, craft a compelling vision, and drive change. In this course, we study the theoretical underpinnings of leadership communication, relying on empirical evidence as a guide for practice. Students gain important perspective on leadership styles, mastering the competencies required for a variety of contexts.

• Department: Strategic Communication
• S 1:30 p.m.-5:30 p.m.
• Instructor: Khalid Azim
  Info
• 3 points

THE DOCUMENTARY TRADITION

• Department: Film
• R 2 p.m.-5:45 p.m.
• Instructor: Nico Baumbach
  Info
• 3 points

MULTIMEDIA STORYTELLING

MULTIMEDIA PRACTICE

This course is a survey of the documentary craft and industry through the lens of the documentary relationship. We will define the documentary relationship as those relations organic to the process of crafting documentary. Those can be between narrator and producer, between collaborators, and between material and meaning. We will ask questions about craft, ethics, and power within documentary work and workshop short documentary works of our own

• Department: Oral History
• W 5:10 p.m.-8 p.m.
• 4 points

MONITORING AND ANALYSIS OF COASTAL MARIN

MARINE & ESTUARY SYSTEMS

From a global perspective, many of the earth’s most important environments and resources for global sustainability are located in marine and estuarine areas.  This class will explore open-ocean and estuarine processes, reviewing what is known about the temporal variability and interconnectedness of these physical and biologic systems.. A few examples include; 1.) oceanic environments were incompletely understood processes regulate the exchange of heat, water and carbon dioxide gas with the atmosphere, 2.) the relationship between nutrients and primary production and fisheries in open ocean, estuarine and coral reef environments and climatic phenomenon such as El Niño South Oscillation (ENSO), the Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO). 3.) For estuaries, current sea level and urbanization stresses on the coastal environments will be discussed.  Professionals working in the environmental and engineering fields will benefit from a wide-ranging discussion of the multi-scaled processes influencing estuaries. Knowledge of the processes operating in these environments will lead to a more thorough understanding of the complex issues that may influence infrastructure and coastal development in and around estuarine environments in the near-future. Throughout the class we will explore marine and estuarine processes by studying regional and local responses to broader scale climatic forcing. Reading of textbook chapters and journal articles will supplement in-class lectures and discussion. Grading will be based on class participation, a two exams and a research paper. At the end of the course, students will have a strong scientific understanding about the impacts made on marine and estuary systems through physical, chemical, and biological processes. The course will prepare students to be well-trained in the core features of these systems and the relationship between natural and human processes, and equip them with the skills needed to explore marine and estuary systems in diverse scales and functions in the future.

• Department: Sustainability Science
• W 6:10 p.m.-8 p.m.
• Instructor: Braddock Linsley
  h-index: 42 Info
• 3 points

Quantum Simulation and Computing Lab

Quantum Simul. & Computin

The “Quantum Simulation and Computing Lab” will give students in the Quantum Masters program hands-on experience in quantum optics, quantum simulation and quantum computing. The course combines lectures, tutorials, and two lab sections. In one lab section, students will do experiments with entangled photons. In the second lab section, students will program quantum computers and run algorithms on them using the IBM Qiskit platform.

The course starts with a recap of linear algebra and quantum mechanics, followed by an introduction to quantum optics and quantum information. Two-level systems, Bloch sphere, quantum gates, and elementary quantum algorithms will be discussed. Quantum teleportation and quantum key distribution will be introduced as applications of entanglement. The lecture content will be directly applied in experiments with entangled photons. In the following, state-of-the-art quantum algorithms will be discussed, related to cutting-edge research results in quantum computing. This includes quantum Fourier transform, quantum simulation of the Schroedinger equation, and the variational quantum eigensolver (VQE) algorithm. During the course students will do one experimental project with entangled photons and one quantum programming project. Students will be guided to implement a quantum algorithm of their choice and run it on a quantum computer (IBM, IonQ, QuEra).

• Department: Physics
• WF 1:10 p.m.-2:25 p.m.
• Instructor: Zhenjie Yan
  Info
• 3 points

DIGITAL MEDIA & ANALYTICS

Digital media opens new opportunities for increasingly targeted communications across a variety of channels, which rapidly expands the importance of analytics in tracking and measuring key performance indicators (KPIs). This course prepares students to work within data- and model-driven environments with an emphasis on using analytics to develop insights and support strategic decisions.

• Department: Strategic Communication
• R 2:10 p.m.-4 p.m.
• M 6:10 p.m.-8 p.m.
• Instructor: Lilian Ore
  Info
• 3 points

FUNDAMENTLS OF OMBUDS PRACTICE

This is a practice-based course designed to introduce students to ombuds work, and to help students develop the skills, knowledge, and attitudes required to succeed in the role of an organizational ombuds.  Underlying theory will be explored in the context of practice. This course will emphasize the Standards of Practice and Code of Ethics put forth by the International Ombudsman Association (IOA).  Upon successful completion of the course, students will be fundamentally prepared to apply for entry-level positions in the field of organizational ombuds and should have the requisite knowledge base to sit for the IOA’s Certified Organizational Ombuds Practitioner exam.

Note for NECR Students
: This course is offered as an elective in the Negotiation and Conflict Resolution (NECR) program. The course builds upon students’ negotiation and mediation skills and self-awareness as a practitioner.  It is highly recommended that students complete PS5105, PS5107 and PS5124 prior to enrolling in this course.

• Department: Negotiation & Conflict Resolution
• S 9 a.m.-5 p.m.
• Instructor: Amy E Sereday
  Info
• 3 points

COMPLEXITY OF CONFLICT AND CHANGE MANAGE

Complexity of Conflict and Change Management (NECR K5095) is an elective course in the Negotiation and Conflict Resolution (NECR) Program. The course explores how change can create conflict and also how conflict requires change. Conflict is generally about differences in how people think, know, prefer, believe, and understand. By entering into a conflict resolution process, people can shift their understanding and beliefs about the conflict, the other party or parties, and possible outcomes. The course reviews literature and case studies of how people are impacted at a fundamental level when change occurs. Understanding this elemental human experience can lead to greater self-awareness and the ability to manage change professionally and personally, in order to become effective change agents, negotiators, mediators, and peacemakers. We will also explore how leaders at all levels in organizations can play an important role in implementing change in an organizational context. Thoughtful and strategic approaches that consider the impact of a change management process can mitigate and even prevent conflict. We will review change management models and links to developments in neuroscience and how humans are biologically wired to resist change. Balancing theory and practice, this course will focus on the experience and expertise of the students. They will learn to apply practical conflict resolution approaches to change efforts at the individual and organizational levels as well as consider national and international applications.

• Department: Negotiation & Conflict Resolution
• WR noon-2 p.m.
• Instructor: Susan Levin
  Info
• 3 points

APPLIED ANALYTICS IN ORGANIZATIONAL CONT

APP AN IN ORG CONTEXT

• Department: Applied Analytics
• M 6:10 p.m.-8 p.m.
• Instructor: Panagiotis Tarhanidis
  Info
• 3 points

ESTIMATING PROJECT COSTS

• Department: Construction Administration
• W 4:10 p.m.-6 p.m.
• Instructor: Jason I Levine
  Info
• 3 points

VALUE-BASED ENTERPRISE RISK MANAGEMENT

VALUE-BASED ERM

Foundational ERM course. Addresses all major ERM activities: risk framework; risk governance; risk identification; risk quantification; risk decision making; and risk messaging. Introduces an advanced yet practical ERM approach based on the integration of ERM and value-based management that supports integration of ERM into decision making. Provides a context to understand the differences between (a) value-based ERM; (b) traditional ERM; and (c) traditional "silo" risk management.

• Department: Enterprise Risk Management
• T 8:10 p.m.-10 p.m.
• M 6:10 p.m.-8 p.m.
• M 8:10 p.m.-10 p.m.
• R 6:10 p.m.-8 p.m.
• R 8:10 p.m.-10 p.m.
• T 6:10 p.m.-8 p.m.
• R 6:10 p.m.-8 p.m.
• W 6:10 p.m.-8 p.m.
• Instructor: Rich Lauria
  Info
• Instructor: Duncan Campbell
  Info
• 3 points

SELF & OTHER IN CLIN ENCOUNTER

TBD

• Department: Narrative Medicine
• M 2:10 p.m.-5 p.m.
• Instructor: Michael E Davidovits
  Info
• 4 points

NONPROFIT DEVELOPMENT, FUNDRAISING, AND

NP DEVELOPMENT & FUNDRAIS

This course provides a comprehensive overview of the design, implementation and management of the components of a philanthropic program and its relationship to the financial sustainability of the nonprofit organization. It introduces the philosophical, ethical and historical underpinnings of fundraising practice, also providing the nomenclature, characteristics, and methods of gift generation and their sources, and the management and stewardship of those sources. Additionally, it examines the relationship of the organization’s mission to its strategic vision and the planning, management and impact of fundraising to the organization’s advancement and sustainability.

• Department: Non Profit Management
• T 6:10 p.m.-8 p.m.
• M 6:30 p.m.-8 p.m.
• Instructor: John Hicks
  Info
• Instructor: Ramone Segree
  CULPA: 1 Info
• 3 points

Leadership and Team Dynamics

This course introduces students to the core principles of effective leadership and collaborative team performance in organizational settings. Through a practical, evidence-based approach, the course examines how leaders influence outcomes, foster engagement, and navigate challenges in dynamic, multidisciplinary environments. Students will explore leadership qualifications, strategic decision-making, ethical considerations, and performance development frameworks. Emphasis is placed on understanding the dynamics of team formation, multicultural collaboration, communication, conflict management, and high-performance team practices.

As a central component of the Project Management curriculum, this course supports the program’s larger goal of preparing graduates to lead effectively in diverse and evolving organizational contexts. By grounding students in evidence-based leadership concepts and team effectiveness frameworks, the course advances the discipline’s primary principles of organizational performance, collaboration, and responsible decision-making. The course aligns closely with other program requirements by complementing technical project management competencies with the interpersonal and strategic skills necessary for successful project execution. In doing so, it bridges technical expertise with leadership acumen, equipping students with a holistic foundation for professional growth.

This is a required core course for all Project Management students and is delivered in person on campus in a full-semester format. Space permitting, the course may also be open to cross-registrants from other Columbia University graduate programs where leadership, management, and teamwork skills are relevant, such as programs in management, public administration, and engineering. There are no formal prerequisites, though prior exposure to management or organizational behavior may be helpful in engaging with course materials. Students will participate in selected readings, interactive discussions, and team-based exercises, as well as hear from guest lecturers with extensive leadership experience. By the end of the course, students will have strengthened their ability to lead ethically, communicate clearly, manage team dynamics, and contribute meaningfully to organizational goals.

• Department: Project Management
• T 4:10 p.m.-6 p.m.
• 3 points

English for International and Public Aff

English for Int'l & Publi

This course is designed to strengthen the academic writing skills of SIPA students whose first language is not English. Emphasizing clarity, structure, and academic rigor, the course supports students in developing the writing competencies necessary for success in the MIA and MPA programs. Students will practice summarizing complex texts, crafting literature reviews, explanatory and argumentative essays, and revising their work based on detailed instructor feedback. The course also reinforces advanced grammar, vocabulary, and citation practices, with an emphasis on avoiding plagiarism and promoting original thought.

Assignments include short weekly exercises, midterm and final in-class essays, and three major take-home writing projects. Active participation, peer review, and group discussion of assigned readings are essential components of the course. By the end of the semester, students will gain confidence writing in English across academic and policy contexts while deepening their understanding of key public affairs topics.

• Department: International and Public Affairs
• MW 10 a.m.-11:50 a.m.
• TR 10 a.m.-11:50 a.m.
• MW 3:10 p.m.-5 p.m.
• 3 points

PAINTING I

• Department: Visual Arts
• M 10 a.m.-4 p.m.
• Instructor: Dana J Lok
  Info
• 3 points

FINANCE AND ACCOUNTING FOR THE CONSTRUCT

FIN & ACCTG CONSTRCTN INDUSTRY

.

• Department: Construction Administration
• M 6:10 p.m.-8 p.m.
• Instructor: William Fischer
  Info
• 3 points

Business Analytics

• Department: Decision, Risk and Operations
• F 3:45 p.m.-6:45 p.m.
• FS 8:30 a.m.-11:30 a.m.
• S 3:45 p.m.-6:45 p.m.
• S 12:30 p.m.-3:30 p.m.
• Instructor: Yashodhan Kanoria
  h-index: 18 Info
• 1.5 points

UNDERSTAND CONFLICT & COOPERATION

UNDERSTAND CONFL & COOPER

• Department: Negotiation & Conflict Resolution
• TMT noon-1:30 p.m.
• Instructor: Fulya N Beriker
  Info
• 3 points

PSYCHIATRIC MEDICINE

• Department: Psychiatry
• 0 points

SAFETY IN THE CONSTRUCTION INDUSTRY

SAFETY IN THE CONSTR IND

.

• Department: Construction Administration
• T 6:10 p.m.-8 p.m.
• Instructor: Joseph Schwed
  Info
• 3 points

Operations Management

• Department: Decision, Risk and Operations
• S 3:45 p.m.-6:45 p.m.
• F 8:30 a.m.-11:30 a.m.
• S 3:45 p.m.-6:45 p.m.
• SS 12:30 p.m.-3:30 p.m.
• Instructor: Hongyao Ma
  h-index: 7 Info
• 1.5 points

PAINTING III

• Department: Visual Arts
• T 10 a.m.-4 p.m.
• Instructor: Kenny Rivero
  Info
• 3 points

LEGAL ISSUES IN BUILDING CONSTRUCTION

LEGAL ISSUES-BLDG CONSTR

• Department: Construction Administration
• R 6:10 p.m.-8 p.m.
• Instructor: Ross McKinnon
  Info
• 3 points

NEGOTIATION & DISPUTE RESOLUTION IN SPRT

• Department: Sports Management
• MTWRF 9 a.m.-5 p.m.
• Instructor: Scott Rosner
  Info
• 3 points

FOUNDATIONS OF CLINICAL MEDICINE II

FOUNDATIONS OF CLINICAL MED II

• Department: Medicine
• 0 points

THE BODY IN HEALTH & DISEASE I

• Department: Medicine
• 0 points

Negotiation for Everyday Use

In this course, we will explore negotiation from several points of view and approaches. We will also look at characteristics that impact the quality of our negotiations and the outcomes, such as the role of emotions, cultural considerations, effectiveness of our communication, and opportunities to seek out negotiation to transform relationships. The course will be a blend of concepts and skills, theory and practice. On some occasions, you will be introduced to a concept and then asked to apply those concepts in an experiential activity. At other times, you will be asked to engage the activity or simulation and then the concepts will be elicited based on your experience. You will have several opportunities to practice developing your skills throughout the course, in terms of enhancing your practice and honing your analytical and conceptual understanding.

• Department: Negotiation & Conflict Resolution
• W 6:10 p.m.-8 p.m.
• Instructor: Martin E Epstein
  Info
• 3 points

PRACTICUM: FACILITATING NARRATIVE MEDICI

PRACTICUM: FACILITATING W

TBA

• Department: Narrative Medicine
• R 6:10 p.m.-8 p.m.
• Instructor: Stephanie J Adler Yuan
  Info
• 3 points

Design-Based Innovation

Design-based Innovation is a set of perspectives and processes that organizations of all kinds, in any kind of industry or context, can use to navigate ambiguity to find the best possible opportunities to create change. It is also a well-developed set of practices to devise and deliver solutions for those potential audiences that result in valuable product, service, and other experiences that customers, consistent, and others respond to with satisfaction, delight, and a sense of value.

• Department: Technology Management
• R 8:10 p.m.-10 p.m.
• Instructor: Nathan Shedroff
  Info
• 3 points

Practices in Design and Innovation

Pract in Design and Innov

Design is at the core of every innovation. It’s the visual, experiential, and strategic medium through which ideas transform into tangible and digital products, service platforms, experiences, and consequences. This course is a comprehensive exploration of the methods, vocabulary, challenges, and opportunities of design-led innovation. It demystifies how business and design intersect through the lens of innovation, and is foundational for anyone seeking to generate positive social and economic outcomes.

Students experience the course through interconnected paths—interrogating contemporary issues in design and business while simultaneously moving a chosen project through a sequence of hands-on design sprints. These sprints cover everything from ideation and visualization to journey mapping, prototyping, user testing, and branding of their own unique ideas. Participants will emerge with a critical and reusable toolkit for both understanding the innovation process and effectively leading creative teams.

Topics include: Design Thinking; User-Centered Design; Business Value of Design; Problem Framing; Systems Mapping; User Journey Mapping; Ambiguity and Complexity; Liberatory Design Practices; The Impact of AI; Design Ethics; Sustainability, Wicked Problems; Design Futuring and speculative design.

• Department: Technology Management
• T 6:10 p.m.-8 p.m.
• Instructor: Allan Chochinov
  Info
• 3 points

Finance for Technology Leaders

An exploration of the central concepts of corporate finance for those who already have some basic knowledge of finance and accounting. This case-based course considers project valuation; cost of capital; capital structure; firm valuation; the interplay between financial decisions, strategic consideration, and economic analyses; and the provision and acquisition of funds. These concepts are analyzed in relation to agency problems: market domination, risk profile, and risk resolution; and market efficiency or the lack thereof. The validity of analytic tools is tested on issues such as highly leveraged transactions, hybrid securities, volatility in initial public offerings, mergers and acquisitions, divestitures, acquisition and control premiums, corporate restructurings, and sustainable and unsustainable market inefficiencies.

• Department: Technology Management
• W 6:10 p.m.-8 p.m.
• Instructor: Conrad Fernandes
  Info
• 3 points

SCHEDULNG FOR MODERN CONSTRUCTION

A comprehensive introduction to the principles, methods and tools required for the development and implementation of scheduling in the construction industry. Topics covered include: the crucial role of the scheduling development plans, budgeting and its impact on project timelines, identification and analysis of critical paths (CPM), resource and cost loading, schedule updating, and schedule management. Coursework is integrated with hands‐on utilization of Oracle Primavera P3 and P6 scheduling and Microsoft Project 2007 software. Students may need to bring their own laptops/notebooks for some class sessions. Guest lecturers may be featured for certain topics.


OBJECTIVE:
 This course should prepare the student to prepare a CPM schedule, calculate the schedule manually or by use of computer software, evaluate the output of such software, and present such analysis both to field personnel for implementation and to upper management for overview.

• Department: Construction Administration
• M 6:10 p.m.-8 p.m.
• Instructor: Sadia I Janjua
  Info
• 3 points

SCREENWRITING II

• Department: Film
• T 10 a.m.-1 p.m.
• R 2 p.m.-5 p.m.
• T 10 a.m.-1 p.m.
• R 10 a.m.-1 p.m.
• W 2 p.m.-5 p.m.
• M 6 p.m.-9 p.m.
• M 9:30 a.m.-1 p.m.
• W 6 p.m.-9 p.m.
• Instructor: Matthew R Fennell
  Info
• Instructor: Rania Attieh
  Info
• 3 points

METHODS: CLOSE READING & CREATIVE WRITIN

METHODS: CLOSE RDNG & CRT

TBA

• Department: Narrative Medicine
• 3 points

NOPM Non-Credit Professional Development

NOPM Professional Develop

Investing in professional growth is essential to building strong, adaptive, and innovative nonprofit organizations. Columbia University's M.S. in Nonprofit Management Professional Development Series is an online, bi-weekly, zero-credit seminar class that helps students stay current with best practices, navigate complex challenges, improve organizational sustainability, and enhance their impact in the communities they serve. Students will increase their networks and connect with potential mentors and employers while hearing how they can leverage the M.S. in Nonprofit Management degree in their own careers.

The course, which is a co-registration requirement for NOPM students taking Capstone, is open to all NOPM students and for cross-registration.

• Department: Non Profit Management
• F noon-1 p.m.
• Instructor: Basil A Jr Smikle
  Info
• 0 points

Electoral Data & Predictive Modeling

One of the best ways to predict the future is to study the past. A dizzying amount of data is available to study elections and politics, including survey and polling data on individual preferences, beliefs, demographics, and choices; data on aggregate conditions and outcomes; and, for more recent years, a wide range of social media data. From polling analysts and pundits to campaign managers and career journalists, making sense of this data can create a competitive advantage for professionals working in the field of politics. By analyzing the results of previous elections, insights can be gleaned to enhance understanding of the factors that contributed to electoral wins and be used to build statistical models or to create machine learning models that can predict future outcomes. Students will curate various types of data and work with starter code to build their data wrangling and computational skills. Students will learn how to explore data with these techniques, understand how they work, and derive insights and knowledge based on the analysis results.

• Department: Political Analytics
• T 6:10 p.m.-8 p.m.
• Instructor: Michael Schwam-Baird
  Info
• 3 points

Figure Painting: Alternative Source Mate

This class is designed to introduce you not only to the subject of painting the human figure and its expressive potential, but also to focus on the art and craft of Painting. We will be painting the figure from secondary source material that can include photos, other artworks, clay models etc. The focus will be on figurative narration. We will be learning to see color, and use paint in response to that. Painting is a way to account for, express and communicate what you have seen with your eyes, mind or in your imagination. You will be introduced to different approaches to the craft of painting, and will by the end of the semester be more free and confident in interpreting your inner and outer vision. We will also be looking at paintings made in different times and places and discuss how and why they look the way they do. You will also be designing and carrying out your own independent project to be completed by the final critique.

• Department: Visual Arts
• F 10 a.m.-4 p.m.
• 3 points

Presence and Persuasion for Technology L

This course teaches students how to get through to any audience for any reason. Technology leaders, more than in any other industry, must be equally comfortable as public speakers for vastly different audiences, from software developers and sales teams to politicians and the general public. Through exercises in speaker and audience analysis, studies in public speaking techniques, and an exploration of behavioral psychology principles influencing audience receptivity, students will gain tangible skills to increase their impact as public speakers. Specifically, this course will equip students to: 1. identify how impactful speakers prepare for, present to, and pivot for maximum impact according to audience type, size, and receptivity; 2. learn strategies on how to “read the room” and adapt both verbal and nonverbal communication techniques in real-time; and 3. gain hands-on experience in public speaking through exercises designed to develop public speaking skills across a range of tech-sector specific experiences, circumstances, audiences.

• Department: Technology Management
• W 6:10 p.m.-8 p.m.
• Instructor: Alexis Wichowski
  CULPA: 1 h-index: 6 Info
• 3 points

Driving Social Change with Technology

Driving Social Change w T

This one-semester onsite course explores how social entrepreneurs use technology innovation to achieve social impact and help achieve the 17 UN Sustainable Development Goals (UN SDGs) in collaboration with corporations and governments. Social entrepreneurs are defined as organizations who develop and implement solutions to social and environmental problems while striving for financial sustainability.

• Department: Technology Management
• T 4:10 p.m.-6 p.m.
• Instructor: Alexis Wichowski
  CULPA: 1 h-index: 6 Info
• 3 points

INTRAPERSONAL DYNAMICS & CONFLICT

• Department: Negotiation & Conflict Resolution
• F noon-2 p.m.
• Instructor: Barry Sommer
  Info
• 3 points

Law for Technology

Law is infused into every part of business, especially through the lens of technology. Fluency in business and legal frameworks, risk/benefit principles, from idea to exit, is essential for any innovation leader. This course offers a deep dive into the critical phases of technology companies and their journey through growth, scaling, and eventual market exit. Topics include capital formation, contracts, intellectual property, human capital, and business transactions.

• Department: Technology Management
• T 4:10 p.m.-6 p.m.
• 3 points

Strategic Advocacy

Today, leaders must confront a world of volatility, uncertainty, complexity, and ambiguity. It demands that we strengthen how we lead change. We are all being stretched to learn, unlearn, relearn, and this is especially true for technology leaders – who operate in the ‘eye of the storm’ of relentless change. 

In this context, strategic advocacy -- achieving support for change to address the challenges that confront an organization and the opportunities they provide – requires knowing and applying useful skills, behavior, and practices to win commitment to new, even unanticipated directions.

This is a full-semester core course in the MS in Technology Management executive program designed to expose students to practices, tools, frameworks, concepts, and real-world examples that will help you move from a technical/functional role to a senior executive orientation. Everyone’s journey is unique. As you apply the course content in real life you will be expected to choose, experiment with, and adapt the relevant approaches most meaningful to your situation.

• Department: Technology Management
• R 6:10 p.m.-8 p.m.
• Instructor: Amy Radin
  Info
• 3 points

Digital Transformation

This course explores the principles, strategies, and challenges of technology-driven transformation in organizations. Students will examine emerging technologies, digital disruption, and frameworks for implementing large-scale change. This course provides a comprehensive understanding of digital transformation, focusing on how businesses leverage technology to drive transformation based on various drivers including efficiency, productivity, competitive advantage, and compliance. Students will explore key topics such as product development, systems development lifecycle, enterprise architecture, IT capabilities, and automation. Through case studies, research, and hands-on projects, students will develop the skills needed to lead strategic and technical skills necessary to lead and manage digital transformation initiatives.

• Department: Technology Management
• T 6:10 p.m.-8 p.m.
• W 4:10 p.m.-6 p.m.
• R 6:10 p.m.-8 p.m.
• M 6:10 p.m.-8 p.m.
• Instructor: Richard R. Ranade
  Info
• Instructor: Shahryar Shaghaghi
  Info
• 3 points

GRN BLDG & SUST: TOOLS & TECH

GREEN BLDG & SUS: TOOLS & TECH

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• Department: Construction Administration
• M 6:10 p.m.-8 p.m.
• Instructor: Christopher P Hagen
  Info
• 3 points

SCRIPT TO SCREEN

• Department: Film
• T 6 p.m.-9 p.m.
• R 2 p.m.-5 p.m.
• F 2 p.m.-5 p.m.
• M 10 a.m.-1 p.m.
• Instructor: Mathilde A Hauducoeur
  Info
• Instructor: Trey Ellis
  Wikipedia Info
• Instructor: Yared Zeleke
  Info
• 1.5 points

SCRIPT TO SCREEN

• Department: Film
• M 10 a.m.-1 p.m.
• R 6 p.m.-9 p.m.
• W 9:30 a.m.-12:30 p.m.
• F 2 p.m.-5 p.m.
• 1.5 points

Big Data & Political Strategy

Some experts on U.S. political campaigns have argued that big data has fundamentally changed the way politicians win elections and pursue policymaking. With the combination of massive amounts of personal data and information about individual voters and society at large, readily available processing power, sophisticated machine learning techniques, and cheap and efficient communication methods, modern political professionals are able to identify likely supporters, understand their issues of interest and concern, make direct appeals with micro-targeted messages, and mobilize these constituencies to donate, volunteer, turnout, mobilize, and vote accordingly. Without a doubt, big data has the potential to inform strategic decision-making across multiple aspects of politics.

In this course, students will learn about the range of big data sources that can be gathered and aggregated, including public data, voter file data, consumer data, and more. Students will become familiar with the ways in which data can be used to gain insights about voters’ sentiments, attitudes, and opinions and to develop strategies to predict and prompt behavior.  Most importantly, students will learn to synthesize a variety of data sources into a cohesive strategy and presentation that can be given to decision-makers, whether for electoral or advocacy purposes.

• Department: Political Analytics
• R 2:10 p.m.-4 p.m.
• Instructor: Benjamin Messner
  Info
• 3 points