POETRY WORKSHOP

• Department: Writing
• F 2 p.m.-5 p.m.
• R 2 p.m.-5 p.m.
• F 2 p.m.-5 p.m.
• M 10 a.m.-1 p.m.
• M 1 p.m.-4 p.m.
• Instructor: Mark Bibbins
  Wikipedia Info
• Instructor: Timothy B Donnelly
  Info
• Instructor: Emily F Skillings
  Info
• Instructor: Lynn Xu
  Info
• Instructor: Cynthia D Cruz
  Wikipedia Info
• 6 points

DENTAL ANATOMY & MORPHOLOGY

• Department: Dental and Oral Surgery
• Instructor: Roseanna Graham
  h-index: 4 Info
• 0 points

PRE-CLINICAL OPPERATIVE DENT

PRE-CLINICAL OP DENT

• Department: Dental and Oral Surgery
• Instructor: Roseanna Graham
  h-index: 4 Info
• 0 points

KNOWLEDGE PROCESSES, PRACTICES & GOVERNA

KNWLDGE PROCESSES, PRACT

• Department: Auditing
• 0 points

KNOWLEDGE PROCESSES, PRACTICES & GOVERNA

KNOWLEDGE PROC, PRACT, & GOV

KNOWLEDGE PROCESSES, PRACTICES & GOVERNANCE
  In economic literature, scholars classify factors of production into three major categories: labor, or human services, capital, or manmade means of production, and land, or natural resources. As the complexity and knowledge intensity of industries engaged in manufacturing and service delivery have increased, knowledge has emerged as the fourth factor of production. According to Peter Drucker, the recognized founder of modern management, knowledge is the primary economic resource that allows workers to manipulate ideas, problem solve, and innovate—land, labor, and capital are becoming restraints rather than driving forces of the economy. This course examines the knowledge processes, practices, and governance mechanisms that activate knowledge to drive performance, innovation, continuous improvement, and create competitive advantage. We kick-off by reading Robert Grant’s article on the knowledge-based view of the firm. Grant, an economist and professor of strategic management at Georgetown University, describes how firms integrate, combine, and coordinate individual and firm knowledge to produce goods and services. After this theoretical introduction, students will learn and apply a series of models and frameworks that address how to capture and disseminate critical knowledge to people performing productive work. For example, Business Model and Value Proposition Design (Alexander Osterwalder), Jobs-To-Be-Done Framework (Clayton Christensen), Operational Model Design, (Cambell and Blenko), and Knowledge Jam (Katrina Pugh). After building a business model, the focus will shift to practical methods to operationalize knowledge to make it productive. Students will learn how to create a technology architecture, identify strategies to enable findability, adopt an adaptive leadership approach to business transformation, and propose a disciplined knowledge governance program.  The course culminates with the design of a transformational project that integrates the concepts and models presented in this course. Students will create a project charter and work plan to guide the transformation from traditional ways of working to processes and practices that fully leverage knowledge.

• Department: Information & Knowledge Strat
• R 8 p.m.-10 p.m.
• Instructor: Ralph Poole
  h-index: 0 Info
• 3 points

PRECLINICAL OPERATIVE DENTISTRY LAB

PRECLIN OPERTV DENT LAB

• Department: Dental and Oral Surgery
• Instructor: Roseanna Graham
  h-index: 4 Info
• 0 points

LEADING BUSINESS ANALYTICS FOR THE ENTER

LEADING BUS ANALYTICS FOR

• Department: Auditing
• Instructor: Jeanne G Harris
  Wikipedia h-index: 27 Info
• 0 points

LEADING BUSINESS ANALYTICS FOR THE ENTER

LEADING BUS ANALYTICS FOR

Digital natives ‘built upon an algorithm’ are profoundly changing the structure of entire industries (e.g.: Google/Advertising, Redfin/Real Estate, Netflix/Entertainment). C-suite executives now view analytics as a strategic priority and investing in acquiring data, data scientists and analytical technologies.   The goal of this course is to enable you to develop a pragmatic, executive level understanding of business analytics, data science and business intelligence as well as the critical management skills needed for success in this field. Through readings, assignments, lectures, guest speakers (previous guests include executives from Google, UPS, Microsoft, McKinsey, NY City, World Economic Forum) and class discussion, students learn why and how organizations are ‘competing on analytics’ to improve decision making and optimize performance. Case examples (e.g.: Disney, Arsenal F.C., Unilever, Caterpillar, USAA, Zillow, Target, UPS) highlight the ways firms are building business strategies around sophisticated analytical capabilities.   Students learn how organizations use analytics to innovate and create new digital products and services. They also study how analytics is profoundly transforming the way businesses operate. Rather than “relying on their gut” when pricing products, understanding customers, maintaining inventory, or hiring talent, managers increasingly rely on data analytics and process optimization to improve efficiency, manage risk, enhance profitability, and deliver superior results. An analytical sophistication framework (descriptive, predictive, prescriptive, autonomous) illustrates how different management challenges require specific types of solutions. A key executive challenge for these organizations is to prioritize investments in data, expertise, and technology as part of a cohesive strategy that optimizes results. Using a real organization, students learn how to apply the DELTA+ model to assess an organization’s analytical capabilities and devise a roadmap to build a robust, analytical capability.  While not a statistics or programming course, students will gain insight into the different types of quantitative techniques (e.g., optimization, NLP, forecasting, simulation, neural nets, Bayesian, regression, genetic algorithms) as well technologies/tools for business intelligence, modeling, machine learning, and AI (e.g., SAS, Tableau, Spark, Excel, MATLAB, PowerBI, Oracle Analytics) and languages (Pyth

• Department: Information & Knowledge Strat
• T 8 p.m.-10 p.m.
• Instructor: Jeanne G Harris
  Wikipedia h-index: 27 Info
• 3 points

SCIENCE FOR BIOETHICISTS

SCI FOR BIOETHICISTS (ONLINE)

• Department: Bioethics
• W 4:30 p.m.-6 p.m.
• Instructor: Carolyn R Chapman
  Info
• Instructor: Jennifer M Gumer
  Info
• 3 points

ROLE OF THE PRODUCER

• Department: Film
• F 9 a.m.-noon
• Instructor: Jack Lechner
  Info
• 1.5 points

SQL

SQL & RELATIONAL DB

• Department: Applied Analytics
• T 8:10 p.m.-10 p.m.
• M 6:10 p.m.-8 p.m.
• Instructor: Day Yi
  Info
• Instructor: Nikolaos Machairas
  Info
• 3 points

ETHICAL DECISION MAKING FOR COMMUNICATRS

• Department: Auditing
• Instructor: Frank J Oswald
  Info
• 0 points

ETHICAL DECISION MAKING FOR COMMUNICATOR

ETHICAL DEC MKNG FOR COMM

• Department: Strategic Communication
• M 6:10 p.m.-8 p.m.
• Instructor: Frank J Oswald
  Info
• 3 points

ECONOMIC POLICY ANALYSIS

• Department: Economics
• T 12:10 p.m.-2 p.m.
• 3 points

Economic Policy Analysis - Disc

Econ Policy Analysis - Di

Discussion Section for Economics MA Course Economic Policy Analysis

• Department: Economics
• R 11:30 a.m.-12:45 p.m.
• 0 points

STRATEGIC PHILANTHROPY

• Department: Auditing
• Instructor: Ali Webb
  Info
• 0 points

STRATEGIC PHILANTHROPY

• Department: Non Profit Management
• W 6:30 p.m.-8 p.m.
• Instructor: Ali Webb
  Info
• 3 points

Jewish Medical Ethics

• Department: Jewish Theological Seminary
• 0-10 points

PHILOSOPHY OF BIOETHICS

• Department: Bioethics
• R 12:10 p.m.-2:40 p.m.
• Instructor: Arthur Kuflik
  Info
• 3 points

PHILOSOPHY OF BIOETHICS

PHIL OF BIOETHICS (ONLINE)

• Department: Bioethics
• T 8:10 p.m.-10 p.m.
• W 6:10 p.m.-8 p.m.
• T 8:10 p.m.-10 p.m.
• Instructor: Arthur Kuflik
  Info
• 3 points

STRATEGIC RISK MANAGEMENT

STRATEGIC RISK MGMT

• Department: Enterprise Risk Management
• R 8:10 p.m.-10 p.m.
• W 6:10 p.m.-8 p.m.
• Instructor: Carlo Di Florio
  Info
• Instructor: Celia Kapsomera
  Info
• 3 points

STRATEGIC RISK MANAGEMENT

STRAT RISK MGMT (ONLINE)

• Department: Enterprise Risk Management
• T 8:10 p.m.-10 p.m.
• Instructor: Cecil R House
  Info
• 3 points

FINANCIAL RISK MGMT & REGULATION

Prerequisites: student expected to be mathematically mature and familiar with probability and statistics, arbitrage pricing theory, and stochastic processes. The course will introduce the notions of financial risk management, review the structure of the markets and the contracts traded, introduce risk measures such as VaR, PFE and EE, overview regulation of financial markets, and study a number of risk management failures. After successfully completing the course, the student will understand the basics of computing parametric VaR, historical VaR, Monte Carlo VaR, cedit exposures and CVA and the issues and computations associated with managing market risk and credit risk. The student will be familiar with the different categories of financial risk, current regulatory practices, and the events of financial crises, especially the most recent one.

• Department: Mathematics
• R 8:10 p.m.-10 p.m.
• Instructor: Harvey Stein
  h-index: None Info
• 3 points

NONPROFIT FINANCIAL MANAGEMENT

NONPROFIT FINANCIAL MGMT

• Department: Non Profit Management
• T 4:10 p.m.-6 p.m.
• Instructor: Odell Mays
  Info
• 3 points

LAW & BIOETHICS

• Department: Bioethics
• M 12:10 p.m.-2 p.m.
• Instructor: Susan Regan
  Info
• 3 points

LAW & BIOETHICS

• Department: Bioethics
• M 12:10 p.m.-2 p.m.
• Instructor: Susan Regan
  Info
• 0 points

DIGITAL COMMUNICATION STRATEGY

DIGITAL STRATEGY

As digital media increasingly drives the field of strategic communication, leading successful communication efforts also require a platform specific, evidence-based strategic approach. Leaders must know how to use a broad and rapidly changing mix of digital media platforms and tools to connect their message with the right audience. To that end, this course covers major topics in digital media and communication, such as content strategy, digital experience, channel planning, online reputation management, programmatic marketing, audience targeting, artificial intelligence and more. Through in-class lectures, discussion, case studies, guest speakers, group projects and individual writing assignments, students in this course will be introduced to strategic decision-making and communications planning for social media, mobile, digital advertising, search, email, digital out-of-home and interactive media (video, radio, podcasts). Students will also gain an in-depth understanding of how to integrate digital strategies and tactics with traditional communication efforts.

• Department: Strategic Communication
• M 12:10 p.m.-2 p.m.
• M 10:10 a.m.-noon
• None None-None
• Instructor: Alexandra M Merceron
  Info
• Instructor: Thomas Hafen
  Info
• Instructor: Jamyn Edis
  Info
• 3 points

Real Estate Finance

• Department: Finance
• TWRFS 9 a.m.-10:15 a.m.
• Instructor: Jessica Soursourian
  Info
• 3 points

SPORTS ENTREPRENEURSHIP

• Department: Sports Management
• M 12:10 p.m.-2 p.m.
• Instructor: Grant J Son
  Info
• 3 points

MACHINE LEARNING: CONCEPTS & APPLICATION

• Department: Applied Analytics
• F 12:30 p.m.-2:20 p.m.
• M 8:10 p.m.-10 p.m.
• Instructor: Siddhartha Dalal
  Info
• Instructor: Adrian Heilbut
  h-index: 13 Info
• 3 points

MACHINE LEARNING: CONCEPTS & APPLICATION

MACHINE LEARNING ONLINE

• Department: Applied Analytics
• T 8:10 p.m.-10 p.m.
• Instructor: Jinjun Xiong
  Info
• 3 points

OPERATIONAL RISK MANAGEMENT

OPERATIONAL RISK MGMT

• Department: Enterprise Risk Management
• R 6:10 p.m.-8 p.m.
• W 8:10 p.m.-10 p.m.
• M 8:10 p.m.-10 p.m.
• Instructor: Prasad Kodali
  Info
• Instructor: Rossano Rossi
  Info
• Instructor: Penny Cagan
  Info
• 3 points

OPERATIONAL RISK MANAGEMENT

OPERATIONAL RISK MGMT (ONLINE)

• Department: Enterprise Risk Management
• T 6:10 p.m.-8 p.m.
• Instructor: Umesh Akki
  Info
• 3 points

FIXED INCOME PORTFOLIO MGMT

Prerequisites: comfortable with algebra, calculus, probability, statistics, and stochastic calculus. The course covers the fundamentals of fixed income portfolio management. Its goal is to help the students develop concepts and tools for valuation and hedging of fixed income securities within a fixed set of parameters. There will be an emphasis on understanding how an investment professional manages a portfolio given a budget and a set of limits.

• Department: Mathematics
• F 6:10 p.m.-8 p.m.
• Instructor: Rosanna Pezzo
  Info
• 3 points

INTRO TO QUANTITATIVE RISK MANAGEMENT

INTRO TO QUANT RISK MGMT

• Department: Enterprise Risk Management
• M 8:10 p.m.-10 p.m.
• R 8:10 p.m.-10 p.m.
• Instructor: David J Romoff
  Info
• Instructor: Witold Czubala
  Info
• 3 points

INTRO TO QUANTITATIVE RISK MANAGEMENT

INTRO TO QRM (ONLINE)

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

CAPSTONE PROJECT SEMINAR

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

CAPSTONE PROJECT SEMINAR

• Department: Information & Knowledge Strat
• TR 8 p.m.-10 p.m.
• Instructor: Denise A Rempe
  Info
• 6 points

WRITING NARRATIVE MEDICINE

• Department: Narrative Medicine
• R 6:10 p.m.-8 p.m.
• Instructor: Benjamin Samuel
  Info
• 3 points

NONPROFIT POLICY & ADVOCACY

TBA

• Department: Non Profit Management
• W 4:10 p.m.-6 p.m.
• 3 points

CODING FOR RISK MANAGEMENT

• Department: Enterprise Risk Management
• W 6:10 p.m.-8 p.m.
• Instructor: David J Romoff
  Info
• 3 points

FINANCIAL RISK MANAGEMENT

FINANCIAL RISK MGMT

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

FINANCIAL RISK MANAGEMENT

FINANCIAL RISK MGMT (ONLINE)

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

SPORT ACCOUNTING & FINANCE

• Department: Sports Management
• T 6:10 p.m.-8 p.m.
• 3 points

ETHICAL ISSUES IN MUSEUMS

Ethical questions about museum activities are legion, yet they are usually only discussed when they become headlines in newspapers. At the same time, people working in museums make decisions with ethical and legal issues regularly and seldom give these judgments even little thought. In part, this is due to the fact that many of these decisions are based upon values that become second nature. This course will explore ethical issues that arise in all areas of a museum's operations from governance and management to collections acquisition, conservation, and deaccessioning. We will examine the issues that arise when the ownership of objects in a museum's are questioned; the ethical considerations involved in retention, restitution and repatriation; and what decolonization means for museums.

• Department: Anthropology
• F 2:10 p.m.-4 p.m.
• Instructor: Sally Yerkovich
  Info
• 3 points

HOPE, MEANING AND ETHICS IN CLINICAL CAR

PASTORAL CARE & ETHICS

• Department: Bioethics
• M 6 p.m.-7:30 p.m.
• Instructor: Linda S Golding
  Info
• 3 points

FRM PRACTICES

• Department: Enterprise Risk Management
• T 8:10 p.m.-10 p.m.
• Instructor: David J Romoff
  Info
• 3 points

FUNDRAISING FUNDAMENTALS FOR NONPROFITS

FUNDRAISING FUND FOR NONPRFTS

• Department: Non Profit Management
• M 4:10 p.m.-6 p.m.
• M 6:10 p.m.-8 p.m.
• Instructor: John Hicks
  Info
• Instructor: Leah E Heister
  Info
• 3 points

BASEBALL ANALYTICS

• Department: Sports Management
• R 6:10 p.m.-8 p.m.
• Instructor: Joseph M Rosales
  Info
• 3 points

INSURANCE RISK MANAGEMENT

INSURANCE RISK MGMT

• Department: Enterprise Risk Management
• W 6:10 p.m.-8 p.m.
• Instructor: Mary Ellen Luning
  Info
• 3 points

CREDIT RISK MANAGEMENT

• Department: Enterprise Risk Management
• T 8:10 p.m.-10 p.m.
• Instructor: Michael Leibrock
  Info
• Instructor: Christopher Mann
  Info
• 3 points

CAPSTONE

• Department: Non Profit Management
• T 6:10 p.m.-8 p.m.
• Instructor: Thomas W Watson
  Info
• 3 points

PROFESSIONAL DEVELOPMENT

The course aims to teach MA in Statistics students how to manage their careers and develop professionally. Topics include resume and cover-letter writing, negotiation, mentoring, interviewing skills and communication across global teams. Top professionals from across the globe speak to students and help improve leadership skills.

• Department: Statistics
• MW 11:40 a.m.-12:55 p.m.
• TR 10:10 a.m.-11:25 a.m.
• MW 11:40 a.m.-12:55 p.m.
• 0 points

MA Mentored Research

MA MENTORED RESEARCH

This course is intended to provide a mechanism to MA students in Statistics who undertake on-campus project work or research. The course may be signed up with a faculty member from the Department of Statistics for academic credit. Students seeking to enroll in the course should identify an on-campus project and a congenial faculty member whose research is appealing to them, and who are able to serve as their mentor. Students should then submit an application to enroll in this course, which will be reviewed and approved by the Faculty Director of the MA in Statistics program.

• Department: Statistics
• Instructor: Demissie Alemayehu
  h-index: 14 Info
• Instructor: Siddhartha Dalal
  Info
• 0-1 points

Statistical Fieldwork

Prerequisites: GR5203; GR5204 &GR5205 and at least 4 approved electives This course is an elective course for students in the M.A. in Statistics program that counts towards the degree requirements. To receive a grade and academic credits for this course, students are expected to engage in approved off-campus internships that can be counted as an elective. Statistical Fieldwork should provide students an opportunity to apply their statistical skills and gain practical knowledge on how statistics can be applied to solve real-world challenges.

• Department: Statistics
• Instructor: Demissie Alemayehu
  h-index: 14 Info
• 1 points

MANAGING DATA

• Department: Applied Analytics
• M 8:10 p.m.-10 p.m.
• Instructor: Michael A Silas
  Info
• 3 points

MANAGING DATA

MANAGING DATA ONLINE

• Department: Applied Analytics
• W 8:10 p.m.-10 p.m.
• Instructor: Mohamed Mneimneh
  Info
• 3 points

INTRO TO CLINICAL ETHICS

INTRO CLINICAL ETHICS (ONLINE)

• Department: Bioethics
• T 9 a.m.-10:30 a.m.
• Instructor: Barbra Rothschild
  Info
• 3 points

INTRO TO CLINICAL ETHICS

INTRO CLINICAL ETHICS ONLINE

• Department: Bioethics
• T 9 a.m.-10:30 a.m.
• Instructor: Barbra Rothschild
  Info
• 0 points

DYNAMICS CLIMATE VAR & CHNG

Required for students in the Climate and Society MA Program Prerequisites: undergraduate course in climate or physics; undergraduate calculus An overview of how the climate system works on large scales of space and time, with particular attention to the science and methods underlying forecasts of climate variability and climate change. This course serves as the basic physical science course for the MA program in Climate and Society

• Department: Earth and Environmental Sciences
• TR 2:40 p.m.-3:55 p.m.
• 3 points

PRACTICAL PRODUCTION I

Practical Production 1 teaches students best practices regarding film production and technology in the integrated first year of the MFA Film Program through lectures, discussions, pre-production meetings, multi-hour shoots on set and an end-of-the-semester screening. This class is required for all first-year students. Throughout the Fall, students will work in small production groups to prep and shoot a short script in the Prentis studio.  Each week one group will organize a pre-production meeting and then produce a four-hour shoot. The professor will be in attendance and two de-briefing sessions will occur throughout the production to reiterate best film production practices.  Additional assignments will include the creation of various pre-production, production and wrap paperwork and tech deliverables. Additional mandatory production and risk management workshops will be given. The last class will be a screening of all group films and prep/discussion for the 3-5 exercise shot over Winter Break. Required for all first-year students.

• Department: Film
• F 9 a.m.-noon
• Instructor: Blythe A Frank
  Info
• 1.5 points

MODELS-CLIM NAT/HUM SYSTEMS

Required course for students in the Climate and Society MA program. An overview of how climate-societal and intra-societal relationships can be evaluated and quantified using relevant data sets, statistical tools, and dynamical models. Concepts and methods in quantitative modeling, data organization, and statistical analysis, with applications to climate and climate impacts. Students will also do some simple model experiments and evaluate the results. Lab required. Pre-requisites: undergraduate-level coursework in introductory statistics or data analysis; knowledge of calculus; basic familiarity with R programming language.

• Department: Earth and Environmental Sciences
• M 4:10 p.m.-7 p.m.
• 4 points

MANAGING & ADAPTING TO CLIMATE

This course offers an exploration of the concepts, methods, and tools required to analyze climate-related problems and craft solutions for reducing vulnerability and building resilience to climate variability and change. Drawing on the framework of risk analysis, the course examines and integrates risk assessment, risk perception, risk communication, and risk management. The course explores several forms of climate governance, including market-based and policy responses, as well as the kinds of cultural and behavioral change that can be promoted by communication and education. Rather than focusing in a single discipline, the course spans both social and natural sciences. It also bridges a number of divides, including those between research and applications, between developed and developing countries, and between the temporal scales of climate variability and change.

• Department: Earth and Environmental Sciences
• MW 1:10 p.m.-2:25 p.m.
• 3 points

MATH METHODS FOR ECONOMISTS

• Department: Economics
• MW 9 a.m.-10:15 a.m.
• Instructor: Ceyhun Elgin
  Info
• 3 points

ECONOMETRICS I

• Department: Economics
• MW 10:45 a.m.-noon
• Instructor: Seyhan Erden
  CULPA: 7 Info
• 3 points

PRINTMAKING:DRAWING INTO PRINT

• Department: Visual Arts
• T 10 a.m.-4 p.m.
• Instructor: Ivan L Forde
  Info
• 3 points

ADVANCED ECONOMETRICS

• Department: Economics
• M 10:10 a.m.-noon
• Instructor: Serguei Maliar Pushnoy
  Info
• 3 points

SYMBOLIC LOGIC

Advanced introduction to classical sentential and predicate logic. No previous acquaintance with logic is required; nonetheless a willingness to master technicalities and to work at a certain level of abstraction is desirable. Note: Due to significant overlap, students may receive credit for only one of the following three courses: PHIL UN3411, UN3415, GR5415.

• Department: Philosophy
• TR 10:10 a.m.-11:25 a.m.
• Instructor: Tamar A Lando
  CULPA: 8 Info
• 4 points

ANOMALY DETECTION

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

ANOMALY DETECTION

ANOMALY DETECTION ONLINE

• Department: Applied Analytics
• W 8:10 p.m.-10 p.m.
• Instructor: Andrew Assing
  Info
• 3 points

TECH ARTS:POST PRODUCTION

Tech Arts: Post Production delivers a practical introduction to modern post production workflows. The course will cover the process of moving efficiently from production to post production, the techniques of non-linear editing and ultimately the process of professionally finishing a film for modern distribution. Students will learn foundational post terminology, how to create the best workflow for your film, how to manage data/footage in the edit room, and offline and online editing.  Additionally, the class will explore other key steps in the post production process including audio syncing, transcoding, exporting and mastering.  The hands-on lessons and exercises will be conducted using the industry-standard non-linear editing system (NLE), Avid Media Composer, and will serve as a primer for other professional systems, including Adobe Premiere and Davinci Resolve.  Students will also learn about Columbia Film’s shared storage system and cloud editing systems, Avid Nexis and Avid Media Central. The course is necessary and required for Columbia Film MFA students as it prepares them for post production, an unavoidable component of the most essential part of the Film MFA, filmmaking.

• Department: Film
• R 9 a.m.-noon
• Instructor: Michael J Cacioppo Belantara
  Info
• 0 points

MODEL & TRADE DERIVATIVES

Prerequisites: Math GR5010 Required: Math GR5010 Intro to the Math of Finance (or equivalent),Recommended: Stat GR5264 Stochastic Processes – Applications I (or equivalent) The objective of this course is to introduce students, from a practitioner's perspective with formal derivations, to the advanced modeling, pricing and risk management techniques that are used on derivatives desks in the industry, which goes beyond the classical option pricing courses focusing solely on the theory. The course is divided into four parts: Differential discounting, advanced volatility modeling, managing a derivatives book, and contagion and systemic risk in financial networks.

• Department: Mathematics
• S 10:10 a.m.-noon
• Instructor: Amal Moussa
  h-index: 7 Info
• 3 points

SPORTS GENERAL MANAGEMENT

• Department: Sports Management
• M 4:10 p.m.-6 p.m.
• Instructor: Christopher Lencheski
  Info
• 3 points

APPLIED TEXT AND NATURAL LANGUAGE ANALYT

APPLIED TXT & NAT LANG AN

• Department: Applied Analytics
• M 6:10 p.m.-8 p.m.
• Instructor: Javid B Huseynov
  Info
• 3 points

APPLIED TEXT AND NATURAL LANGUAGE ANALYT

APPLD TXT & NAT LANG AN ONLINE

• Department: Applied Analytics
• M 8:10 p.m.-10 p.m.
• Instructor: John Ortega
  h-index: None Info
• 3 points

WRITING CREATIVELY: CRAFT LAB & WORKSHOP

• Department: Narrative Medicine
• 3 points

PRINTMAKING I: RELIEF

• Department: Visual Arts
• MW 9:30 a.m.-noon
• Instructor: Nathan N Catlin
  Info
• 3 points

FINANCIAL DATA SCIENCE AND MACHINE LEARN

FIN DATA SCI & MACHINE LE

This course teaches cutting-edge tools and methods that drive investment decisions at quantitative trading firms, and, more generally, firms applying machine learning to big data. The course will combine presentations of theory, immediately followed by in-class Python programming examples using real financial data. The course will develop a general approach to building models of economic and financial processes, with a focus on statistical learning techniques that scale to large data sets. Among the topics covered are lasso, elastic net, cross validation, Bayesian models, the EM algorithm, Support Vector Machines, kernel methods, Gaussian processes, Hidden Markov Models, and neural networks. The final project will lead the students to build a trading strategy based on the techniques learned throughout the course.

• Department: Applied Analytics
• M 8:10 p.m.-10 p.m.
• Instructor: William G Ritter
  Info
• 3 points

ORGANIZATIONAL ETHICS AND COMPLIANCE

ORGANIZATIONAL ETHICS & C

• Department: Bioethics
• M 10:10 a.m.-noon
• Instructor: David N Hoffman
  h-index: 2 Info
• 3 points

RESEARCH ETHICS

• Department: Bioethics
• R 4:10 p.m.-6 p.m.
• Instructor: Robert L Klitzman
  Wikipedia Info
• 3 points