PYTHON FOR DATA ANALYSIS

• Department: Applied Analytics
• S 12:10 p.m.-2 p.m.
• T 6:10 p.m.-8 p.m.
• Instructor: Alexander Clark
  Info
• Instructor: Guilherme de Oliveira
  Info
• 3 points

PYTHON FOR DATA ANALYSIS

PYTHON FOR DATA ANALYSIS ONLNE

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

DIRECTING I

Students explore the grammatical rules and narrative elements of cinematic storytelling by completing a minimum of three short, nondialogue exercises and two sound exercises, all shot and edited in video. Emphasizes using the camera as an articulate narrator to tell a coherent, grammatically correct, engaging, and cinematic story. Technical workshops on camera, lighting, sound, and editing accompany the workshops, as well as lectures that provide a methodology for the director.

• Department: Film
• R 6 p.m.-9 p.m.
• R 9 a.m.-noon
• Instructor: Rania Attieh
  Info
• Instructor: Hilary Brougher
  Wikipedia Info
• Instructor: Bogdan G Apetri
  Info
• Instructor: Ramin Bahrani
  Wikipedia Info
• 3 points

CONFLICT RESOLUTION AND DYNAMICAL SYSTEM

CONF RESOLUTION/DYNAMIC SYSTMS

• Department: Negotiation & Conflict Resolution
• U 9 a.m.-5 p.m.
• 3 points

MICROECONOMICS ANALYSIS I

• Department: Economics
• TR 11:40 a.m.-12:55 p.m.
• Instructor: Wouter R Vergote
  CULPA: 15 Info
• 3 points

CONFLICT, SOCIAL NETWORKS AND COMMUNICAT

CONFL SOCIAL NETWKS/COMM

• Department: Auditing
• Instructor: Ted Perlmutter
  Info
• 0 points

CONFLICT, SOCIAL NETWORKS AND COMMUNICAT

CONFLICT SOCIAL NETWORKS

• Department: Negotiation & Conflict Resolution
• Instructor: Ted Perlmutter
  Info
• 3 points

MACROECONOMICS ANALYSIS I

• Department: Economics
• TR 9:30 a.m.-10:45 a.m.
• Instructor: Ronald Miller
  CULPA: 4 Info
• 3 points

ADVANCED MICROECONOMICS

• Department: Economics
• M 4:10 p.m.-6 p.m.
• Instructor: Bernard Salanie
  CULPA: 18 Wikipedia h-index: 40 Info
• 3 points

ADVANCED MACROECONOMICS

• Department: Economics
• W 4:10 p.m.-6 p.m.
• Instructor: Irasema Alonso
  CULPA: 12 Info
• 3 points

QUANT MTHDS IN INVESTMENT MGMT

Prerequisites: Knowledge of statistics basics and programming skills in any programming language. Surveys the field of quantitative investment strategies from a buy side perspective, through the eyes of portfolio managers, analysts and investors. Financial modeling there often involves avoiding complexity in favor of simplicity and practical compromise. All necessary material scattered in finance, computer science and statistics is combined into a project-based curriculum, which give students hands-on experience to solve real world problems in portfolio management. Students will work with market and historical data to develop and test trading and risk management strategies. Programming projects are required to complete this course.

• Department: Mathematics
• T 7:40 p.m.-10 p.m.
• Instructor: Alexander Greyserman
  Info
• 3 points

FOUNDATIONS OF SPORTS MANAGEMENT

FOUNDATIONS OF SPORTS MGMT

• Department: Sports Management
• T 2:10 p.m.-4 p.m.
• R 6:10 p.m.-8 p.m.
• Instructor: Scott Rosner
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• Instructor: Christopher Lencheski
  Info
• 3 points

BAYESIAN STATISTICS

This course introduces the Bayesian paradigm for statistical inference.  Topics covered include prior and posterior distributions: conjugate priors, informative and non-informative priors; one- and two-sample problems; models for normal data, models for binary data, Bayesian linear models, Bayesian computation: MCMC algorithms, the Gibbs sampler; hierarchical models; hypothesis testing, Bayes factors, model selection; use of statistical software.   Prerequisites: A course in the theory of statistical inference, such as STAT GU4204/GR5204 a  course in statistical modeling and data analysis such as STAT GU4205/GR5205.

• Department: Statistics
• MW 6:10 p.m.-7:25 p.m.
• Instructor: Ronald Neath
  CULPA: 13 Info
• 3 points

DELIVERING THE STRAT MESSAGE

• Department: School of Professional Studies (DVSP)
• W 6:10 p.m.-8 p.m.
• Instructor: Matthew Frederick
  Info
• 3 points

INNOVATIONS IN PHILANTHROPY

• Department: Auditing
• Instructor: Richard T Feiner
  Info
• 0 points

DIRECTING ACTORS I

A workshop in which the student explores the craft and vocabulary of the actor through exercises and scene study as actors and the incorporation of the actor's vocabulary in directed scenes. Exploration of script analysis, casting, and the rehearsal process.

• Department: Film
• T 2 p.m.-5 p.m.
• Instructor: John Rubin
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• Instructor: Mikael Sodersten
  Info
• Instructor: Jodie Markell
  Wikipedia Info
• Instructor: Shira-Lee Shalit
  Info
• Instructor: June Stein
  Info
• Instructor: Rachel Israel
  Wikipedia Info
• 3 points

INNOVATIONS IN PHILANTHROPY

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

ATHLETE ACTIVISM & SOCL JUSTICE IN SPRTS

• Department: Sports Management
• T 4:10 p.m.-6 p.m.
• Instructor: Leonard Elmore
  Info
• 3 points

THE EARTH'S CLIMATE SYSTEM

• Department: Sustainability Management
• T 4:10 p.m.-6 p.m.
• Instructor: Benjamin I Cook
  CULPA: 3 h-index: 56 Info
• 3 points

SAMPLE SURVEYS

Prerequisites: STAT GR5204 Introductory course on the design and analysis of sample surveys. How sample surveys are conducted, why the designs are used, how to analyze survey results, and how to derive from first principles the standard results and their generalizations. Examples from public health, social work, opinion polling, and other topics of interest.

• Department: Statistics
• MW 1:10 p.m.-2:25 p.m.
• Instructor: Rongning Wu
  CULPA: 1 Info
• 3 points

ADVANCED MACHINE LEARNING

Prerequisites: STAT GR5241 This course covers some advanced topics in machine learning and has an emphasis on applications to real world data. A major part of this course is a course project which consists of an in-class presentation and a written project report.

• Department: Statistics
• MW 4:10 p.m.-5:25 p.m.
• TR 10:10 a.m.-11:25 a.m.
• 3 points

APPLIED DATA SCIENCE

Prerequisites: Pre-requisite for this course includes working knowledge in Statistics and Probability, data mining, statistical modeling and machine learning. Prior programming experience in R or Python is required. This course will incorporate knowledge and skills covered in a statistical curriculum with topics and projects in data science. Programming will covered using existing tools in R. Computing best practices will be taught using test-driven development, version control, and collaboration. Students finish the class with a portfolio of projects, and deeper understanding of several core statistical/machine-learning algorithms. Short project cycles throughout the semester provide students extensive hands-on experience with various data-driven applications.

• Department: Statistics
• W 6:10 p.m.-8:55 p.m.
• Instructor: Ying Liu
  CULPA: 2 h-index: 12 Info
• 3 points

Introduction to TensorFlow with Python

Intro. TensorFlow with Py

This course is an optional companion lab course for GR5242 Advanced Machine Learning. The aim of this course is to help students acquire the basic computational skills in Tensorflow and Python to implement machine learning models. Lab class materials will be aligned closely with the topics covered in GR5242. Google Colab and Jupyter notebooks will be used as the main tools for the hands-on lab exercises.  Open to GR5242 students only.
Prerequisites
Some familiarity with python is assumed, but we will begin the class with a tutorial on 'Python for machine learning'.

• Department: Statistics
• W 7 p.m.-8 p.m.
• W 8:10 p.m.-9:10 p.m.
• M 7 p.m.-8 p.m.
• Instructor: Ka-Yi Ng
  Info
• 1 points

DATA ANALYTICS/METRICS IN NONPROF SECTOR

• Department: Auditing
• Instructor: Erwin Vincent de Leon
  Info
• 0 points

EXTERNAL STAKEHOLDER REQUIREMENTS

• Department: Enterprise Risk Management
• T 6:10 p.m.-8 p.m.
• Instructor: R S Forston
  Info
• 3 points

EXTERNAL STAKEHOLDER REQUIREMENTS

EXT STAKEHOLDER REQ (ONLINE)

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

CONFLICT RESOLUTION FIELDWORK

NECR FIELDWORK

• Department: Negotiation & Conflict Resolution
• U 10 a.m.-12:15 p.m.
• Instructor: Nikolas Katsimpras
  Info
• 1 points

DATA ANALYTICS/METRICS IN THE NONPROFIT

Data Analytics

• Department: Non Profit Management
• W 2:10 p.m.-4 p.m.
• Instructor: Erwin Vincent de Leon
  Info
• 3 points

DATA ANALYTICS/METRICS IN THE NONPROFIT

DATA ANLTCS/METRICS IN NONPRFT

Data Analytics

• Department: Non Profit Management
• T 6:30 p.m.-8 p.m.
• Instructor: Annie Rhodes
  Info
• 3 points

DATA ANALYSIS AND VISUALIZATION IN SUSTA

DATA ANALYSIS & VIS IN SU

• Department: Auditing
• 0 points

DATA ANALYSIS AND VISUALIZATION IN SUSTA

DATA ANALYSIS & VIS SUST

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

CORPORATE, PRIVATE, COMMUNTY FOUNDATIONS

• Department: Auditing
• Instructor: John Tyler
  h-index: 3 Info
• 0 points

VALUE OF BRAND

VALUE OF BRAND:MAKNG-BUSI

• Department: Strategic Communication
• Instructor: Tasha Space
  Info
• 3 points

ORGANIZATIONAL STRATEGY AND LEARNING

ORG STRAT AND LEARNING

• Department: Human Capital Management
• M 6:30 p.m.-8 p.m.
• W 8:30 p.m.-10 p.m.
• Instructor: Nabeel Ahmad
  Info
• Instructor: Sara Daly-Padron
  Info
• 3 points

CORPORATE, PRIVATE, COMMUNTY FOUNDATIONS

CORP PRIV COMM FOUNDATIONS

• Department: Non Profit Management
• M 6:30 p.m.-8 p.m.
• Instructor: John Tyler
  h-index: 3 Info
• 3 points

STATISTICAL METHODS IN FINANCE

Prerequisites: STAT GR5204 or the equivalent. STAT GR5205 is recommended. A fast-paced introduction to statistical methods used in quantitative finance. Financial applications and statistical methodologies are intertwined in all lectures. Topics include regression analysis and applications to the Capital Asset Pricing Model and multifactor pricing models, principal components and multivariate analysis, smoothing techniques and estimation of yield curves statistical methods for financial time series, value at risk, term structure models and fixed income research, and estimation and modeling of volatilities. Hands-on experience with financial data.

• Department: Statistics
• F 8:40 a.m.-11:25 a.m.
• Instructor: Hammou El Barmi
  CULPA: 8 Info
• 3 points

STAT INF/TIME-SERIES MODELLING

Prerequisites: STAT GR5205 or the equivalent. Available to SSP, SMP Modeling and inference for random processes, from natural sciences to finance and economics. ARMA, ARCH, GARCH and nonlinear models, parameter estimation, prediction and filtering.

• Department: Statistics
• TR 6:10 p.m.-7:25 p.m.
• S 10:10 a.m.-12:40 p.m.
• Instructor: Li Haoran
  Info
• 3 points

STOCHASTC PROCSSES-APPLICTNS I

Prerequisites: STAT GR5203 or the equivalent. Basics of continuous-time stochastic processes. Wiener processes. Stochastic integrals. Ito's formula, stochastic calculus. Stochastic exponentials and Girsanov's theorem. Gaussian processes. Stochastic differential equations. Additional topics as time permits.

• Department: Statistics
• MW 4:10 p.m.-5:25 p.m.
• MW 6:10 p.m.-7:25 p.m.
• Instructor: Johannes Wiesel
  h-index: 6 Info
• Instructor: Lars T Nielsen
  h-index: 17 Info
• 3 points

MANAGING THE MISSION-BASED ORGANIZATION

• Department: Auditing
• Instructor: Carlos Ponce
  Info
• 0 points

MANAGING THE MISSION-BASED ORGANIZATION

• Department: Non Profit Management
• T 6:10 p.m.-8 p.m.
• Instructor: Carlos Ponce
  Info
• 3 points

TRANSFORMING TOTAL REWARDS

• Department: Human Capital Management
• W 6:10 p.m.-8 p.m.
• M 8:30 p.m.-10 p.m.
• Instructor: Hassan Abdulhaqq
  Info
• Instructor: Solange Charas
  h-index: 4 Info
• 3 points

DIGITAL HR AND THE DIGITAL WORKPLACE

DIGITAL HR & DIGITAL WORK

• Department: Human Capital Management
• T 6:10 p.m.-8 p.m.
• Instructor: Ashley K Buonasera
  Info
• 3 points

CAPITAL MARKETS & INVESTMENTS

Risk/return tradeoff, diversification and their role in the modern portfolio theory, their consequences for asset allocation, portfilio optimization. Capitol Asset Pricing Model, Modern Portfolio Theory, Factor Models, Equities Valuation, definition and treatment of futures, options and fixed income securities will be covered.

• Department: Mathematics
• S 7 p.m.-9:20 p.m.
• Instructor: Alexei Chekhlov
  CULPA: 3 h-index: 10 Info
• 3 points

ROLE & UNIQUE NATURE OF NONPROFIT SECTOR

ROLE & UNIQUE NATURE NONP

• Department: Auditing
• Instructor: Gregory Witkowski
  Info
• 0 points

ROLE & UNIQUE NATURE OF NONPROFIT SECTOR

ROLE & UNIQUE NATURE NONP

• Department: Auditing
• Instructor: Gregory Witkowski
  Info
• 0 points

ROLE & UNIQUE NATURE OF NONPROFIT SECTOR

ROLE & UNIQUE NATURE NONP

• Department: Auditing
• Instructor: Gregory Witkowski
  Info
• 0 points

NARRATIVES-DEATH,LIVING,CARING

NARRATIVES, DEATH, LIVING & CA

• Department: Narrative Medicine
• T 6:10 p.m.-8 p.m.
• Instructor: Deborah Starr
  Info
• 3 points

ROLE & UNIQUE NATURE OF NONPROFIT SECTOR

ROLE & UNIQUE NATURE OF NONPRF

• Department: Non Profit Management
• M 2:10 p.m.-4 p.m.
• W 4:10 p.m.-6 p.m.
• T 6:10 p.m.-8 p.m.
• T 6:30 p.m.-8 p.m.
• Instructor: Gregory Witkowski
  Info
• 3 points

ADVANCED DATA ANALYSIS

Prerequisites: W4315 and either another statistics course numbered above the 4200 or permission of instructor. Required for the major in statistics. Data analysis using a computer statistical package and selected exploratory data analysis subroutines. Topics include editing of data for errors, exploratory and standard techniques for one-way analysis of variance, linear regression, and two-way analysis of variance. Material is presented in case-study format.

• Department: Statistics
• F 6:10 p.m.-8:40 p.m.
• Instructor: Demissie Alemayehu
  h-index: 14 Info
• 3 points

TOPICS IN MODERN STATISTICS

APP MACHINE LRNING IMAGE ANALY

Topics in Modern Statistics will provide MA Statistics students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field.

• Department: Statistics
• W 7 p.m.-9:30 p.m.
• Instructor: Xiaofu He
  h-index: 21 Info
• 3 points

TOPICS IN MODERN STATISTICS

INTRO TO PROB GRAPHICAL MODELS

Topics in Modern Statistics will provide MA Statistics students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field.

• Department: Statistics
• M 7 p.m.-9:30 p.m.
• Instructor: Ori Shental
  Info
• 3 points

TOPICS IN MODERN STATISTICS

INTRO TO PROB GRAPHICAL MODELS

Topics in Modern Statistics will provide MA Statistics students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field.

• Department: Statistics
• M 7 p.m.-9:30 p.m.
• Instructor: Ori Shental
  Info
• 3 points

TOPICS IN MODERN STATISTICS

FIN TECH & DATA DRIVEN INNOVTN

Topics in Modern Statistics will provide MA Statistics students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field.

• Department: Statistics
• TR 10:10 a.m.-11:25 a.m.
• Instructor: Margaret Holen
  Info
• 3 points

TOPICS IN MODERN STATISTICS

STAT ADVCS IN DIFRNTL PRIVACY

Topics in Modern Statistics will provide MA Statistics students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field.

• Department: Statistics
• M 10:10 a.m.-11:25 a.m.
• Instructor: Marco A Avella Medina
  Info
• 1-2 points

RESEARCH DESIGN

• Department: Applied Analytics
• R 6:10 p.m.-8 p.m.
• R 8:10 p.m.-10 p.m.
• R 6:10 p.m.-8 p.m.
• W 6:10 p.m.-8 p.m.
• F 12:10 p.m.-2 p.m.
• W 8:10 p.m.-10 p.m.
• Instructor: Laura A Sywulak
  Info
• 3 points

RESEARCH DESIGN

RESEARCH DESIGN ONLINE

• Department: Applied Analytics
• M 8:10 p.m.-10 p.m.
• Instructor: Natasa Rajicic
  Info
• 3 points

STRATEGIC COMM FOR RISK PROFESSIONALS

STRAT COMM FOR RISK PROF

• Department: Enterprise Risk Management
• W 6:10 p.m.-8 p.m.
• M 8:10 p.m.-10 p.m.
• R 6:10 p.m.-8 p.m.
• M 6:10 p.m.-8 p.m.
• W 6:10 p.m.-8 p.m.
• Instructor: Sim Segal
  Info
• Instructor: Violet Diamant
  Info
• Instructor: Helda Rock
  Info
• Instructor: Violet Diamant
  Info
• Instructor: Helda Rock
  Info
• Instructor: Peter Barnes
  Info
• Instructor: Jennifer C Hoffman
  Info
• 3 points

STRATEGIC COMM FOR RISK PROFESSIONALS

STRAT COMM FOR RISK PROF (ONL)

• Department: Enterprise Risk Management
• W 8:10 p.m.-10 p.m.
• Instructor: Saba Zahid
  Info
• 3 points

Corporate Finance

• Department: Finance
• Instructor: Donna M Hitscherich
  Info
• 3 points

Corporate Finance

• Department: Finance
• Instructor: Donna M Hitscherich
  Info
• 3 points

CAPSTONE SEMINAR

• Department: Human Capital Management
• T 4:10 p.m.-6 p.m.
• Instructor: Steven I Safier
  Info
• 3 points

FOUNDATIONS FOR THE KNOWLEDGE-DRIVEN ORG

FOUND. FOR KNWLDGE-DRIVEN

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

CLAIMS MANAGEMENT

• Department: Insurance Management
• Instructor: Richard Woollams
  Info
• 3 points

HEDGE FUNDS STRATEGIES & RISK

The hedge fund industry has continued to grow after the financial crisis, and hedge funds are increasingly important as an investable asset class for institutional investors as well as wealthy individuals. This course will cover hedge funds from the point of view of portfolio managers and investors. We will analyze a number of hedge fund trading strategies, including fixed income arbitrage, global macro, and various equities strategies, with a strong focus on quantitative strategies. We distinguish hedge fund managers from other asset managers, and discuss issues such as fees and incentives, liquidity, performance evaluation, and risk management. We also discuss career development in the hedge fund context.

• Department: Mathematics
• T 8:10 p.m.-10 p.m.
• Instructor: Eric Yeh
  Info
• 3 points

QUALITATIVE METHODS & RESEARCH

• Department: Narrative Medicine
• M 6:10 p.m.-9 p.m.
• Instructor: Edgar Rivera Colon
  Info
• 4 points

SCULPTURE I

• Department: Visual Arts
• F 10 a.m.-4 p.m.
• Instructor: Jon Kessler
  CULPA: 5 Wikipedia Info
• 3 points