MARKETING STRATEGY

Prerequisites: BUSI PS5020 Introduction to Marketing/or Professor Approval is required Students will develop analytical skills used to formulate and implement marketing driven strategies for an organization. Students will develop a deeper understanding of marketing strategies and how to implement tactics to achieve desired goals. Students will work on case study projects in both individual and a team based projects. By the end of this course you will be able to develop a marketing strategy based market assessments and company needs.

• Department: School of Professional Studies (DVSP)
• M 6:10 p.m.-8 p.m.
• Instructor: Andrew Blum
  Info
• 3 points

CINEMA HIST IV:AFTER 1990

CINEMA HIST IV: AFTER 1990

• Department: Film
• R 2:10 p.m.-4:55 p.m.
• Instructor: Richard A Pena
  CULPA: 16 Info
• 3 points

ILLNESS/DISABILITY NARRATIVES

• Department: Narrative Medicine
• T 6:10 p.m.-9 p.m.
• Instructor: Griffith T Couser
  Info
• 4 points

BODIES, ILLNESS & CARE

What is the meaning of embodiment? For most of us, it seems this is a question we only confront when our bodies “break down,” “fail,” or “betray” us. In truth, however, it is a question we are answering with every intentional movement, feeling, impulse and desire of our conscious life. In this course, we will explore various approaches to embodiment from contemporary thinkers. We will ask what these approaches might tell us about the experience of illness and how they might inform our understanding of 
care
, both of ourselves and others.

• Department: Narrative Medicine
• M 12:10 p.m.-2 p.m.
• 3 points

DEVELOP/IMPLEMENTING NEW IDEAS

Interested in starting your own company? Do you have an idea for a new product or service? Have you come up with a way to improve something that already exists? This course tackles the central business concept of how one creates, builds and leads companies. It looks at aspects of entrepreneurship and leadership for both individuals and teams in the face of complex situations. Using the case study method as taught in business school, also known as participant-centered learning, this course puts students in the role of an entrepreneur facing critical business decisions. A selection of guest speakers will offer firsthand experience on innovation and entrepreneurship.

• Department: School of Professional Studies (DVSP)
• R 6:10 p.m.-8 p.m.
• Instructor: Michael E Jr McGuire
  Info
• 3 points

NUMERICAL METHODS IN FINANCE

Prerequisites: some familiarity with the basic principles of partial differential equations, probability and stochastic processes, and of mathematical finance as provided, e.g. in MATH W5010. Prerequisites: some familiarity with the basic principles of partial differential equations, probability and stochastic processes, and of mathematical finance as provided, e.g. in MATH W5010. Review of the basic numerical methods for partial differential equations, variational inequalities and free-boundary problems. Numerical methods for solving stochastic differential equations; random number generation, Monte Carlo techniques for evaluating path-integrals, numerical techniques for the valuation of American, path-dependent and barrier options.

• Department: Mathematics
• MW 7:40 p.m.-8:44 p.m.
• Instructor: Tat Sang Fung
  Info
• 3 points

OBSERVING AND UNDERSTANDING SEA LEVEL CH

OBS & UNDRSTNDNG SEA LEVE

This course provides an overview of the science related to observing and understanding sea-level rise, which has a profound impact on the sustainability of coastal cities and ecosystems. In modern research, sea-level rise is viewed as a complex response of the Earth “system of systems” to climate change. Measuring ongoing sea-level change is challenging due to the great natural variability of sea level on short time scales caused by tides, weather, and ocean currents.  Interpreting measurements so that one can assess (and mitigate against) potential economic and societal impacts of sea-level rise is crucial but can be complicated, since so many Earth-system processes play a role. Some of these processes are related and others are unrelated to climate change; some of the latter are natural and others are of anthropogenic origin. Students enrolled in this course will through lectures and class discussions address topics related to the underlying observational basis for sea-level rise. Given the complexity of sea level rise, it is important for those in technical positions to understand the systems level interactions that not only lead to rising waters but also the consequences that these changes inflict on other parts of our environment. What we hear most commonly is that sea level rise will affect hundreds of millions of people living in coastal areas and make those populations susceptible to flooding. But in addition to this community effect, sea level rise also have dramatic effects on coastal habitats, leading to issue such as erosion, soil contamination, and wetland flooding, just to name a few. This course will introduce and prepare students to develop a more comprehensive and holistic approach to sea level rise. By training students to observe, measure, interpret, and begin to predict how sea level rise affects populations and communities differently, students will be in strong positions to address, mitigate, and adapt to the challenges more effectively using evidence-based approaches.

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

Genetics

Prerequisites: BIOL UN2005 and BIOL UN2006 or the equivalent. General genetics course focused on basic principles of transmission genetics and the application of genetic approaches to the study of biological function. Principles will be illustrated using classical and contemporary examples from prokaryote and eukaryote organisms, and the experimental discoveries at their foundation will be featured. Applications will include genetic approaches to studying animal development and human diseases.  All students must get permission from the instructor to be added from the waitlist.

• Department: Biological Sciences
• TR 10:10 a.m.-11:25 a.m.
• 3 points

DECISION MODELS & MANAGEMENT

This course provides an introduction to computer-based models for decision-making.  The emphasis is on models that are widely used in diverse industries and functional areas, including finance, accounting, operations, and marketing.  Applications will include advertising planning, revenue management, asset-liability management, environmental policy modeling, portfolio optimization, and corporate risk management, among others. The applicability and usage of computer-based models have increased dramatically in recent years, due to the extraordinary improvements in computer, information and communication technologies, including not just hardware but also model-solution techniques and user interfaces.  Twenty years ago working with a model meant using an expensive mainframe computer, learning a complex programming language, and struggling to compile data by hand; the entire process was clearly marked “experts only.”  The rise of personal computers, friendly interfaces (such as spreadsheets), and large databases has made modeling far more accessible to managers.  Information has come to be recognized as a critical resource, and models play a key role in deploying this resource, in organizing and structuring information so that it can be used productively.

• Department: Sustainability Management
• M 6:10 p.m.-8 p.m.
• Instructor: Lucius J Riccio
  CULPA: 4 Info
• 3 points

GHG EMISSIONS: MEASURING AND MINIMIZING

GHG EMISSIONS CARBON FOOT

Environmental, social and governance issues (‘ESG’) are moving to center stage for corporate boards and executive teams.  This elective course complements management and operations courses by focusing on the perspective and roles of the board and C-suite of corporations, financial institutions and professional firms in addressing ESG risks as well as promoting and overseeing governance aligned with ESG principles. The course focuses on the interchange between the external legal, competitive, societal, environmental and policy ‘ecosystems’ corporations face (which vary around the world) and a company’s internal structure, operations and pressures. We will use the United Nations Guiding Principles on Business and Human Rights and the UN Global Compact Principles (which incorporate all aspects of ESG) as the central frameworks to explore the concept of a corporation’s responsibility to respect and remedy human rights and environmental harms.  We will also examine the Equator Principles and other frameworks that spell out good practices for project finance and other investment decisions, and reference a wide range of the myriad indices, supplier disclosure portals and benchmarks that exist in this inter-disciplinary field.  Relevant regulations, corporate law regimes and court cases will be discussed from the point of view of what business managers need to know. While most of the course will deal with companies and firms serving global, regional or national markets, several examples will deal with the question of how the ESG ecosystem affects or offers opportunities to start-ups.

• Department: Sustainability Management
• M 6:10 p.m.-8 p.m.
• Instructor: Cynthia E Cummis
  Info
• 3 points

SECURITY ANALYSIS

Prerequisites: BUSI PS5001 Intro to Finance and BUSI PS5003 Corporate Finance or Professor Approval required. If you have not taken PS5001 or PS5003 at Columbia University, please contact the course instructor for approval. Students will learn about the valuation of publicly traded equity securities. By the end of the semester students will be able to perform fundamental analysis (bottoms-up, firm-level, business and financial analysis), prepare pro forma financial statements, estimate free cash flows and apply valuation models.

• Department: School of Professional Studies (DVSP)
• R 6:10 p.m.-8 p.m.
• Instructor: Panos Mourdoukoutas
  Wikipedia Info
• 3 points

American Film: Comedy

THE WESTERN

Grad section for FILM UN 2190 Topics in American Cinema.   This course surveys the American film genre known as film noir, focusing primarily on the genre’s heyday in the 1940s and early 1950s, taking into account some of its antecedents in the hard-boiled detective novel, German Expressionism, and the gangster film, among other sources. We will consider a number of critical and theoretical approaches to the genre, and will also study a number of film noir adaptations and their literary sources.

• Department: Film
• MW 10:10 a.m.-11:25 a.m.
• Instructor: James A Schamus
  CULPA: 8 Wikipedia Info
• 3 points

American Film: Comedy

CULT & EXPLOITATION

Grad section for FILM UN 2190 Topics in American Cinema.   This course surveys the American film genre known as film noir, focusing primarily on the genre’s heyday in the 1940s and early 1950s, taking into account some of its antecedents in the hard-boiled detective novel, German Expressionism, and the gangster film, among other sources. We will consider a number of critical and theoretical approaches to the genre, and will also study a number of film noir adaptations and their literary sources.

• Department: Film
• R 11:25 a.m.-12:55 p.m.
• Instructor: Robert King
  CULPA: 7 Info
• 3 points

ESG-ALIGNED CORPORATE GOVERNANCE

ESG-ALIGNED CORP GOVER

Environmental, social and governance issues (‘ESG’) are moving to center stage for corporate boards and executive teams.  This elective course complements management and operations courses by focusing on the perspective and roles of the board and C-suite of corporations, financial institutions and professional firms in addressing ESG risks as well as promoting and overseeing governance aligned with ESG principles. The course focuses on the interchange between the external legal, competitive, societal, environmental and policy ‘ecosystems’ corporations face (which vary around the world) and a company’s internal structure, operations and pressures. We will use the United Nations Guiding Principles on Business and Human Rights and the UN Global Compact Principles (which incorporate all aspects of ESG) as the central frameworks to explore the concept of a corporation’s responsibility to respect and remedy human rights and environmental harms.  We will also examine the Equator Principles and other frameworks that spell out good practices for project finance and other investment decisions, and reference a wide range of the myriad indices, supplier disclosure portals and benchmarks that exist in this inter-disciplinary field.  Relevant regulations, corporate law regimes and court cases will be discussed from the point of view of what business managers need to know. While most of the course will deal with companies and firms serving global, regional or national markets, several examples will deal with the question of how the ESG ecosystem affects or offers opportunities to start-ups.

• Department: Sustainability Management
• R 6:10 p.m.-8 p.m.
• Instructor: Jo Weiss
  Info
• 3 points

SUSTAINABILITY IN THE FACE OF NATURAL DI

SUST IN FACE OF NATRL DIS

Natural hazards, naturally occurring phenomena, which can lead to great damage and loss of life, pose a great challenge for the sustainability of communities around the world. This course aims to prepare students to tackle specific hazards relevant to their life and work by providing them the scientific background and knowledge of the environmental factors that combine to produce natural disasters. The course will also train students about the methods used to study certain aspects of natural hazards and strategies for assessing risk and preparing communities and businesses for natural disasters. The course will cover a range of natural hazards, including geological, hydro-meteorological, and biological. The course will emphasize the driving physical, chemical and biological processes controlling the various hazards, and the observation and modeling methods used by scientists to assess and monitor events. Many case examples, including hurricanes, earthquakes and volcanic eruptions that occurred in the last five years, will be given and analyzed for the characteristics of the event, the preparation, and the response. By providing students with a solid understanding of past natural disasters, the course prepares them to think more critically about creating more resilient communities, which can resist catastrophic events. Students will be studying the underpinning scientific principles of natural disasters but will also learn specific strategies for planning, mitigation, and response. During the course, students will master cutting-edge tools and technologies that will prepare them to work in the complex and demanding field of disaster management. After completing the course, students will be able to understand past events, communicate risk, and make critical decision related to disaster and preparedness. In increasingly unpredictable times, there is a need for more resilient and connected communities, and this particular course will train students in both the knowledge and skills needed to lead and strengthen those communities and resilience efforts at scale.
Advising Note:
Students are expected to have taken college-level Calculus, Physics, and Introductory Statistics. Students are expected to have experience with computer based data analysis (Excel, R, Matlab or Python).

• Department: Sustainability Technology
• R 6:10 p.m.-8 p.m.
• 3 points

MATH FINANCE PRACTITIONERS SEM

Prerequisites: MATH W5010 or knowledge of J. Hulls book Options, futures. Prerequisites: Math GR5010 or knowledge of J. Hulls book Options, futures. Seminar consists of presentations and mini-courses by leading industry specialists in quantitative finance. Topics include portfolio optimization, exotic derivatives, high frequency analysis of data and numerical methods. While most talks require knowledge of mathematical methods in finance, some talks are accessible to general audience.

• Department: Mathematics
• TR 7:40 p.m.-8:55 p.m.
• Instructor: Lars T Nielsen
  h-index: 17 Info
• 3 points

INTRODUCTION TO HEALTH CARE NEGOTIATION

TBA

• Department: Auditing
• Instructor: Rodney K Jr. Erb
  Info
• 0 points

INTRODUCTION TO HEALTH CARE NEGOTIATION

TBA

• Department: Negotiation & Conflict Resolution
• Instructor: Rodney K Jr. Erb
  Info
• 3 points

INTERNSHIP IN SOCIAL RESEARCH

This course gives students two credits of academic credit for the work they perform in such an social science oriented internships.

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

INTERNSHIP IN SOCIAL RESEARCH II

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

PRACTICUM IN DATA ANALYSIS

This practicum course is meant to offer valuable training to students. Specifically, this practicum will mimicthe typical conditions that students would face in an internship in a large data-intense institution. Thepracticum will focus on four core elements involved in most internships: (1) Developing the intuition andskills to properly scope ambiguous project ideas; (2) practicing organizing and accessing a variety oflarge-scale data sources and formats; (3) conducting basic and advanced analysis of big data; and (4)communicating and “productizing” results and findings from the earlier steps, in things like dashboards,reports, interactive graphics, or apps. The practicum will also give students time to reflect on their work, andhow it would best translate into corporate, non-profit, start-up and other contexts.

• Department: Quantitative Methods/Social Sciences
• W 8:10 p.m.-10 p.m.
• Instructor: Aracelis Z Torres
  Info
• 2 points

METHODS WORKSHOP

Introducing students to a series of methods, methodological discussions, and questions relevant to the focus of the Masters program: urban sociology and the public interest. Three methodological perspectives will frame discussions: analytical sociology, small-n methods, and actor-network theory.

• Department: Sociology
• M 9 a.m.-11 a.m.
• Instructor: Ryan A Hagen
  Info
• 4 points

PRACTICUM IN DATA ANALYSIS II

This practicum will mimic the typical conditions that students would face in an internship in a large data-intense institution. The practicum will focus on four core elements involved in most internships: • developing the intuition and skills to properly scope ambiguous project ideas; • practicing organizing and accessing a variety of large-scale data sources and formats; • conducting basic and advanced analysis of big data; and • communicating and “productizing” results and findings from the earlier steps, in things like dashboards, reports, interactive graphics, or apps. The practicum will also give students time to reflect on their work, and how it would best translate into corporate, non-profit, start-up and other contexts.

• Department: Quantitative Methods/Social Sciences
• W 8:10 p.m.-10 p.m.
• Instructor: Aracelis Z Torres
  Info
• 2 points

SUSTAINABLE FASHION AND STARTUP STRATEGY

SUST FASHION & STARTUP ST

Fashion’s consistent ranking among the top 3 global polluters has become a decades old fact struggling to gain a proportionate response among the brand startup and sourcing community. With industry revenues set to exceed $1 trillion, there is an opportunity to critically address existing revenue models predicated on traditional metrics, such as constant growth, and singular bottom lines. The course attempts to create a nexus between the fashion entrepreneur and systems thinker to explore strategic solutions that address sustainability though an environmental, social and economic lens. The aim is to foster a mindful, yet critical discourse on fashion industry initiatives, past and present, and to practice various tools that help transition existing organizations and incubate new startups towards sustainable outcomes.

• Department: Auditing
• W 6:10 p.m.-8 p.m.
• Instructor: Ammar Belal
  Info
• 0 points

SOCIAL NETWORK ANALYSIS

The course is designed to teach students the foundations of network analysis including how to manipulate, analyze and visualize network data themselves using statistical software. We will focus on using the statistical program R for most of the work. Topics will include measures of network size, density, and tie strength, measures of network diversity, sampling issues, making ego-nets from whole networks, distance, dyads, homophily, balance and transitivity, structural holes, brokerage, measures of centrality (degree, betweenness, closeness, eigenvector, beta/Bonacich), statistical inference using network data, community detection, affiliation/bipartite networks, clustering and small worlds; positions, roles and equivalence; visualization, simulation, and network evolution over time.

• Department: Quantitative Methods/Social Sciences
• F 5:10 p.m.-7 p.m.
• Instructor: Gregory M Eirich
  CULPA: 1 Info
• 4 points

SOCIAL NETWORK ANALYSIS

The course is designed to teach students the foundations of network analysis including how to manipulate, analyze and visualize network data themselves using statistical software. We will focus on using the statistical program R for most of the work. Topics will include measures of network size, density, and tie strength, measures of network diversity, sampling issues, making ego-nets from whole networks, distance, dyads, homophily, balance and transitivity, structural holes, brokerage, measures of centrality (degree, betweenness, closeness, eigenvector, beta/Bonacich), statistical inference using network data, community detection, affiliation/bipartite networks, clustering and small worlds; positions, roles and equivalence; visualization, simulation, and network evolution over time.

• Department: Quantitative Methods/Social Sciences
• F 5:10 p.m.-7 p.m.
• Instructor: Gregory M Eirich
  CULPA: 1 Info
• 4 points

DATA VISUALIZATION

This course is designed to the interdisciplinary and emerging field of data science. It will cover techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science to enhance the understanding of complex data. Students will be required to complete several scripting, data analysis and visualization design assignments as well as a final project. Topics include: data and image models, social and interactive visualizations, principles and designs, perception and attention, mapping and cartography, network visualization. Computational methods are emphasized and students will be expected to program in R, Javascript, D3, HTML and CSS and will be expected to submit and peer review work through Github. Students will be expected to write up the results of the project in the form of a conference paper submission.

• Department: Quantitative Methods/Social Sciences
• M 8:10 p.m.-10 p.m.
• Instructor: Thomas Brambor
  h-index: 9 Info
• 4 points

PROSEMINAR II

• Department: Sociology
• W 10:10 a.m.-noon
• 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
• T 8:10 p.m.-10 p.m.
• Instructor: Benjamin K Goodrich
  Info
• 4 points

FIELD WORK II

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

NATURAL LANG PROCESSING SOCIAL SCIENCES

NATURAL LANG PROC SOC SCI

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
• T 6:10 p.m.-8 p.m.
• Instructor: Patrick Houlihan
  Info
• 4 points

NATURAL LANG PROCESSING SOCIAL SCIENCES

NATURAL LANG PROC SOC SCI

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
• T 6:10 p.m.-8 p.m.
• Instructor: Patrick Houlihan
  Info
• 4 points

THESIS SEMINAR II

• Department: Sociology
• T 10:10 a.m.-noon
• Instructor: Denise Milstein
  CULPA: 9 h-index: 5 Info
• 3 points

APPLIED DATA SCI FOR SOC SCIENTISTS

APPLIED DATA SCI-SOC SCIENTIST

• Department: Quantitative Methods/Social Sciences
• W 6:10 p.m.-8 p.m.
• Instructor: Marco A Morales Barba
  Info
• 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
• M 10:10 a.m.-noon
• Instructor: Michael Parrott
  Info
• 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
• M 10:10 a.m.-noon
• Instructor: Michael Parrott
  Info
• 4 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
• R 10:10 a.m.-noon
• Instructor: Michael Parrott
  Info
• 4 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
• R 10:10 a.m.-noon
• Instructor: Michael Parrott
  Info
• 4 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
• M 6:10 p.m.-8 p.m.
• Instructor: Michael Parrott
  Info
• 4 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
• M 6:10 p.m.-8 p.m.
• Instructor: Michael Parrott
  Info
• 4 points

INTRO SOCIAL DATA ANLYS II LAB

• Department: Sociology
• M noon-1 p.m.
• Instructor: Jongsun J Chae
  Info
• 0 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
• R 8:10 a.m.-10 a.m.
• Instructor: Jesse L Scinto
  Info
• Instructor: Cheryl Fenelle-Dixon
  Info
• 3 points

THE AUTHENTIC LEADER

THE AUTHENTIC LEADER - CEU

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
• R 8:10 a.m.-10 a.m.
• Instructor: Cheryl Fenelle-Dixon
  Info
• 3 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
• R 10:10 a.m.-noon
• Instructor: Cheryl Fenelle-Dixon
  Info
• 3 points

THE DOCUMENTARY TRADITION

• Department: Film
• T 2:10 p.m.-4:55 p.m.
• Instructor: Nico Baumbach
  Info
• 3 points

MONITORING AND ANALYSIS OF MARINE & ESTU

MONIT & ANLYS OF MARINE &

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 Technology
• W 6:10 p.m.-8 p.m.
• Instructor: Braddock Linsley
  h-index: 42 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
• M 10:10 a.m.-noon
• Instructor: Jamyn Edis
  Info
• Instructor: Ethan Pew
  h-index: 0 Info
• 3 points

DIGITAL MEDIA & ANALYTICS

DIGITAL MEDIA & ANALYTICS- CEU

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
• M 10:10 a.m.-noon
• Instructor: Ethan Pew
  h-index: 0 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
• M 2:10 p.m.-4 p.m.
• Instructor: Ethan Pew
  h-index: 0 Info
• 3 points

APPLIED ANALYTICS IN ORGANIZATIONAL CONT

APP AN IN ORG CONTEXT

• Department: Applied Analytics
• T 6:10 p.m.-8 p.m.
• W 6:10 p.m.-8 p.m.
• R 8:10 p.m.-10 p.m.
• Instructor: Hany S Saleeb
  Info
• Instructor: Cory E Labanow
  Info
• Instructor: Arunava Bhattacharyya
  Info
• 3 points

ESTIMATING PROJECT COSTS

• Department: Construction Administration
• T 6:10 p.m.-8 p.m.
• Instructor: Jason I Levine
  Info
• 3 points

VALUE-BASED ENTERPRISE RISK MGMT

VALUE-BASED ERM (ONLINE)

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

VALUE-BASED ENTERPRISE RISK MGMT

VALUE-BASED ERM

• Department: Enterprise Risk Management
• R 6:10 p.m.-8 p.m.
• T 6:10 p.m.-8 p.m.
• R 8:10 p.m.-10 p.m.
• Instructor: Sim Segal
  Info
• Instructor: Rich Lauria
  Info
• 3 points

INTRODUCTION TO HUMAN CAPITAL MANAGEMENT

• Department: Human Capital Management
• W 8:30 p.m.-10 p.m.
• M 6:10 p.m.-8 p.m.
• Instructor: Edna B Chun
  Info
• Instructor: Angela Finlay
  Info
• 3 points

CAMPUS PRACTICUM

CAMPUS PRACTICUM II

• Department: Information & Knowledge Strat
• RFS 8:30 a.m.-6:30 p.m.
• Instructor: Nancy Dixon
  Info
• 1 points

SELF & OTHER IN CLIN ENCOUNTER

• Department: Narrative Medicine
• M 6:10 p.m.-9 p.m.
• Instructor: Craig Irvine
  Info
• 4 points

PAINTING I

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

FICTION WORKSHOP

• Department: Writing
• R 10 a.m.-1 p.m.
• R 2 p.m.-5 p.m.
• M 2 p.m.-5 p.m.
• W 2 p.m.-5 p.m.
• T 10 a.m.-1 p.m.
• Instructor: Diksha Basu
  Wikipedia Info
• Instructor: James Canon
  Wikipedia Info
• Instructor: Joshua Furst
  Wikipedia Info
• Instructor: Elissa Schappell
  Wikipedia Info
• Instructor: Lynn S Strong
  Info
• 6 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
• Instructor: Yashodhan Kanoria
  h-index: 18 Info
• 1.5 points

Business Analytics

• Department: Decision, Risk and Operations
• Instructor: Yashodhan Kanoria
  h-index: 18 Info
• 1.5 points

Business Analytics

• Department: Decision, Risk and Operations
• Instructor: Yashodhan Kanoria
  h-index: 18 Info
• 1.5 points

Business Analytics

• Department: Decision, Risk and Operations
• Instructor: Yashodhan Kanoria
  h-index: 18 Info
• 1.5 points

UNDERSTAND CONFLICT & COOPERATION

UNDERSTAND CONF & COOPERATION

• Department: Negotiation & Conflict Resolution
• W 5:10 p.m.-6:50 p.m.
• Instructor: Peter T Coleman
  h-index: 35 Info
• 3 points

PSYCHIATRIC MEDICINE

• Department: Psychiatry
• 0 points

SAFETY IN THE CONSTRUCTION INDUSTRY

SAFETY IN THE CONSTR INDU

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

Operations Management & Strate

• Department: Decision, Risk and Operations
• Instructor: Marcelo Olivares
  Info
• 1.5 points

Operations Management & Strate

• Department: Decision, Risk and Operations
• Instructor: Marcelo Olivares
  Info
• Instructor: Cyrus Mohebbi
  Info
• 1.5 points

Operations Management & Strate

• Department: Decision, Risk and Operations
• Instructor: Cyrus Mohebbi
  Info
• 1.5 points

LEGAL ISSUES IN BUILDING CONSTRUCTION

• Department: Construction Administration
• W 6:10 p.m.-8 p.m.
• Instructor: Dana E Gold
  Info
• 3 points

ELEMENTAL TOPICS IN CONSTRUCTION MANAGEM

ELEMENTAL TOPICS IN CNSTR

This course is designed to provide students with introductory knowledge and basic skills they will need to understand and apply as they progress through the program. Students receive an overview of key topics that will be covered in greater detail through core courses and electives during subsequent terms. Each class session provides a primer on a specific area of vital importance, including construction techniques, legal issues, contracts, blueprint reading, scheduling, sustainability, claims and more. Upon completion students will be familiar with basic concepts, terminology and procedures associated with the industry, and well prepared to study these subjects in greater depth.

• Department: Construction Administration
• T 6:10 p.m.-8 p.m.
• 1.5 points

INTRODUCTION TO NEGOTIATION

• Department: Negotiation & Conflict Resolution
• R 6:10 p.m.-8 p.m.
• M 6:10 p.m.-8 p.m.
• Instructor: Bodi Regan
  Info
• Instructor: Stephanie S Chow
  Info
• 3 points

NEGOTIATION & DISPUTE RESOLUTION IN SPRT

NEG & DISPUTE RES IN SPRT

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

FOUNDATIONS OF CLINICAL MEDICINE II

• Department: Medicine
• 0 points

INTRODUCTION TO MEDIATION

• Department: Negotiation & Conflict Resolution
• US 9 a.m.-5 p.m.
• Instructor: Michele Riley
  Info
• 3 points

THE BODY IN HEALTH & DISEASE I

• Department: Medicine
• Instructor: Camilla Smith
  Info
• 0 points

PRACTICUM: FACILITATING NARRATIVE MEDICI

PRACTICUM: FACILITATING W

TBA

• Department: Narrative Medicine
• R 6:10 p.m.-8 p.m.
• 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
• 1.5 points

SCREENWRITING II

• Department: Film
• W 9:30 a.m.-12:30 p.m.
• Instructor: Laurence Vannicelli
  Info
• Instructor: David B Schwab
  Info
• Instructor: Jamal Joseph
  Info
• Instructor: Richard Dresser
  Info
• Instructor: Hannah H Weyer
  Info
• Instructor: Rachel Israel
  Wikipedia Info
• 3 points

OPERATIONS MANAGEMENT IN IT

OPERATIONS MGMT IN IT

This course provides an examination of the role the technology leader plays in the daily operations and performance management of an organization. The course focuses on how tech leaders can manage both up and down within their organizations through critical examination of current IT topics such as Outsourcing, Cloud Computing, Enterprise Architecture (as a strategy), Information Security, Risk Management, IT Governance, and determining/communicating the business value of IT. Students leave the course with a deep understanding of the dramatically different priorities, skills, and actions required to succeed as an IT leader.

• Department: Technology Management
• W 6:10 p.m.-8 p.m.
• Instructor: Craig Cuyar
  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
• R 10 a.m.-4 p.m.
• Instructor: Susanna Coffey
  CULPA: 1 Info
• 3 points