DROM B8000OPTIMIZATION ＆ SIMULATION BOOTCAMP0.00
- Subject
- DROM B
- Credits (min)
- 0
- Credits (max)
- 0
- Credit unit
- points
- Type
- course
- Edition
- 2026
- Source
- bulletin.columbia.edu
Teachers College at Columbia University · Courses
DROM B
18 courses with the subject DROM B, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
DROM B8101Business Analytics I (Full Term)
- Subject
- DROM B
- Type
- course
- Edition
- 2026-2027
- Source
- bulletin.columbia.edu
DROM B8102Process Improvement and Growth
- Subject
- DROM B
- Type
- course
- Edition
- 2026-2027
- Source
- bulletin.columbia.edu
DROM B8103Business Analytics II Half Term
DROM B8105
- Subject
- DROM B
- Type
- course
- Edition
- 2026-2027
- Source
- bulletin.columbia.edu
DROM B8105HEALTHCARE ANALYTICS1.50
Course Description: This course will introduce students to the core concepts of health analytics, the types of questions addressed by healthcare analytics departments, and a broad overview of the most common analysis techniques
- Subject
- DROM B
- Credits (min)
- 1.5
- Credits (max)
- 1.5
- Credit unit
- point
- Type
- course
- Edition
- 2026
- Source
- bulletin.columbia.edu
DROM B8106Operations Strategy3.00
Prerequisites: DROM B5101 OR DROM B6101 Operations Strategy
- Subject
- DROM B
- Credits (min)
- 3
- Credits (max)
- 3
- Credit unit
- points
- Type
- course
- Edition
- 2026
- Source
- bulletin.columbia.edu
DROM B8107Service Operations Management3.00
Prerequisites: DROM B6102 OR DROM B5102
- Subject
- DROM B
- Credits (min)
- 3
- Credits (max)
- 3
- Credit unit
- points
- Type
- course
- Edition
- 2026
- Source
- bulletin.columbia.edu
DROM B8108Supply chain management
or DROM B8109 DROM B8110 DROM B8112 DROM B8114 DROM B8115 DROM B8116 DROM B8117 DROM B8123 DROM B8128 DROM B8131 DROM B8132 DROM B8143 DROM B8145 DROM B8146 DROM B8148 DROM B8153 DROM B8159 DROM B8510 DROM B8816 DROM B8823 DROM B9122
- Subject
- DROM B
- Type
- course
- Edition
- 2026-2027
- Source
- bulletin.columbia.edu
DROM B8110Game-Theoretic Business Strategy
DROM B8112 DROM B8114 DROM B8115
- Subject
- DROM B
- Type
- course
- Edition
- 2026-2027
- Source
- bulletin.columbia.edu
DROM B8116Risk Management3.00
Spring 2026: DROM B8116 Course Number Section/Call Number Times/Location Instructor Points Enrollment DROM 8116 060/14701 M W 6:00pm - 7:30pm 640 Kravis Hall Evan Picoult 3.00 49/74
- Subject
- DROM B
- Credits (min)
- 3
- Credits (max)
- 3
- Credit unit
- points
- Type
- course
- Edition
- 2026
- Source
- bulletin.columbia.edu
DROM B8117Business Analytics III: Modern AI, Deep
Learning, and Generative AI
- Subject
- DROM B
- Type
- course
- Edition
- 2026-2027
- Source
- bulletin.columbia.edu
DROM B8123Demand Analytics3.00
Lect: 3. Tools to efficiently manage supply and demand networks. Topics include service and inventory trade-offs, stock allocation, pricing, markdown management and contracts, timely product distribution to market while avoiding excess inventory, allocating adequate resources to the most profitable products and selling the right product to the right customer at the right price and at the right time.
- Subject
- DROM B
- Credits (min)
- 3
- Credits (max)
- 3
- Credit unit
- points
- Type
- course
- Edition
- 2026
- Source
- bulletin.columbia.edu
DROM B8131Sports Analytics3.00
Sports analytics refers to the use of data and quantitative methods to measure performance and make decisions to gain advantage in the competitive sports arena. This course builds on the Business Analytics core course and is designed to help students to develop and apply analytical skills that are useful in business, using sports as the application area. These skills include critical thinking, mathematical modeling, statistical analysis, predictive analytics, game theory, optimization and simulation. These skills will be applied to sports in this course, but are equally useful in many areas of business.There will be three main topics in the course: (1) measuring and predicting player and team performance, (2) decision-making and strategy in sports, and (3) fantasy sports and sports betting. Typical questions addressed in sports analytics include: How to rank players or teams? How to predict future performance of players or teams? How much is a player on a team worth? How likely are extreme performances, i.e., streaks? Are there hot-hands in sports performances? Which decision is more likely to lead to a win (e.g., attempt a stolen base or not in baseball, punt or go for it on fourth down in football, dump and chase or not in hockey, pull the goalie or not in hockey)? How to form lineups in daily fantasy sports? How to manage money in sports betting? How to analyze various ``prop'' bets?The main sports discussed in the course will be baseball, football, basketball, hockey, and golf. Soccer, tennis, and other sports will be briefly discussed. Students are welcome to pursue any sport in more detail (e.g., cricket, rugby, auto racing, horse racing, Australian rules football, skiiing, track and field, or even card games such as blackjack, poker, etc.) in a project. Class sessions will involve a mixture of current events, lecture, discussion, and hands-on analysis with computers in class. Each session will typically address a question from a sport using an important analytical idea (e.g., mean reversion) together with a mathematical technique (e.g., regression). Because of the "laboratory" nature of part of the sessions, students should bring their laptops to each class
- Subject
- DROM B
- Credits (min)
- 3
- Credits (max)
- 3
- Credit unit
- points
- Type
- course
- Edition
- 2026
- Source
- bulletin.columbia.edu
DROM B8148The Analytics Advantage
- Subject
- DROM B
- Type
- course
- Edition
- 2026-2027
- Source
- bulletin.columbia.edu
DROM B8153People Analytics: Creating Business Value from Data1.50
point.
- Subject
- DROM B
- Credits (min)
- 1.5
- Credits (max)
- 1.5
- Credit unit
- point
- Type
- course
- Edition
- 2026
- Source
- bulletin.columbia.edu
DROM B8159Digital Marketplaces
- Subject
- DROM B
- Type
- course
- Edition
- 2026-2027
- Source
- bulletin.columbia.edu
DROM B8510MANAGERIAL NEGOTIATIONS3.00
- Subject
- DROM B
- Credits (min)
- 3
- Credits (max)
- 3
- Credit unit
- points
- Type
- course
- Edition
- 2026
- Source
- bulletin.columbia.edu
DROM B8816Quantitative Pricing ＆ Revenue1.50
Prerequisites: Familiar with the content covered in the managerial statistics, business analytics, managerial economics and marketing core courses. Knowledge in managerial statistics, probability, probability distributions, expected value calculations, etc Quantitative pricing and revenue analytics collectively refers to the set of practices and tools that firms in various industries use to quantitatively model consumer preferences, segment their market, and tactically optimize (often in micro targeted or personalized manner) their product assortment, pricing, and promotion strategies. The origins of this field, often referred to as revenue management as it is also called, are in the airline industry during the late 80s. The prototypical question is how a firm should set and update pricing and product availability decisions across its various selling channels in order to maximize its profitability. In the airline industry, as most of us know, tickets for the same flight may be sold at many different fares, the availability of which is changing as a function of purchase restrictions, the forecasted future demand, and the number of unsold seats. The adoption of such systems has transformed the transportation and hospitality industries, and is increasingly important in retail, telecommunications, entertainment, financial services, health care, manufacturing, as well as on-line advertising, online retailing, and online markets. In parallel, pricing and revenue optimization has become a rapidly expanding practice in consulting services, and a growing area of software and IT development. We will be doing a hands-on dive into the above tools in the context of 2-3 case studies and datasets, in conjunction with lectures to set the stage. The case studies will cover markdown pricing for a retailer, demand and inventory data for a self-storage company, customer research data of a mortgage lender, and peak load pricing data for a highway toll booth.Through this course, students will be able to model and identify opportunities for revenue optimization in different business contexts. As the ensuing outline reveals, most of the topics covered in the course are either directly or indirectly related to customer segmentation, demand modeling, and tactical price optimization.TextbookOne recommended book for the course is by Robert Phillips titled "Pricing and Revenue Optimization. This will primarily be done in teams, much of it in class, and with the help of the TA(s) and the professor. Sample code will be shared for various parts of these analyses. Course deliverables align. Apart from class participation (30％ of the total grade), the other course deliverables consist of a set of in-class (homework) assignments (40％) and a take-home final exam (30％).Class participation: I will routinely ask that you review some material, or watch some short recor
- Subject
- DROM B
- Credits (min)
- 1.5
- Credits (max)
- 1.5
- Credit unit
- point
- Type
- course
- Edition
- 2026
- Source
- bulletin.columbia.edu
Source: Teachers College at Columbia University's catalog, linked per course · table learning_unit · CourseShelf publish 61