# mec

# ‘math+econ+code’ masterclass series

**Data science meets economics**

Alfred Galichon (NYU Econ+Math and Sciences Po Econ)

### Description

*An immersive learning experience*

The ‘math+econ+code’ masterclasses are very intensive classes in which the students are immersed over five consecutive days on a topic at the interaction between mathematics, economics and computation. These classes are innovative in several respects: the condensed format, the mix of theoretical, computational and empirical components, and the emphasis on coding. The classes focus on the acquisition of an operational knowledge: throughout the week, students will learn the mathematical structures, the economic models, and how to code them in practice. The course relies on scientific context-based learning: mathematical concepts and computational methods are introduced on a needs basis while studying various economic models, and thus there are no prerequisite other than the equivalent of a first-year graduate sequence in econ, applied mathematics or other quantitative disciplines.

*A very active area of research*

The intersection between economics, mathematics and computation is coming back as a major area of current research. There are at least two reasons for this. The first one is the emergence of online platforms, which act as central planners and need to solve complex computational problems such as matching service providers with customers, introducing potential dating partners, performing dynamic pricing tasks, etc. The second reason is that econometric methods have been cross-fertilized by novel techniques from machine learning, that heavily rely on computational tools.

*“Closer to cooking lessons than to traditional lectures”*

The teaching format of a ‘math+econ+code’ series is somewhat unusual: a class is typically taught over six consecutive days, with a lesson in the morning (alternating between theory blocks and coding blocks); a guest lecture in the early afternoon; followed by individual work on computational assignments in the last part of the afternoon. These classes are very demanding from students, but the learning rewards are also very high. Students are expected to write their own code, and the teaching staff will ensure that it is operational at the end of the day. In complete opposition to the trend of ‘massively open online courses’ (MOOCS), these classes place instead a particular emphasis on personal interactions between teaching staff and students. They are therefore closer to cooking lessons than to traditional lectures. Without equivalent elsewhere, these series are experiencing growing popularity and draw graduate students across various quantitative disciplines and universities.

### Classes offered

**‘math+econ+code’ masterclass on optimal transport and economic applications**. Next edition: online, Jan 18-22, 2021. Past editions: NYU New York, Jan 20-24, 2020. NYU Paris, June 17-21, 2019, NYU NY, Jan 14-18, 2019, NYU NY, Jan 15-20, 2018.

**‘math+econ+code’ masterclass on equilibrium transport and matching models in economics**. Next edition: June 21-25, 2021. Past editions: Paris+online, June 8-12, 2020, NYU NY, May 21-26, 2018.

**‘math+econ+code’ masterclass on submodular optimization in economics.** TBA.

**‘math+econ+code’ masterclass on networks economics.** TBA.

### Diffusion list

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