Computational Economics and Finance
Four and a Half MA classes
Design
Sargent designed these classes in close collaboration with Chase Coleman and Spencer Lyon, lead developers at QuantEcon, an open source website devoted to teaching how to put Python and Julia at the service of the social sciences. Coleman, Lyon, and Sargent team teach all four courses in the sequence, as well as the online summer “pre-class”.
Purposes
Advancements in computational power over the last 30 years have led to significant steps forward in how we approach social science, giving rise to a new class of social scientists: computational social scientists. Computational social scientists work throughout the public and private sector: in universities, private research institutions, and business enterprises such as tech companies like Amazon, Facebook, Google, and Uber.
Our suite of classes aim to prepare students for either a graduate program in the social sciences or for a career as a data analyst or computational social scientist.
The integrated sequence consists of four one-semester long classes together with an online preparatory class to be completed during the summer preceding the certificate program sequence. Students with qualifying experience or coursework can opt out of the “pre-class”.
Prerequisites and location
No prior knowledge of computer programming or machine learning is assumed. The Online Foundations Course (see below) will provide the basic computer programming skills required to succeed in subsequent courses in the sequence. The program does assume familiarity with elements of multivariate calculus, linear algebra, statistics, and probability.
The sequence is partly even mainly online. Ample student-teacher interactions will be facilitated online.
Course Descriptions
Before program (online)
ECON GA 4001,
0 credits
Online Foundations Course: The foundational course of the
sequence is an online, self-paced course typically completed over
12 weeks. This course covers skills that will be used for the
remainder of the year and we assume students are familiar with
this material in the fall. The class introduces students to the
Python programming language and elementary software engineering
tools that will be applied in subsequent courses in the sequence.(Syllabus)
Semester 1 (Fall)
ECON GA 4002,
3 credits
Mathematical Foundations for Computational Social Science:
This course reviews essential mathematical tools — such as
calculus and linear algebra — and introduces students to
foundational concepts of random variables, model building, and
model estimation (both frequentist and Bayesian). The goal of this
course is two-fold: (1) to empower students to build models to
discuss the world around them, and, (2) to foster mathematical
maturity so that students can teach themselves mathematically
useful material as needed. (Syllabus)
ECON GA 4003,
3 credits
3 credits
Data Tools for Computational Social Science: This course
arms students with cutting-edge data manipulation and management
tools. The class relies heavily on the Python pandas package and
emphasizes that “real-world” data are messy. The course provides
students with tools for analysis in such environments. (Syllabus)
Semester 2 (Spring)
ECON GA 4004,
3 credits
Dynamic Models for Computational Social Science: This
course covers dynamic models and their uses for guiding decisions.
Topics include dynamic programming, time-series analysis (both
Bayesian and frequentist), Markov models, and Hidden Markov
Models. Many of these topics are the focus of cutting-edge
research. Examples include text analysis. This course empowers
students to apply these tools in academic, government, and
industry research. (Syllabus)
ECON GA 4005,
3 credits
Machine Learning for Computational Social Science: When
applied correctly, “machine learning” tools allow individuals to
approximate complicated outcomes in the real world. However, when
applied carelessly, these tools generate misleading findings. This
course covers supervised learning (both regression and
classification) , reinforcement learning, and model selection via
validation procedures. The course prepares students to apply
classical and cutting-edge machine learning techniques to problems
in the social sciences. The course presents a principled approach
that adheres to best practices and encourages understanding and
transparency. (Syllabus)
Course Sequence
| Semester | Course 1 | Course 2 |
|---|---|---|
| Summer | Online Foundations Course | |
| Fall | Mathematical Foundations for Computational Social Science | Data Tools for Computational Social Science |
| Spring | Dynamic Models for Computational Social Science | Machine Learning for Computational Social Science |