Fall Quarter  

Perspectives on Computational Analysis

(MACS 30000). Benjamin Soltoff, M/W 11:30-1:20 p.m. & Weekly Lab Wednesdays 4:30-5:20 p.m.

Massive digital traces of human behavior and ubiquitous computation have both extended and altered classical social science inquiry. This course surveys successful social science applications of computational approaches to the representation of complex data, information visualization, and model construction and estimation. We will reexamine the scientific method in the social sciences in context of both theory development and testing, exploring how computation and digital data enables new answers to classic investigations, the posing of novel questions, and new ethical challenges and opportunities. Students will review fundamental research designs such as observational studies and experiments, statistical summaries, visualization of data, and how computational opportunities can enhance them. The focus of the course is on exploring the wide range of contemporary approaches to computational social science, with practical programming assignments to train with these approaches.

Computing for the Social Sciences

(MACS 30500). Benjamin Soltoff, M/W 1:30-2:50 p.m. & Weekly Lab Wednesdays 3-4:20 p.m.

This is an applied course for social scientists with little-to-no programming experience who wish to harness growing digital and computational resources. The focus of the course is on generating reproducible research through the use of programming languages and version control software. Major emphasis is placed on a pragmatic understanding of core principles of programming and packaged implementations of methods. Students will leave the course with basic computational skills implemented through many computational methods and approaches to social science; while students will not become expert programmers, they will gain the knowledge of how to adapt and expand these skills as they are presented with new questions, methods, and data.

Economic Policy Analysis with Overlapping Generation Models

(MACS 40000). Rick Evans, T/Th 10:30-11:50 a.m.

This course will study economic policy questions ideally addressed by the overlapping generations (OG) dynamic general equilibrium framework. OG models represent a rich class of macroeconomic general equilibrium model that is extremely useful for answering questions in which inequality, demographics, and individual heterogeneity are important. OG models are used extensively by the Joint Committee on Taxation, Congressional Budget Office, and Department of the Treasury. This course will train students how to set up and solve OG models. The standard nonlinear global solution method for these models--time path iteration--is a fixed point method that is similar to but significantly different from value function iteration. This course will take students through progressively richer versions of the model, which will include endogenous labor supply, nontrivial demographics, bequests, stochastic income, multiple industries, non-balanced government budget constraint, and household tax structure.

Introduction to Spatial Data Science

(MACS 54000). Luc Anselin, M/W 1:30-2:50 p.m.

Spatial data science is an evolving field that can be thought of as a collection of concepts and methods drawn from both statistics and computer science. These techniques deal with accessing, transforming, manipulating, visualizing, exploring and reasoning about data where the locational component is important (spatial data). The course introduces the types of spatial data relevant in social science inquiry and reviews a range of methods to explore these data. The types of data considered include observations at the point level (e.g., locations of crimes, commercial establishments, traffic accidents), data gathered for aggregate units, such as census tracts or counties (e.g., unemployment rates, disease rates by area, crime rates), and data measured at spatially located sampling points (such as air quality monitoring stations and urban sensors). Specific topics covered include the implementation of formal spatial data structures, geovisualization and visual analytics, spatial autocorrelation analysis, variogram analysis, cluster detection, regionalization, point pattern analysis and spatial data mining. An important aspect of the course is to learn and apply open source geospatial software tools, such as R and GeoDa.

Computational Social Science Workshop

(MACS 50000). James Evans, Thursdays 11-12:20 p.m. Saieh 247. PQ: Computation students must register for a R. Other faculty and graduate students welcome.

High performance and cloud computing, massive digital traces of human behavior from ubiquitous sensors, and a growing suite of efficient model estimation, machine learning and simulation tools are not just extending classical social science inquiry, but transforming it to pose novel questions at larger and smaller scales. The Computational Social Science (CSS) Workshop is a weekly event that features this work, highlights associated skills and data, and explores the use of CSS in the world. The CSS Workshop alternates weekly between research workshops and professional workshops. The research workshops feature new CSS work from top faculty and advanced graduate students from UChicago and around the world, while professional workshops highlight useful skills and data (e.g., machine learning with Python’s scikit-learn; the Twitter firehose API) and showcase practitioners using CSS in the government, industry and nonprofit sectors. Each quarter, the CSS Workshop also hosts a distinguished lecture, debate and dinner, and a student conference.

Computer Science with Applications - 1

(CAPP 30121). Borja Sotomayor, M/W/F 9:30-10:20 a.m. & Weekly Lab Mondays 6-7:20 p.m.

This three-quarter sequence teaches computational thinking and skills to students who are majoring in the sciences, mathematics, and economics. Lectures cover topics in (1) programming, such as recursion, abstract data types, and processing data; (2) computer science, such as clustering methods, event-driven simulation, and theory of computation; and to a lesser extent (3) numerical computation, such as approximating functions and their derivatives and integrals, solving systems of linear equations, and simple Monte Carlo techniques. Applications from a wide variety of fields serve both as examples in lectures and as the basis for programming assignments. In recent offerings, students have written programs to evaluate betting strategies, determine the number of machines needed at a polling place, and predict the size of extinct marsupials. Students learn Java, Python, R and C++.

Data Visualization for Policy Analysis

(CAPP 30239). Alex Engler, M/W 3:00-4:20 p.m. PQ: CAPP 30122.

This course introduces students to the tools, skills, and concepts necessary for making compelling data visualizations for political analysis and public policy research. Students will learn about the theory of graphical design, including the grammar of graphics, and the science of visual perception such as to inform making visualizations that are intuitive, effective, and memorable. // Data Visualization for Policy Analysis will cover exploratory data analysis and static graphics in R, aesthetic design using Adobe Illustrator, and interactive web graphics using the powerful JavaScript library D3. Throughout the course, each student will create an aesthetically compelling and analytically sound visualization portfolio on a public policy topic of their own choosing.

Linear Algebra

(MATH 19620). Instructor TBD, T/Th 8-9:20 a.m.

This course takes a concrete approach to the basic topics of linear algebra. Topics include vector geometry, systems of linear equations, vector spaces, matrices and determinants, and eigenvalue problems.

Statistical Theory and Methods - 1

(STAT 24400). Instructor/Days/Times TBD. PQ: Multivariate calculus. Some previous experience with statistics and/or probability helpful but not required.

This course is the first quarter of a two-quarter systematic introduction to the principles and techniques of statistics, as well as to practical considerations in the analysis of data, with emphasis on the analysis of experimental data. This course covers tools from probability and the elements of statistical theory. Topics include the definitions of probability and random variables, binomial and other discrete probability distributions, normal and other continuous probability distributions, joint probability distributions and the transformation of random variables, principles of inference (including Bayesian inference), maximum likelihood estimation, hypothesis testing and confidence intervals, likelihood ratio tests, multinomial distributions, and chi-square tests. Examples are drawn from the social, physical, and biological sciences. The coverage of topics in probability is limited and brief, so students who have taken a course in probability find reinforcement rather than redundancy. Students who have already taken STAT 25100 may choose to take STAT 24410 (if offered) instead of STAT 24400.

Analysis in Rn I

(MATH 20300). Instructor TBD, M/W/F 12:30-1:20 p.m. PQ: MATH 16300 or MATH 15910 or MATH 15900 or MATH 19900.

For students concentrating in Computational Economics who need exposure to real analysis. Students must be proficient in linear algebra. This course covers the construction of the real numbers, the topology of R^n including the Bolzano-Weierstrass and Heine-Borel theorems, and a detailed treatment of abstract metric spaces, including convergence and completeness, compact sets, continuous mappings, and more.


(MPCS 55001). Geraldine Brady, Days/Times TBD  PQ: immersion math (MPCS 50103) or placement. Immersion programming (MPCS 50101) or programming waiver, or core programming (MPCS 51036 or 51040), or instructor consent. Placement exam given on September 14th and 15th. MPCS students will be given priority. Complete Course Request Form

The course is an introduction to the design and analysis of efficient algorithms, with emphasis on developing techniques for the design and rigorous analysis of algorithms rather than on implementation. Algorithmic problems include sorting and searching, discrete optimization, and algorithmic graph theory.  Design techniques include divide-and-conquer methods, dynamic programming, greedy methods, graph search, as well as the design of efficient data structures. Methods of algorithm analysis include asymptotic notation, evaluation of recurrences, and the concepts of polynomial-time algorithms.  NP-completeness is introduced toward the end the course. Students who complete the course will have demonstrated the ability to use divide-and-conquer methods, dynamic programming methods, and greedy methods, when an algorithmic design problem calls for such a method. They will have learned the design strategies employed by the major sorting algorithms and the major graph algorithms, and will have demonstrated the ability to use these design strategies or modify such algorithms to solve algorithm problems when appropriate.  They will have derived and solved recurrences describing the performance of divide-and-conquer algorithms, have analyzed the time and space complexity of dynamic programming algorithms, and have analyzed the efficiency of the major graph algorithms, using asymptotic analysis.

Theoretical Neuroscience: Single Neuron Dynamics and Computation

(CPNS 35510/STAT 42510). Nicolas Brunel, Days/Times TBD.

This course is the first part of a three-quarter sequence in theoretical/computational neuroscience. It will focus on mathematical models of single neurons. Topics will include: basic biophysical properties of neurons; Hodgkin-Huxley model for action potential generation; 2D models, phase-plane analysis and bifurcations leading to action potential generation; integrate-and-fire-type models; noise; characterization of neuronal activity with stochastic inputs; spatially extended models; models of synaptic currents and synaptic plasticity; unsupervised learning; supervised learning; reinforcement learning.


(MPCS 53001). Zachary Freeman, Days/Times TBD. PQ: Core programming (completed or currently enrolled). Placement exam given on September 14th and 15th. MPCS students will be given priority. Complete Course Request Form.

In this course students will learn database design and development and will build a simple but complete web application powered by a relational database. We start by gathering requirements and showing how to model a relational database using an Entity-Relationship Diagram (ERD). Concepts covered include entity sets and relationships, using keys as a unique identifier for each object in an entity set, one-one, many-one, and many-many relationships as well as translational rules from conceptual modeling (ERD) to relational table definitions. We will examine the relational model and functional dependencies along with their application to the methods for improving database design: normal forms and normalization. After designing and modeling their database, students will learn the universal language of relational databases: SQL (Structured Query Language). We will first introduce relational algebra, the theoretical foundation of SQL and then examine in detail the two main aspects of SQL: data definition language (DDL) and data manipulation language (DML). Concepts covered include subqueries, aggregation, various types of joins, functions, triggers and stored procedures. Students will then learn about web connectivity, as they build a simple front-end for their application in order to interact with their database online. Finally, we will provide an overview of related topics such as data warehousing, big data, NoSQL and NewSQL databases.

Mathematical Methods for Biological Sciences - 1

(PSYC 36210). Dmitry Kondrashov, T/Th 12:30-1:50 p.m. & Weekly Lab Fridays 3-4:50 p.m.

This course builds on the introduction to modeling course biology students take in the first year (BIOS 20151 or 152). It begins with a review of one-variable ordinary differential equations as models for biological processes changing with time, and proceeds to develop basic dynamical systems theory. Analytic skills include stability analysis, phase portraits, limit cycles, and bifurcations. Linear algebra concepts are introduced and developed, and Fourier methods are applied to data analysis. The methods are applied to diverse areas of biology, such as ecology, neuroscience, regulatory networks, and molecular structure. The students learn computation methods to implement the models in MATLAB.

Nonparametric Inference

(STAT 37400). John Lafferty, T/Th 10:30-11:50 a.m.

Nonparametric inference is about developing statistical methods and models that make weak assumptions. A typical nonparametric approach estimates a nonlinear function from an infinite dimensional space, rather than a linear model from a finite dimensional space. This course gives an introduction to nonparametric inference, with a focus on density estimation, regression, confidence sets, orthogonal functions, random processes, and kernels. The course treats nonparametric methodology and its use, together with theory that explains the statistical properties of the methods.

Sociology of Education

(CHDV 40128 / SOCI 40225). Anna Mueller, Days/Times TBD.

Education plays a fundamental role in society, both because it determines individuals’ life chances and because it has the power to reproduce or ameliorate inequality in society. In this course, we will discuss theoretical and empirical research that examines how schools both perpetuate socioeconomic inequality and provide opportunities for social mobility. We will pay particular attention to the role of schools in the intergenerational transmission of social status, especially based on race, class, gender, and immigrant status and with an emphasis on the U.S. We will also discuss the social side of schools, delving into (1) the role of adolescent culture(s) in youths’ educational experiences and human development and (2) social psychological aspects of schooling. Schools are the primary extra-familial socializing institution that youth experience; thus, understanding how schools work is central to understanding the very structure of societies as well as the transition from childhood to adulthood. 

Game Theory I

(PLSC 29102). John Patty, T/Th 9:30-10:50 a.m.

This is a course for graduate students in Political Science. It introduces students to games of complete information through solving problem sets. We will cover the concepts of equilibrium in dominant strategies, weak dominance, iterated elimination of weakly dominated strategies, Nash equilibrium, subgame perfection, backward induction, and imperfect information. The course will be centered around several applications of game theory to politics: electoral competition, agenda control, lobbying, voting in legislatures and coalition games. This class serves as a prerequisite for Game Theory II offered in the Winter quarter.