Below are the courses that will be available to QMSS students during Fall Semester 2017. Course information will continue to be updated as it becomes available. If you see discrepancies between this list and the Columbia Directory of Classes or Vergil, you should default to the details on this page.

Advanced Registration for Fall Semester begins Monday, April 17th. You should check SSOL to see when your first registration appointment is. Full information is available on GSAS Academic Calendar.

 

UPDATED 07/31/2017

GIS and Spatial Analysis of Social Data
GR4070
Michael Parrott
M 6:10P-8:00P
This course introduces students to basic spatial analytic skills. It covers introductory concepts and tools in Geographic Information Systems (GIS) and database management. As well, the course introduces students to the process of developing and writing an original spatial research project. Topics to be covered include: social theories involving space, place and reflexive relationships; social demography concepts and databases; visualizing social data using geographic information systems; exploratory spatial data analysis of social data and spatially weighted regression models, spatial regression models of social data, and space-time models. Use of open-source software (primarily the R software package) will be taught as well.
VIEW PREVIOUS SYLLABUS HERE

Data Analysis for the Social Sciences
GR4015
Greg Eirich
W 11:00A-1:00P
This course is meant to provide an introduction to probability and social statistics, tailored to the types of analyses and data issues encountered by QMSS students. The chief goal is to help students generate and interpret quantitative data in helpful and provocative ways. The hope is that by trying to measure the social world, students will see their thinking become clearer and their understandings of concepts grow more complex. They will also become competent at reading statistical results in social science publications and in other media. Only basic mathematics skills are assumed, but it is hoped that students will become more facile with numbers, functions and their relationships. Another important goal of the course is to teach students how to manipulate and analyze data themselves using statistical software. We will focus mainly on the program R. There will be an optional lab section every other week, which will be devoted to using these software programs to practice commands and to develop a paper using the General Social Survey, World Values Survey or another dataset of the student’s choosing.
VIEW PREVIOUS SYLLABUS HERE

Quantitative Theory and Methodology in the Social Sciences
GR4010
Christy Baker-Smith; Michael Parrott; Marco A Morales
T 6:10P-8:00P; W 4:10P-6:00P; Th 6:10P-8:00P; 
This interdisciplinary course, taken in the fall semester, is a comprehensive introduction to quantitative research in the social sciences. The course focuses on foundational ideas of social science research, including strengths and weaknesses of different research designs, interpretation of data drawn from contemporary and historical contexts, and strategies for evaluating evidence. The majority of the course is comprised of two-week units examining particular research designs, with a set of scholarly articles that utilize that design. Topics include: the “science” of social science and the role of statistical models, causality and causal inference, concepts and measurement, understanding human decision making, randomization and experimental methods, observation and quasi-experimentation, sampling, survey research, and working with archival data.
VIEW PREVIOUS SYLLABUS HERE

Master's Thesis
GR5999
Elena Krumova
R 6:10P-8:00P
This course is designed to help you make consistent progress on your master’s thesis throughout the semester, as well as to provide structure during the writing process. The master’s thesis, upon completion, should answer a fundamental research question in the subject matter of your choice. It should be an academic paper based on data that you can acquire, clean, and analyze within a single semester, with an emphasis on clarity and policy relevance. Remember that your thesis is not designed to be the crowning achievement of your career. If you find that the scale of your topic is too great, please choose a limited number of research questions to explore for the master’s thesis. Keep in mind that your time is limited! Early semester homework: Selecting a topic of interest is often the most difficult part of writing an academic paper, but deciding on the data you will be using is a significant step towards completing a satisfactory dissertation project. We will discuss your data before exploring plausible research designs. If you have elected to change topics from the literature review you prepared for G4010, let me know and begin researching other ideas so that you are prepared to move quickly through the semester.
VIEW PREVIOUS SYLLABUS HERE

Data Mining
GR4058
Benjamin Goodrich
T 6:10P-8:00P
The class is roughly divided into two parts: 1. programming best practices, exploratory data analysis (EDA), and unsupervised learning 2. supervised learning including regression and classification methods In the first part of the course we will focus writing R programs in the context of simulations, data wrangling, and EDA. Unsupervised learning is focused on problems where the outcome variable is not known and the goal of the analysis is to find hidden structure in data such as different market segments from buying patterns or human population structure from genetics data. Supervised learning deals with prediction problems where the outcome variable is known such as predicting a price of a house in a certain neighborhood or an outcome of a congressional race.
VIEW PREVIOUS SYLLABUS HERE

Modern Data Structures
GR5070
Thomas Brambor
W 6:10P-8:00P
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.
VIEW PREVIOUS SYLLABUS HERE

Research Seminar
GR4021 & GR4022
Gregory Eirich
W 08:10P-10:00P
This course has two goals. One, it is designed to expose students in the QMSS degree program to different methods and practices of social science research. Seminar presentations are given on a wide range of topics by faculty from Columbia and other New York City universities, as well as researchers from other settings. Two, it is also designed to give students important professional development skills, particularly around academic writing, research methods and job skills.
VIEW PREVIOUS SYLLABUS HERE (NOTE: Speakers will differ from last spring)

Time Series, Panel Data and Forecasting
GR4016
Gregory Eirich
F 10:10A-12:00P
This course will introduce students to the main concepts and methods behind regression analysis of temporal processes and highlight the benefits and limitations of using temporally ordered data. Students study the complementary areas of time series data and longitudinal (or panel) data. There are no formal prerequisites for the course, but a solid understanding of the mechanics and interpretation of OLS regression will be assumed (we will briefly review it at the beginning of the course). Topics to be covered include regression with panel data, probit and logit regression of pooled cross-sectional data, difference-in-difference models, time series regression, dynamic causal effects, vector autoregressions, cointegration, and GARCH models. Statistical computing will be carried out in R.
VIEW PREVIOUS SYLLABUS HERE

 

Non-QMSS Concentration Classes

ECONOMICS CONCENTRATION

Advanced Macroeconomics
GU4213
Andres Drenik
MW 2:40P-3:55P
Prerequisites: ECON W3211, W3213, W3412 and MATH V2010. An introduction to the dynamic models used in the study of modern macroeconomics. Applications of the models will include theoretical issues such as optimal lifetime consumption decisions and policy issues such as inflation targeting. This course is strongly recommended for students considering graduate work in economics.
VIEW PREVIOUS SYLLABUS HERE

Advances Econometrics
GU4412
Seyhan Erden
TTH 1:10P-2:25P
Prerequisites: ECON W3211, ECON W3213, ECON W3412, MATH V2010. The linear regression model will be presented in matrix form and basic asymptotic theory will be introduced. The course will also introduce students to basic time series methods for forecasting and analyzing economic data. Students will be expected to apply the tools to real data.
VIEW PREVIOUS SYLLABUS HERE
 

DATA SCIENCE CONCENTRATION

Algorithms for Data Science
CSOR W4246
Eleni Drinea
TTH 1:10P-2:25P
Prerequisites: basic knowledge in programming (e.g., at the level of COMS W1007), a basic grounding in calculus and linear algebra. Methods for organizing data, e.g. hashing, trees, queues, lists,priority queues. Streaming algorithms for computing statistics on the data. Sorting and searching. Basic graph models and algorithms for searching, shortest paths, and matching. Dynamic programming. Linear and convex programming. Floating point arithmetic, stability of numerical algorithms, Eigenvalues, singular values, PCA, gradient descent, stochastic gradient descent, and block coordinate descent. Conjugate gradient, Newton and quasi-Newton methods. Large scale applications from signal processing, collaborative filtering, recommendations systems, etc.
VIEW PREVIOUS SYLLABUS HERE
 
Probability and Statistics
STAT W5701
Banu Baydil
TR 7:40pm-8:55pm
Prerequisites: Calculus This course covers the following topics: Fundamentals of probability theory and statistical inference used in data science; Probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem; Statistical inference; point and confidence interval estimation, hypothesis tests, linear regression.
VIEW PREVIOUS SYLLABUS HERE