INSH 6406 Analyzing Complex Digitized Data
Northeastern University, Fall 2024
Instructor
​This course introduces cutting-edge methods for analyzing text and images using the open-source programming language Python. Social scientists often deal with large amounts of digitized data, which tend to be unstructured. For decades, we have aimed to utilize text and images as a data source. The wealth of data contained in transcripts, manuscripts, and photographs is both a blessing and a curse. While the written word and images hold answers to many fascinating questions, extracting and using this data efficiently and reliably presents significant methodological challenges. There is a growing need to learn how to handle web datasets effectively. To address this, the course explores machine learning models for a wide array of web data. By the end of this class, you'll be ready to use these methods in your research, whether in academia or industry. To achieve this, the course will help you (i) practice computational skills to scrape and manage data from the web, (ii) learn machine learning techniques to analyze data and visualize results, and (iii) interpret the findings and limitations in conjunction with social science theories.​
COMM 2105 Social Networks
Northeastern University, Spring 2024, Spring 2025
Instructor
In this course, we delve into the intricacies of social networks, extending far beyond the surface of social media to the expansive networks that shape our lives—from personal relationships to professional interactions. Through the application of social network theories and analytical methods, this course aims to decode the intricate web of connections that orchestrate the world we live in. You will be invited to adopt a network perspective, a transformative lens through which the inherent linkages of our societal, professional, and technological realms can be discerned and scrutinized. As we navigate these themes together, we will uncover the dynamics of network formation and explore the profound impact that these structures have on individual behavior, belief systems, and access to opportunities. By the end of the course, you will have insights into optimizing personal networks, comprehend factors behind influential figures like Steve Jobs, grasp the spread of pandemics, the formation of social movements, and the polarization of the Internet.
INSH 6500 Statitscial Analysis
Northeastern University, Fall 2023, Spring 2024, Spring 2025
Instructor
This course is specifically designed as an introductory course in probability and statistics, tailored to meet the needs of graduate students in the College of Social Sciences and Humanities (CSSH). The primary goal of this course is to provide students with a strong foundation in regression and generalized linear models, which will be extensively examined in INSH 7500 or an equivalent advanced statistics course. To achieve this objective, the course will cover a broad range of topics: the fundamental concepts of probability theory, the properties of random variables, asymptotic approximations, statistical estimators, hypothesis testing, and causal inference. In order to provide hands-on experience and practical skills, the course will incorporate statistical computing using Excel. Students will have the opportunity to gain proficiency, enabling them to manipulate, analyze, and visualize data effectively.
POLS 2400 Quantitative Techniques
Northeastern University, Spring 2023, Summer 2024
Instructor
POLS2400 serves as an introduction to quantitative methods in political science. This course introduces students how political ‘scientists’ see and leverage data to understand human behaviors and society. It covers a range of topics including data visualization through tables and graphs, descriptive statistics, probability, sampling distributions, estimation, hypothesis testing, and the analysis of relationships among variables, such as regression analysis. There are various methods to teaching statistics. Some instructors focus heavily on formulas and calculations, while others concentrate on interpreting computer-generated results. Some emphasize procedural techniques, whereas others prioritize conceptual comprehension. In this course, I aim to integrate and balance all these approaches. Performing certain calculations is essential for understanding the logic behind statistical tools, although you won't be required to memorize any formulas. The course emphasizes understanding when and how to use different statistical procedures and ensures you become proficient in computer-based analysis and interpretation.
POLITSC 7552 Quantitative Political Analysis II
Ohio State University, Spring 2022
Instructor
This course is doctoral-level course of quantitative methods to learn a family of the linear and generalized linear modeling using maximum likelihood estimation (MLE) and Bayesian estimation. The primary purpose of the course is to build on statistical foundations taught in PoliSci 7551. We will perform regression analysis with the following types of outcome variables: continuous, counts, dichotomous outcomes, ordered categorical outcomes, unordered categorical outcomes, bounded variables, and multilevel variables.