INSH 6406 Analyzing Complex Digitized Data
Northeastern University, Fall 2024
Instructor
This course introduces cutting-edge ways of structuring and analyzing complex data or digitized text-as-data and image-as-data using the open-source programming language Python. Scholars across multiple disciplines are finding themselves face-to-face with massive amounts of digitized data. In the humanities and social sciences, these data are often in the form of unstructured text and un- or under-structured data. Encourages students to think about novel ways they can apply these techniques to their own data and research questions and to apply the methods in their own research, whether it be in academia or in industry. We will learn Tensorflow and Keras to learn deep learning framework for a variety of text and image mining such as Latent Dirichlet Allocation (LDA), word/image embedding, sentiment analysis and text/image classification.
COMM 2105 Social Networks
Northeastern University, Spring 2024
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
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 R. Students will have the opportunity to gain proficiency in utilizing R as a statistical tool, enabling them to manipulate, analyze, and visualize data effectively.
POLS 2400 Quantitative Techniques
Northeastern University, Spring 2023, Summer 2024
Instructor
This class is an intro-level course of quantitative methods in Political Science to introduce students how political ‘scientists’ see and leverage data to understand human behaviors and society. The primary purpose of this course is to build on statistical basics and develop data analytic skills including how to describe data, draw inferences from samples to population, set up hypotheses, and test them based on data in experimental and observational research.
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.