Students are introduced to computer programming as it pertains to collecting and analyzing linguistic data. The particular programming language is chosen at the discretion of the instructor. Graduate-level requirements include more challenging exams; 50% greater contribution to their respective group projects; 9 instead of 6 assignment; additional readings from the primary literature.
Although standard graduate statistics courses prepare students to design and run statistical analyses, courses generally do not spend a great deal of time discussing data management and workflows, which are critical to making research replicable, efficient, and accurate. This is unfortunate because the best designed statistical analysis is easily undone by poor data management, whether through misconstructed variables, unreplicable workflows, and/or poorly commented or documented workflows and programs. This course addresses this oversight by focusing on computational tools for data management and workflows, including using software packages such as Stata and python.
Formal models for the study of cultural fields, social and cultural logics, cultural classification, the mutual shaping of networks and culture, modeling of cultural toolkits, narrative modeling, and analysis of ethnographic data.
Neural networks are a branch of machine learning that combines a large number of simple computational units to allow computers to learn from and generalize over complex patterns in data. Students in this course will learn how to train and optimize feed forward, convolutional, and recurrent neural networks for tasks such as text classification, image recognition, and game playing.
Most of web data today consists of unstructured text. This course will cover the fundamental knowledge necessary to organize such texts, search them a meaningful way, and extract relevant information from them. This course will teach natural language processing through the design and development of end-to-end natural language understanding applications, including sentiment analysis (e.g., is this review positive or negative?), information extraction (e.g., extracting named entities and their relations from text), and question answering (retrieving exact answers to natural language questions such as "What is the capital of France" from large document collections). We will use several natural language processing toolkits, such as NLTK and Stanford's CoreNLP. The main programming language used in the course will be Python, but code written in Java or Scala will be accepted as well. Graduate-level requirements include implementing more complex, state-of-the-art algorithms for the three proposed projects. This will require additional reading of conference papers and journal articles.
This course will introduce students to the concepts and techniques of data mining for knowledge discovery. It includes methods developed in the fields of statistics, large-scale data analytics, machine learning, pattern recognition, database technology and artificial intelligence for automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns. Topics include understanding varieties of data, data preprocessing, classification, association and correlation rule analysis, cluster analysis, outlier detection, and data mining trends and research frontiers. We will use software packages for data mining, explaining the underlying algorithms and their use and limitations. The course include laboratory exercises, with data mining case studies using data from many different resources such as social networks, linguistics, geo-spatial applications, marketing and/or psychology.
This course will guide students through advanced applications of computational methods for social science research. Students will be encouraged to consider social problems from across sectors, like health science, education, environmental policy and business. Particular attention will be given to the collection and use of data to study social networks, online communities, electronic commerce and digital marketing. Students will consider the many research designs used in contemporary social research and will learn to think critically about claims of causality, mechanisms, and generalization in big data studies. Graduate requirements include additional readings and a more in-depth final paper than is required at the undergraduate level.
