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University of Michigan School of Information



705 - First Semester Experience

This required course will expose first-semester students to a wide range of information that will be useful as they navigate the UMSI doctoral program, the research process, and academic culture. The seminar will be led by the Doctoral Program Director but will include visits from other faculty, students, staff, and campus resource representatives.

721 - Data Mining: Methods and Applications

With the explosive growth of information generated from different sources, in a variety of formats, and with various qualities, information analysis has become challenging for researchers in many disciplines. Automatic, robust, and intelligent data mining techniques have become essential tools to handle heterogeneous, noisy, nontraditional, and large-scale data sets. This is a doctoral seminar course of advanced topics in data mining. The course provides an overview of recent research topics in the field of data mining, the state-of-the-art methods to analyze different genres of information, and the applications to many real world problems. The course will highlight the practical applications of data mining instead of the theoretical foundations of machine learning and statistical computing. The course materials will focus on how the information in different real world problems can be represented as particular genres, or formats of data, and how the basic mining tasks of each genre of data can be accomplished using the state-of-the-art techniques. To this end, the course is not only suitable for doctoral students who are doing research in data mining related fields, but also for those who are consumers of data mining techniques in their own disciplines, such as natural language processing, network science, human computer interaction, economics, social computing, sociology, business intelligence, and biomedical informatics, etc.

755 - Unorthodox Research Methods

Any traditional research method was once unorthodox. While many are prone to see methods as boring tools (or even as a necessary but unpleasant step on the road to results), any common method was once daring and controversial. This seminar will cover very recent developments in both qualitative and quantitative social scientific research methods and attempt to address the question of how new research methods are invented, applied, transferred between problems and disciplines, and formalized. The overall focus of the course will be research design, rather than learning and procedures of a single method. In addition, we will spend some time trying to think creatively about possible new methods and designs. Readings in the course will be split between classics and readings concerning very recent innovations in methods. In discussion of recent methodological trends, particular attention will be paid to internet / new media research, new digital sources of data (sometimes called "big data" or "e-social science"), spatial / geographic methods, visualization as a research method, and unobtrusive methods. A goal of the seminar is to encourage researchers to conceptualize methodology - whether using new or old methods - as a creative art.

840 - Research Methods

PhD level introduction to research design from a methods perspective. Examines various research methods with illustrations drawn from specific studies. Includes discussion of the scientific method and research design, issues of problem selection, data collection, data analysis, and research report evaluation.

998 - Curriculum Practical Project

This internship/work experience course is designed to provide opportunities to further professional experiences in addition to the required curriculum. Students will work in an internship or obtain a temporary employment opportunity and will apply and add to the practical skills and knowledge acquired in their academic studies. It is available to students in good academic standing. The independent field experience is under the supervision of School of Information faculty instructor. Faculty instructors must be tenure-track faculty member. The student should meet with their faculty instructor regularly to develop learning goals and assignments.