Data Analytics for Information Professionals: Fall 2015

Instructor:

Yla Tausczik

School:

iSchool at Maryland

Semester:

Fall 2015

Description:

Advances in hardware and software technologies have led to a rapid increase in the amount of data collected, with no end in sight. Decision making in the coming decades will depend, to an ever greater extent, on extracting meaning and knowledge from all that data. In this class we focus on one branch of statistics, inferential statistics, to help us reason about data. By gathering datasets, formulating proper statistical analyses and executing these analyses, information professionals play a significant role in bridging the gap between raw data and decision making.

This course will introduce basic concepts in data analytics including study design, measure construction, data exploration, hypothesis testing, and statistical analysis. The course also provides an overview of commonly used data manipulation and analytic tools. Through homework assignments, projects, and in-class activities, you will practice working with these techniques and develop statistical reasoning skills.

Required Textbook:

Rice University. The Online Stats Book. Available online.

Link to Syllabus:

http://ischool.umd.edu/sites/default/files/syllabi/inst627fall15tausczik.pdf

Introduction to Scientific Data Informatics: Spring 2014

Instructor:

William L. Anderson

School:

University of Texas at Austin

Semester:

Spring 2014

Description:

We examine the information properties of scientific data, develop criteria to appraise some properties of scientific data sets, take an expedition into the Semantic Web and the growing network of Linked Data, examine issues of long-term management of, and access to, scientific data, and endeavor to write a semantically marked up and enhanced term paper about what we learn.

Required Textbook:

No required textbook.

Link to Syllabus:

http://courses.ischool.utexas.edu/Anderson_Bill/2014/spring/INF385T/index.php

Information Modeling: Spring 2014

Instructor:

Karen Wickett

School:

University of Texas at Austin

Semester:

Spring 2014

Description:

An introduction to the foundations of the information modeling methods used in current digital library applications as well as in information management in general. The specific methods considered are relational database design, conceptual modeling, markup systems, and ontologies. The basic concepts underlying these methods are, respectively, relations, entities, grammars, and logic. Implementations include relational database design, ER/EER/UML diagrams, XML markup languages, and RDF/OWL semantic web languages. First order logic is emphasized throughout as the foundational framework for information modeling in general, and for contemporary web-based information management and delivery systems (including semantic web technologies) in particular.

Required Textbook:

Teller, P. 1989. A Modern Formal Logic Primer. (online)
Elmasri, R., Navathe, S. B. 2010. Fundamentals of Database Systems, 6th ed.

Link to Syllabus:

http://courses.ischool.utexas.edu/wickett/2014/spring/385T/home.html

Data Mining: Fall 2014

Instructor:

Bei Yu

School:

Syracuse

Semester:

Fall 2014

Description:

This course will introduce popular data mining methods for extracting knowledge from data. The principles and theories of data mining methods will be discussed and will be related to the issues in applying data mining to problems. Students will also acquire hands-on experience using state-of-the-art software to develop data mining solutions to scientific and business problems. The focus of this course is in understanding data and how to formulate data mining tasks in order to solve problems using the data.

The topics of the course will include the key tasks of data mining, including data preparation, concept description, association rule mining, classification, clustering, evaluation and analysis.

Required Textbook:

Tan, P., Steinbach, M., Kumar, V. 2005. Introduction to Data Mining.

Link to Syllabus:

http://my.ischool.syr.edu/Uploads/CourseSyllabus/IST565_Fall2014-Yu-syllabus-schedule-1151.24293-625c3297-a249-4509-bca4-a98ca437d734.pdf

Text Analytics: Summer 2013

Instructor:

Lauren Harrison

School:

Long Island University

Semester:

Summer 2013

Description:

Text analytics as a function of knowledge management-Information Literacy & Emerging Technologies

Required Textbook:

No required textbook.

Link to Syllabus:

http://palmerblog.liu.edu/wp-content/uploads/2010/04/901-Text-Analytics-SU-13-Harrison.pdf

Digital Information Representation: Fall 2013

Instructor:

Heting Chu

School:

Long Island University

Semester:

Fall 2013

Description:

The course covers principles and concepts of information representation methods for the purpose of information retrieval in the digital environment. It includes preparation of abstracts, automatic summarization, subject analysis and vocabulary control, thesaurus/folksonomy/ontology construction, index creation, tagging, and evaluation of information representation and retrieval (IRR) systems.

Required Textbook:

Cleveland, D. B., Cleveland, A. D. 2013. Introduction to indexing and abstracting, 4th ed.
Wellisch, H. H. 1995. Indexing from A to Z, 2nd ed.

Link to Syllabus:

http://palmerblog.liu.edu/wp-content/uploads/2010/04/Syllabus768-2013Fall.pdf

Information Visualization and Presentation

Instructor:

School:

Rutgers

Semester:

Description:

This course provides a thorough introduction to the emerging field of Information Visualization. The goal of Information Visualization is to use human perceptual capabilities to gain insights into large and abstract data sets that are difficult to extract using standard query languages. Specific abstract data sets that will be studied are: symbolic, tabular, networked, hierarchical, or textual information.

Required Textbook:

No required textbook.

Link to Syllabus:

http://comminfo.rutgers.edu/images/syllabus_554.pdf

Metadata Generation and Interfaces for Massive Datasets: Fall 2014

Instructor:

Unmil Karadkar

School:

University of Texas

Semester:

Fall 2014

Description:

Large-scale digitization projects as well as increasing quantities of born-digital materials have put enormous collections of documents and data
within our reach. The use of programmatic techniques is necessary for managing such massive collections and improving their utility for specific purposes. The ability to blend understanding of digital collections, computational techniques (such as machine learning, data mining, image, audio, or video processing, data analysis), user experience design that you gain from this course will enable you to.craft small projects that demonstrate the viability of your proposed solutions to your supervisors, whether in academic institutions, not for profit organizations, start ups, or large corporations.

Required Textbook:

No required textbook.

Link to Syllabus:

https://utexas.instructure.com/courses/1103536/assignments/syllabus

Digital Curation: Spring 2014

Instructor:

Dorothea Salo

School:

University of Wisconsin-Madison

Semester:

Spring 2014

Description:

• Assess, plan for, manage, and execute a small-scale data-management or digital-archiving project.
• Assess digital data for preservability; make yes-or-no accessioning decisions.
• Understand (and where relevant, apply) technological, economic, and social models of digital preservation and sustainability.
• Understand forms, formats, and lifecycles of digital data across a wide breadth of contexts.
• Evaluate software and hardware tools relevant across the data lifecycle.
• Construct a current-awareness strategy; assimilate substantial amounts of relevant writing.
• Self-sufficiently acquire technical knowledge.

Required Textbook:

No required textbook.

Link to Syllabus:

http://files.dsalo.info/668syll2014.pdf