Fall 2015
HCI 530: HCI for Big Data
The underlying theories of Data Science are addressed in the course.
Additionally, the techniques required for developing
meaningful visualizations on large datasets are explored. There are two
distinct components to the
course: exploring visualizations from an HCI perspective, and the
manipulation of large datasets. Therefore, the course will focus on the
use of Data Science techniques for HCI visualizations; particularly the
use of MapReduce frameworks like Hadoop, coding with the Hive and Pig
programming languages, the use of data analysis tools like Tableau and
Qlikview, and on programming D3.js for interactive visualizations.
TIE-12206: Gamification Design
This seminar course, taught to PhD students at three
universities: Tampere
University, Oulu University, and Tampere University of Technology as a
Fulbright Scholar, introduces the concepts of gamification,
engagement, and motivation. The course focuses on the emerging theories
and frameworks of gamification and examines how they might
best be utilized in design. It covers the
mechanics of gamification design, including the
benefits, caveats, and risks that designing gamification tasks
might involve. More information about this course can be found
here.
Summer 2015
HCI 510: HCI Methods I - Design
and Evaluation
This course provides students with a detailed introduction
to the methodologies used in the design and evaluation of human
computer interfaces as well as research in HCI. These methodologies
permit the evaluation of user needs, comparisons of design
alternatives, the evaluation of existing products, and basic research
in HCI. (Taken from the SUNY Oswego Course Catalog.)
HCI 511: HCI Methods II - Research and
Statistical Methods for HCI
Building on the knowledge learned in HCI 509 (or
PSY 280/290) and in HCI 510, this course covers the process of scientific
inquiry,
motivating a study, and developing solid hypotheses. The course focuses
on post-hoc analysis for parametric methods such as Analysis of
Variance (ANOVA), an in-depth evaluation of non-parametric methods,
correlation and regression. Course is taught using R and/or SPSS and
focuses on lab assignments using HCI data.
HCI 505/PSY 407 - Human Factors
This course will provide an in depth review of the
application of psychology to the design, development, and assessment of
systems, products and information. Students will be provided with an
understanding of human abilities, the user centered design process that
accounts for those abilities, and methodologies for assessing
usability. Through the review of case studies human factors issues
related to human-computer interfaces, control design, workspace design
and the needs of special populations (e.g. elderly) will be studied.
(Taken from the SUNY Oswego Course Catalog.)
HCI 531/CSC 490/MBA 590 - Software
Entrepreneurship
This course is about entrepreneurship and specifically about
starting, growing, managing, leading, and ultimately exiting a new
technology-based venture. The course sessions will follow the natural
order of starting a new software-related business: choosing your idea
and your team, validating that idea with customers, honing your initial
pitch, dealing with the legal issues of starting a business, building a
great product, deciding among financing strategies, developing a
go-to-market and operating plan, and exiting successfully.
ISC 220 - Information Storage and Retrieval
Consideration of the basic principles and tools for analysis
and retrieval of information in various information systems (both
textual and database systems). Topics include analysis and storage of
information, retrieval concepts and types of retrieval systems. (Taken
from the SUNY Oswego Course Catalog.)
CSC 212 - Introduction to Computer Science
(Lab)
The notion of "object" directs the discipline of programming
presented in this course. The Java programming language serves as the
medium through which key ideas are introduced. The "smaller" issues of
message passing and control flow are presented, as are the "larger"
issues of abstraction, encapsulation, and hierarchy. Variables and
typing, procedures and parameters are discussed. Functionality provided
in specific java packages is employed. Standard algorithms are
presented. Problem solving strategies are articulated and exploited
(Taken from the SUNY Oswego Course Catalog.)