Instructors

• Kendall Roark

Description

This course provides an introduction to Ethical, Legal Social Issues (ELSI) in Data Science. Students will be introduced to interdisciplinary theoretical and practical frameworks that can aid in exploring the impact and role of Data Science in society. This is a writing intensive course. Students will work individually and on collaborative assignments.

Instructors

• JJ Sadler
• Amy Childress

Description

This course is for current Purdue undergraduate researchers to hone skills necessary for successfully reflecting on and completing the experience. During this course, students will utilize their research experience to apply skills such as managing time with a research project, communicating your research, utilizing Purdue Libraries' resources, and providing feedback to peer researchers. Students will deliver research pitches about their own project and provide critiques to others’ pitches.

Instructors

• John Fritch

Description

This course examines concepts of trust and authority and uses them to promote critical thinking and assessments regarding credibility. Authoritative information sources, evaluative criteria, and technical tools will be enumerated and discussed as students work through a research issue of personal interest. Topics include: what is trust and why is it significant, what types of authority exist and what specifically is cognitive authority, how is in-person trust and authority different from digital trust, when does credibility matter and what are criteria for determining credibility, how and where is quality information found, techniques of the nefarious (cons, scams, spam, phishing etc.), considerations of a skeptical consumer (research project). This course is designed to help lower-level undergraduate students think deeply about trust, authority, evaluation, and quality, topics that they may not have consciously considered or studied previously.

Instructors

• JJ Sadler
• Amy Childress

Description

This course is for current Purdue undergraduate researchers to build upon the previous course and focus on research data collection, presentation, and communication for current Purdue undergraduate researchers. During this course, students will learn and discuss various forms of data and collection practices. Students will develop their own academic poster to present their research project's data and implications. Students are encouraged to present their poster at one of Purdue’s undergraduate research conferences near the end of the semester.

Instructors

• Clarence Maybee
• J.P. Herubel
• Thom Gerrish

Description

Any time information is used for a particular means conflict is inevitable. This seminar course examines historical and current societal issues and challenges related to the consumption and production of information. The course delves into how the use and misuse of information has resulted in historical and contemporary challenges, including ethical concerns in the dissemination of science, technology, engineering and mathematics (STEM) information, the capturing and sharing of surveillance and privacy information, the creation and sharing of disinformation and ‘fake’ news, and information on social media that takes on a life of its own (i.e., going viral). New issues will be examined weekly and students will be able to introduce topics of interest as well. The cumulative final project will allow students to select and explore their own topics on an evolving information practice and its influence on culture or society.

Instructors

• JJ Sadler
• Amy Childress

Description

This course is for current Purdue undergraduate researchers to build on previous courses and focus on continuing their education in graduate or professional school. During this course, students will learn and discuss the various phases of identifying, selecting, applying to and funding graduate or professional school programs. Students will also gain a deeper comprehension of the qualities and skills that make research mentors effective while developing skills they will need to be successful mentees and peer mentors. Students will conduct research to identify potential programs of interest and develop a statement of purpose.

Instructors

• Jane Yatcilla

Description

So you want to go to medical school or veterinary school, or become a chiropractor, dentist, public health specialist, osteopath, occupational therapist, physical therapist, physician’s assistant, or get a PhD and do clinical research. Take this course to develop critical information skills to support your professional goals and prepare you for graduate or professional school. Show up on day one of professional or graduate school knowing how to navigate PubMed and other databases, differentiate between various types of research articles, and save and organize articles so you can easily locate them, “cite while you write,” and share articles with your classmates or research group.

Instructors

• Chao Cai
• Megan Sapp Nelson

Description

Intensive study of selected topics varying from semester to semester, from the practice of information and data sciences. Topics may include data management and organization, digital scholarship, data visualization, computer languages for data and information science, information literacy, archival literacy, and emerging trends in information and data science. Permission of the instructor is required for undergraduates.

Instructors

• Margaret Phillips
• Dave Zwicky

Description

This course focuses on information strategies for successful research in science, engineering, and technology disciplines. Students will learn about how scholarly information and discipline relevant grey literature (e.g., patents, technical standards) are created, organized, disseminated, retrieved, and managed. In addition, students will learn strategies to critically evaluate information and present their research effectively and ethically.

Instructors

• Dharmendra Saraswat
• Gang Shao

Description

This course is designed to help you learn fundamental Python programming concepts, get introduced to the Python scripting environment within ArcGIS Pro, perform data visualization and advanced analytical skills using Python libraries for GIS and spatial data science, automate GIS tasks, learn to use version control with Git and practice basics of sharing code using GitHub. These topics will be taught in the context of solving geoscientific problems. The course consists of readings, quizzes, and laboratory exercises about programming concepts and techniques and a final term project. You will be encouraged to research concepts, examples, and content from online resources such as esri.com, stack overflow, GIS StackExchange, etc.

Instructors

• Margaret Phillips
• Dave Zwicky
• Michael Fosmire

Description

Emphasizes the importance and role of strong information and communication skills (written, oral, graphical, and interpersonal) in a successful engineering or technology career. Search, evaluate, access, use, and synthesize technical information in order to present information clearly, ethically, and effectively in a variety of professional formats.

Instructors

• Kendall Roark

Description

This course offers an interdisciplinary introduction to data management and curation with a focus on the use, value, and organization of data, materials, infrastructure, tools, and scholarly communication in qualitative research. The course will introduce literature concerning ethical and legal considerations of data management and curation, and will provide opportunity for hands-on digital, data literacy, and data manipulation skills development.

Instructors

• Trevor Burrows

Description

Computational analysis of textual data has become increasingly important in the world of digital humanities, digital history, data science, and computational social science. This course provides an introduction to the methods, debates, controversies, and tools of computational text analysis (CTA) specifically crafted for the humanities and social science graduate student. Students will explore the central theoretical debates in CTA while also learning practical hands-on skills in corpus creation, OCR, text mining, topic modeling, sentiment analysis, and other methods. They will learn how CTA relates to established interpretative practices in the larger histories of the humanities and social sciences and the broader context of their own disciplines, and will consider both the possibilities and the limitations of CTA in their own work. While the course is designed for a beginner with little technical training, students will become familiar with the basic elements of coding/scripting using the programming language R and other tools. Upon completion of this course, students will understand the challenges of CTA, be conversant with major theoretical discussions around CTA, and have a foundational understanding of the steps required to incorporate CTA into their regular research practices and particular projects.

Instructors

• Megan Sapp Nelson
• Nicole Kong

Description

This course walks students through the process of preparing a dataset for sharing with both internal and external audiences. Students wil select authoritative datasets for sharing and publication, apply metadata to those datasets, create documentation for end-users of the datasets, and publish the datasets to internal or external data repositories or storage as appropriate.