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Introduces concepts of the management of data throughout its lifecycle. Understanding different types of data and their functions. Managing data in the context of a particular discipline or profession. Finding and evaluating data purposefully. Using data ethically and responsibly. Creating and sharing data for reuse, accountability, and enhancement. Making decisions and communicating using data, including data analysis and visualization. Protecting and archiving data. This course is currently required for and restricted to the Engineering in the World of Data Learning Community. Additional sections will be offered for students not in the LC.
This course is for prospective Purdue undergraduate researchers who are interested in conducting undergraduate research or creative endeavors. Purdue students who have not already started an independent research project with a research mentor will learn valuable skills to market themselves to individuals and research programs. Throughout the course, students will develop components for a final application packet to submit to a research team or program they choose.
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.
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.
This course is for prospective Purdue undergraduate researchers who are within their first two years at Purdue who are interested in conducting undergraduate research or creative endeavors. New Purdue Boilermakers who have not already started an independent research project with a research mentor will learn valuable skills to market themselves to individuals and research programs to prepare for a project in the following semester. Throughout the course, students will develop components for a final application packet to submit to a research team or program they choose while focusing on the transition to college expectations regarding research-related topics. Must be a first-year or second-year student.
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.
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.
This seminar will introduce students to the exciting world of rare books and the endless research possibilities they provide. The co-instructors will cover the many methods necessary in understanding and successfully researching rare books primarily published between the 15th and 19th centuries. Students will engage with multiple rare books from the Purdue University Archives and Special Collections as part of in-class activities and assignments. Applying what they learn from reading and discussion to real-life examples will create hands-on, active-learning experiences that will enhance their own critical thinking skills when researching a rare book.
This course is for current Purdue undergraduate researchers who want to learn how to serve as peer mentors to undergraduate researchers early in their careers. This course will train students on how to create mutually beneficial and productive mentorships. This course will provide research-based best practices for mentoring newer student researchers while developing as a cohort of new research mentors. This course is especially useful for those students who enjoy supporting peer researchers or plan to continue into more formal mentorship roles as a senior undergraduate researcher, graduate student, or research supervisor in academia or industry. Must be a current undergraduate researcher.
This course will introduce systematic review methodology of published health sciences literature. Students will learn to form research questions, develop inclusion and exclusion criteria, search for evidence, manage data, and assess the risk of bias.
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, stack overflow, GIS StackExchange, etc.
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.