LIS-S 407 Social Science Data
3 credits
- Prerequisite(s): None
- Delivery: On-Campus
Description
This course reviews data practices in the social sciences. Students examine geospatial data sources, management, and analytical tools for the social sciences. Additionally, students explore newly developing geospatial technologies and analytical practices, including data visualization and big data methods for the social sciences, and ethical and policy considerations.
Social science data is used to study human behavior. It informs policies and practices related to crime, education, poverty, public health, human services, and more. In this course you will develop skills and experience that prepares you for informatics positions requiring knowledge of social science data and the tools and techniques used to manage and analyze them. The course focuses on how geospatial tools and methods can be applied in the creation, analysis, visualization, and management of social science data. Multiple hands-on practice activities, as well as a realistic case study that leverages the rich collection of social science data available for Central Indiana, provide opportunities to apply your skills throughout the course.
Program Learning Outcomes Supported
Instructors map their courses to specific Data Science Program Learning Outcomes (PLOs). Mapped program goals drive the design of each course and what students can expect to generally learn.
- A1: Data Literacy - Distinguish between data, information, and knowledge.
- A2: Data Literacy - Analyze the value and key role data plays in society in providing opportunities to expand knowledge, to innovate, and to influence.
- A3: Data Literacy - Analyze datasets in context to determine data veracity including bias in data collection or representation.
- A4: Data Literacy - Assess values with respect to the use of data technologies.
- B4: Data Science - Conceptualize and design effective visualizations for a variety of data types and analytical tasks.
- C4: Information Science - Understand the characteristics of various data types generated and used by a variety of disciplines, subdisciplines, research communities, and government organizations.
- D1: Data Ethics - Understand the relation between data, ethics, and society.
- E1: Other Topics - Design, conduct, and write up results of research.
Learning Outcomes
- Identify and evaluate the role social science data in academia, government, industry, and other organizations.
- Summarize emerging data practices in the social sciences.
- Analyze and evaluate best practices for data management in the social sciences.
- Analyze and evaluate geospatial tools, technologies, and data analytic practices in the social sciences.
- Create geospatial data analysis plans for social science data.
Profiles of Learning for Undergraduate Success (PLUS) Alignment
Instructors align their courses with the Profiles of Learning for Undergraduate Success. The profiles provide students various opportunities to deepen disciplinary understanding, participate in engaged learning, and refine what it means to be a well-rounded, well-educated person prepared for lifelong learning and success.
- P2.1 Problem Solver – Think critically
- P2.3 Problem Solver – Analyzes, synthesizes, and evaluates
- P3.2 Innovator – Creates/designs
- P4.3 Community Contributor – Behaves ethically
- P4.4 Community Contributor – Anticipates consequences
Course Overview
Module 1: Overview of the role of social science data in academia, government and industry and other organizations; GIS; introduction to case studies
Module 2: Spatial concepts; GIS data formats, GIS tools
Module 3: Map design principles; thematic maps development techniques
Module 4: Designing appropriate questions; raw vs. geographic aggregations, aggregated social data sources
Module 5: Raw and administrative social data sources; mobile technologies for collecting raw data; georeferencing tools
Module 6: Design considerations for creating social science data; GIS tools & techniques for creating social science data; metadata standards
Module 7: Current social science data munging practices (current practices in data discovery, structuring, cleansing, validation, and enrichment)
Module 8: Emerging social science data munging practices (emerging practices in data discovery, structuring, cleansing, validation, and enrichment)
Module 9: Social science data analysis part I - descriptive analysis
Module 10: Social science data analysis part II - methods for identifying where patterns exist.
Module 11: Social science data analysis part III - methods for explaining patterns
Module 12: Social science data ethics, online Mapping and analysis - social science geospatial data ethical and policy considerations; desktop vs online mapping; ArcGIS Online
Module 13: Telling stories with social science data - storytelling strategies, creating StoryMaps
Module 14: Lab work
Module 15: Lab work
Policies and Procedures
Please be aware of the following linked policies and procedures. Note that in individual courses instructors will have stipulations specific to their course.