LIS-S 201 Foundations of Data Studies
- Prerequisites: None
- Delivery: Online
- Semesters offered: Fall, Spring, Summer (Check the schedule to confirm.)
This class introduces digital literacies, focusing on data and information literacy in the media, civic engagement, business, informatics, and data science. Students explore the production of data, their roles as data creators and consumers, and the effects of data practices on society. Students apply their acquired skills in real-world situations.
This course emphasizes the value of data in society and provides students the opportunity to learn basic data concepts and skills. Students will gain an understanding of key factors for data studies including data sources, data ethics, data policy, data evaluation, data manipulation, and data visualization. Additionally, students will gain valuable hands-on experience working with data.
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. B1: Data Science - Organize, visualize, and analyze large, complex datasets using descriptive statistics and graphs to make decisions.
- 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.
- Recognize that data can have value and play a key role in society by providing opportunities to grow knowledge, to innovate, and to influence.
- Identify sources of data to evaluate news and other information.
- Analyze datasets in context to determine the reliability of the information including potential bias in data collection or representation.
- Understand the ethical guidelines and implications for using, managing, and communicating data.
- Examine results produced in data analysis using data visualizations that are suitable for their purpose and targeted audience.
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
Module 1: Introduction to Course
- Course Basics and Course Navigation
- Course Structure and Schedule
- Course Technology
- Course Textbook
- Writing Resources and Student Engagement Roster
- How to Create a Video
Module 2: Introduction to Data Foundations
- Why data is important
- What is Data Science
- Data Lifecycle
- Data Skillsets
Module 3: Data Basics
- What are data?
- Data-Information-Knowledge-Wisdom Hierarchy
- History of data work
- Data formats and data types
Module 4: Finding Data
- How and why data is made available online
- Online databases and other publicly available data
- Biases to consider
- Data documentation
Module 5: Requesting and evaluating data
- Offline Data
- Freedom of Information Act
- Ethical and technical considerations when working with data
Module 6: Data integrity checks
- Data evaluation
Module 7: Data manipulation
- Data manipulation in Excel and Open Refine
- Column carving, removing extraneous spaces, concatenation, extracting dates in Excel
- Using facets in Open Refine
Module 8: Introduction to Data Ethics
- What are ethics?
- Why are ethics important to data and technology?
- How to identify potential ethical issues?
Module 9: Data ethics, part 2
- Big data
- Ethical best practices for data work
- Case study examples
Module 10: Data journalism, part 1
- What is data journalism?
- Data Journalism examples including techniques and tools
Module 11: Data journalism, part 2
- Conducting data journalism
- Acquiring data
- Critiquing data
- Tools for working with data
- Ways to tell a story with data
Module 12: Summary statistics
- Basic descriptive statistics
- Calculating and interrupting summary statistics
Module 13: Introduction to data visualization
- Types of data visualizations
- Pros and cons of visualization types
- How to create basic visualizations
Module 14: Visualization, part 2
- Advanced data visualization
Module 15: Final Project Preparation
- Review of finding, exploring, inspecting, cleaning, manipulating, visualizing, and using data to tell a story.
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.