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LIS-S 400 Topics in Applied Data and Information Science

3 credits

  • Prerequisites: None
  • Delivery: On-Campus, Online
  • Semesters offered: Spring
    The above are the semesters this course is generally offered. View the course schedule to confirm.
  • This course covers specific topics in applied data and information science. It may be repeated for credit when the topic varies. The same course number is used for different courses.

    Learning Outcomes

    Data Literacy

    1. Distinguish between data, information, and knowledge.
    2. Recognize that data can have value and play a key role in society by providing opportunities to expand knowledge, to innovate, and to influence.
    3. Analyze datasets in context to determine data veracity including bias in data collection or representation.
    4. Assess values with respect to the use of data technologies.

    Data Science

    1. Organize, visualize, and analyze large, complex datasets using descriptive statistics and graphs to make decisions.
    2. Apply inferential statistics, predictive analytics, and data mining to informatics-related fields.
    3. Assess the purpose, benefits, and limitations of visualization as a human-centered data analysis methodology.
    4. Conceptualize and design effective visualizations for a variety of data types and analytical tasks.
    5. Identify, assess, and select appropriately among data analytics methods and models for solving real-world problems, weighing their advantages and disadvantages.
    6. Understand data science concepts, techniques, and tools to support big data analytics.

    Information Science

    1. Demonstrate an understanding of the data lifecycle, including data curation, stewardship, and long-term preservation.
    2. Apply the principles of consistency and uniformity to recognize the need for authorized terms for describing various types of data.
    3. Understand the principles of data organization including file name conventions, version control, and data documentation.
    4. Understand the characteristics of various data types generated and used by a variety of disciplines, subdisciplines, research communities, and government organizations.
    5. Understand critical issues associated with the storage, backup, and security of data.
    6. Analyze data policies to compare possible outcomes.

    Data Ethics

    1. Understand the relation between data, ethics, and society.
    2. Identify and understand the social, political, ethical, and legal aspects of data creation, access, ownership, service, and communication.
    3. Develop substantive arguments using ethical reasoning to suggest improvements to data-driven systems and practices.
    4. Differentiate between surveillance systems that promote and inhibit values.

    Other Topics

    1. Design, conduct, and write up results of research.
    2. Understand tools and techniques of project management.
    3. Understand legal and business aspects of technology and media.


    There is not a syllabus available for this course.