CS 479/579

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Course Information

  • Title: Data Science
  • Class number: CS 479/579
  • Class length: 3 credits
  • Class time: TBD at each semester
  • Class location: TBD at each semester
  • Instructor: Xiaogang Ma, Assistant Professor at CS Dept.
  • Instructor contact: Office: JEB #337; Email: max@uidaho.edu; Office phone: 208.885.1547

Synopsis

Data science is advancing the conduct of science in individual and collaborative works. Data science combines aspects of data management, library science, computer science, and physical science using supporting cyberinfrastructure and information technology. Key methodologies in application areas based on real research experience are taught to build a skill-set that enables students to handle each stage in a data lifecycle, from data collection, analysis, archiving, to data discovery, access and reuse.

Course Outline and Description

Learning Objectives:

  1. Understand the fundamental concepts in data science, such as data, information, knowledge, metadata, data lifecycle, data management and data analysis, as well as the inter-relationships among those concepts. Through independent learning and collaborative discussion with classmates from different disciplines, student will attain an integrative overview of data science.
  2. Develop and Demonstrate skills for steps in a data lifecycle, including data collection, management, analysis and product generation. Students will learn and improve their skills with real world examples and work in groups of classmates from multi-disciplinary backgrounds.
  3. Be proficient in the publication and communication of data and information products. Students will learn state-of-the-art technologies in data visualization and work in groups to prepare and present the outputs of course projects.
  4. Understand the diversity and ethics in the conduction of data science. Students will discuss and learn ethical concepts such as neutral perspective, privacy, intellectual property, accountability and responsibility in data science. They will also apply those ethical guidelines in their course projects.
  5. Apply principles of respect in collaboration and interaction with others in course study, group discussion, and course projects.

Course Schedule

  1. [Week 1] Basic concepts in data science
  2. [Week 2] Data collection, stewardship and preservation
  3. [Week 3] Data formats and standards
  4. [Week 4] Class exercise - data collection (individual)
  5. [Week 5] Open Data and the Web of Data
  6. [Week 6] Class presentation - present your data (individual)
  7. [Week 7] Data science process and exploratory data analysis
  8. [Week 8] Algorithms and class exercise - group course project definitions
  9. [Week 9] Interdisciplinary data science: Examples
  10. [Week 10] Data mining
  11. [Week 11] Data quality, uncertainty, and bias
  12. [Week 12] Class presentation - group course project progress report
  13. [Week 13] Data visualization
  14. [Week 14] Data workflow
  15. [Week 15] The data ecosystem
  16. [Week 16] Final group project presentations

Assessment Method

Learning Activities:

  • Attendance and participation in lectures
  • Reading and writing assignments
  • Class exercises and group discussion/presentation
  • Course project

Grading Criteria

The grading (A/B/C/D/F) is based on the student learning activities.

  • 20% Attendance
  • 35% Class exercise and assignment
  • 45% Class project report and presentation