UndergradSkiller: Skill Set Map for Undergraduates and Their Educators
CRITICAL THINKING AND PROBLEM-SOLVING: Data Literacy
Computational Thinking
Computational thinking involves breaking down complex problems into manageable parts, identifying patterns, and designing step-by-step solutions. These skills are valuable well beyond computer science.
Useful Resources on Computational Thinking:
The Open University. (2019, April 9). Introduction to computational thinking. https://www.open.edu/openlearn/science-maths-technology/introduction-computational-thinking
Annotation: Free self-paced course from The Open University introducing the core concepts of computational thinking, including algorithms, abstraction (modeling and encapsulation), and applications across disciplines such as biology, physics, economics, and sport science. Includes a glossary and activities.
Grimson, E., Guttag, J., & Bell, A. (2016). Introduction to computational thinking and data science. MIT OpenCourseWare. https://ocw.mit.edu/courses/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/
Annotation: Full undergraduate course from MIT covering computational thinking and data science using Python. Topics include optimization, stochastic thinking, Monte Carlo simulation, clustering, and machine learning. Includes lecture videos, lecture slides, problem sets, and programming assignments. Designed for students with little or no programming experience.
Peer Review: None
Table of contents:
- 3.1. Critical Thinking
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3.1.1. Introductions to Critical Thinking
3.1.2. Developing Thinking Skills
- 3.4. Critical Reasoning
- 3.5. Synthesis
- 3.6. Problem-Solving and Design Thinking
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3.6.1. Problem-Solving
3.6.2. Design Thinking
- 3.7. Research Methods
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3.7.1. Research Design
3.7.2. Research Ethics
- 3.8. Data Literacy
- 3.8.1. Understanding Data
3.8.2. Computational Thinking
- 3.9. Curiosity and Creative Thinking
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3.9.1. Creative Exercises
3.9.2. Understanding Curiosity
Author: Leeanne Irwin
Peer Reviewer(s): None