Dec 26, 2024  
2022-2023 Course Catalog 
    
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2022-2023 Course Catalog [ARCHIVED CATALOG]

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CSCI 1000 - Computational Thinking and Problem Solving

Credits: 4
Hours/Week: Lecture 4
Course Description: Computational thinking is an emerging skill in the 21st century. This course helps students develop that skill. The course introduces the basic mathematical principles underlying computational thinking. Course topics include data representation, communication, and processing and how they affect computation. Course activities engage students in analyzing a problem, developing an effective algorithm, and then collaboratively applying appropriate techniques and resources to devise a solution in light of societal, economic, and ethical issues.
MnTC Goals
None

Prerequisite(s): Course placement into MATH 0070  or higher, or completion of MATH 0030  with a grade of C or higher
Corequisite(s): None
Recommendation: Basic ability to operate a computer and use the Internet.

Major Content
  1. Computational design and development methods
  2. Collaborative methods in software design and development
  3. Data representation, storage, and use
    1. Numeric
    2. Graphics
    3. Sound
  4. Algorithms and programming
    1. Develop and implement algorithms
    2. Develop programs that incorporate abstractions
    3. Evaluate and test algorithms
  5. Networks and computing systems
    1. Cloud
    2. Mobile
    3. Local
  6. Collaborative and ethical computing culture
  7. Impact of computing
    1. social
    2. ethical
    3. privacy
    4. automation and digitization

Learning Outcomes
At the end of this course students will be able to:
  1. select the appropriate development approach for a given problem.
  2. work collaboratively in developing a computational solution.
  3. choose the appropriate data type to represent a particular information source.
  4. describe how data is stored, transformed, and used.
  5. apply the appropriate computational and mathematical algorithms to a given problem.
  6. implement algorithms using basic programming constructs and mathematical formulas.
  7. analyze algorithms for mathematical complexity in time and space.
  8. compare different data communication technologies.
  9. describe different computational systems (e.g., cloud, mobile, local) and technologies (e.g. AI, data science, quantum computing).
  10. describe the best personal and societal practices for dealing with critical privacy, security, and ethical issues.

Competency 1 (1-6)
None
Competency 2 (7-10)
None


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