Computational Thinking

Syllabus

1. Module 1

   Problem Formulation

   • From Informal Needs to Precise Problems
   • Inputs, Outputs, Constraints, and Assumptions
   • Examples, Counterexamples, and Edge Cases
   • Defining Success and Failure Conditions

2. Module 2

   Decomposition

   • Breaking Systems into Subproblems
   • Dependency Structure and Ordering
   • Top-Down and Bottom-Up Decomposition
   • Interfaces Between Subproblems

3. Module 3

   Abstraction and Representation

   • Choosing the Right Level of Detail
   • Data Abstraction and Representation Barriers
   • Representing State and Change
   • Models, Maps, and Useful Simplifications

4. Module 4

   Algorithms and Pseudocode

   • Properties of an Algorithm
   • Writing Unambiguous Pseudocode
   • Sequence, Selection, and Iteration
   • Tracing Algorithms by Hand

5. Module 5

   Patterns of Computation

   • Accumulation and Reduction
   • Search and Filtering
   • Transformation and Mapping
   • Divide, Classify, and Recombine

6. Module 6

   Reasoning About Solutions

   • Correctness Before Efficiency
   • Invariants and Progress Measures
   • Complexity as Growth
   • Trade-Offs Between Time, Space, and Simplicity

7. Module 7

   Debugging Thought Processes

   • Hypotheses and Controlled Experiments
   • Localizing a Fault
   • Distinguishing Symptoms from Causes
   • Using Minimal Reproductions

8. Module 8

   Computing in Context

   • Automation and Human Judgment
   • Bias in Models and Data
   • Privacy, Safety, and Failure Impact
   • Communicating Computational Decisions