AP® Computer Science Principles Course Outline

Diverse perspectives make better computing innovations.

Pair programming, feedback and shared tools.

Communication, consensus and resolving conflict.

What problem an innovation solves, and for whom.

Describing what code segments do.

User actions, sensors, files and events.

Visual, audio, text and stored results.

Investigate, design, prototype, test and refine.

Requirements, user needs and interface design.

Comments and program documentation.

Acknowledging code you didn't write.

Logic, syntax, run-time and overflow errors.

Choosing test cases, including edge cases.

Bits, bytes and abstraction.

Overflow, rounding and analog vs. digital.

Place value in base 2 and base 10.

Choosing the right compression for the job.

Finding patterns, trends and correlations.

Data about data, and what it reveals.

Messy, incomplete, biased and huge data sets.

Filtering, sorting, combining and visualizing data.

Using programs to answer questions and test ideas.

Naming and storing values.

Tracing how values change.

Ordered collections and indexes starting at 1.

Using lists to manage complexity.

Sequencing, selection and iteration.

Evaluating code statements in sequence.

+, −, *, / and MOD.

Concatenation, substrings and length.

Comparing values to get true or false.

NOT, AND and OR.

Choosing a path based on a condition.

Writing and tracing conditional code.

Decisions inside decisions.

Repeating steps until a condition is met.

Writing and tracing loops.

Different code, same result.

Min, max, sum, average and combining algorithms.

Access, assign, insert, append, remove and length.

FOR EACH loops and linear search.

Halving a sorted list and counting iterations.

Arguments, parameters and return values.

Hiding detail to manage complexity.

Parameters, RETURN and reuse.

Using existing code and its documentation.

RANDOM(a, b) and possible outcomes.

Modeling the real world, and its limits.

Reasonable vs. unreasonable time, and heuristics.

Problems no algorithm can always solve.

Devices, paths, bandwidth and routing.

IP, TCP, UDP and open standards.

How data is broken up and reassembled.

HTTP and how the web runs on the internet.

Why redundant paths keep the internet running.

Calculating run times and speedup.

Why speedup has limits.

One innovation, many effects.

Effects beyond an innovation's purpose.

Unequal access to computing and the internet.

How bias enters data and algorithms, and how to reduce it.

Solving problems at scale with many contributors.

Intellectual property, licenses and open source.

PII, tracking and how data is collected.

Authentication, MFA, encryption and safe habits.

Phishing, malware, keylogging and rogue access points.