AP® Computer Science Principles Cheat Sheet
A comprehensive resource for AP Computer Science Principles. This expertly crafted cheat sheet provides a concise overview of essential topics, including algorithms, data management, and computer systems, ensuring you are fully prepared for your exams.
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Unit 1: Creative Development
Computing Tools: software, hardware, IDEs, etc.
Program Design: flowcharts, pseudocode, testing strategies
Collaboration: roles, pair programming, code review, version control
Algorithms: step-by-step procedures, sequencing, selection, iteration
Problem-Solving Process: define, plan, implement, test, reflect
Abstraction: simplifying complex problems by focusing on the main ideas
Digital Representation: binary, data compression, and data storage
Unit 2: Data
Data Types: numbers, strings, booleans, lists, etc.
Data Representation: binary, hexadecimal, text (ASCII/Unicode)
Data Collection: surveys, sensors, databases, web scraping
Data Storage: cloud, local storage, databases
Data Analysis: filtering, sorting, visualizing with graphs/charts
Big Data: large datasets, machine learning, predictive analysis
Data Security: encryption, privacy laws (GDPR, HIPAA)
Unit 3: Algorithms & Programming
Programming Languages: high-level vs. low-level, syntax, semantics
Variables: declaration, initialization, assignment, scope
Control Structures: if-else, loops (for, while), switch-case
Functions: definition, parameters, return values, recursion
Debugging: syntax errors, runtime errors, logic errors, testing
Program Efficiency: time complexity, space complexity (Big-O notation)
APIs: libraries, functions, documentation, integration
Unit 4: Computer Systems & Networks
Computer Components: CPU, memory, storage, input/output devices
Operating Systems: tasks, file management, user interface
Internet: IP, DNS, HTTP, protocols, packet-switching
Network Types: LAN, WAN, VPN, peer-to-peer, client-server
Cybersecurity: firewalls, antivirus, encryption, phishing, malware
Data Transmission: bandwidth, latency, throughput
Cloud Computing: SaaS, IaaS, PaaS, distributed computing
Unit 5: Impact of Computing
Ethics: privacy, digital divide, intellectual property, hacking
Legal Issues: copyright, patents, fair use, DMCA
Social Impacts: social media, AI ethics, job automation, bias
Global Impact: digital communication, internet access, globalization
Economic Impact: e-commerce, fintech, gig economy
Environmental Impact: e-waste, energy consumption, green computing
Future Trends: AI, quantum computing, blockchain, IoT
Unit 6: Simulation & Modeling
Simulations: virtual models, predictions, testing hypotheses
Modeling: abstract representations, simplifications of reality
Randomness: random number generation, Monte Carlo methods
Heuristics: problem-solving approaches, approximation algorithms
Data Visualization: graphs, charts, dashboards, infographics
System Modeling: input-output models, flowcharts, state diagrams
Decision Making: cost-benefit analysis, risk assessment, optimization