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.

Free AP Computer Science Principles Practice Test

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