Study Guid

Study Guide for AP CSP

• 3 min read

CSP Lesson Notes

1. Beneficial & Harmful Effects of Computing

  • Beneficial Examples
    • Automated telephone trees: cuts hold times, saves businesses money
    • Medical tech: MRI, DNA sequencing → faster diagnoses, vaccine development
    • Access to education: online encyclopedias, e‑books, search engines
  • Harmful Examples
    • Cyberbullying & scams: kids exposed to grooming or fraud
    • Social media: mental‑health impacts, risky “challenges” (e.g. blackout challenge)
    • Autonomous systems dilemmas: trolley‑problem trade‑offs in self‑driving cars
  • Neutral/Debatable
    • AI: boosts productivity & data analysis vs. carbon footprint, academic dishonesty, deepfakes
    • Drones/UAVs: search‑and‑rescue & agriculture vs. privacy invasion, crashes, military abuse
    • Gene editing: cures genetic diseases & improves crops vs. ethical/genetic risks

2. The Digital Divide

  • Why it exists: economic, geographic, societal factors
  • Impacts:
    • Education: online assignments vs. lower performance
    • Employment: remote‑work opportunities vs. reduced job access
    • Healthcare: telemedicine vs. poorer outcomes
    • Civic engagement: e‑government vs. isolation
  • Bridging solutions:
    1. Infrastructure in underserved areas
    2. Subsidized devices & internet
    3. Inclusive tech design
    4. Policy for digital equity

3. Computing Bias

  • What is it? algorithms or data that unfairly favor/disadvantage groups
  • Sources
    • Data bias: unrepresentative or flawed training data
    • Algorithmic bias: faulty system logic
    • Cognitive bias: human prejudice in data labeling
  • Types
    • Explicit data (user‑provided) vs. Implicit data (inferred behavior)
    • Intentional (deliberate unfairness) vs. Unintentional (oversights)
  • Mitigation phases
    1. Pre‑processing: diversify and clean data
    2. In‑processing: adjust during training (e.g. synthetic samples)
    3. Post‑processing: monitor and correct outputs

4. Crowdsourcing & Distributed Computing

  • Crowdsourcing types
    • Crowdfunding (Kickstarter)
    • Crowd creation (Wikipedia, Threadless)
    • Crowd voting (Reddit upvotes)
    • Crowd wisdom (prediction markets)
  • Data crowdsourcing: Wikipedia edits, traffic reports (Waze), Mechanical Turk
  • Distributed computing examples: SETI@home, Bitcoin mining, cloud services (AWS)
  • Benefits & challenges: diversity & scale vs. quality control, privacy, coordination

5. Legal & Ethical Concerns in CS

  • Intellectual Property
    • Copyright, patents, trademarks, trade secrets
License Permissions Restrictions Best Use
MIT Free to use, modify, distribute (with credit) No liability/warranty Great for small projects, web tools
Apache 2.0 Same as MIT + patent protection Must include license/notice Corporate/open-source APIs
GPL (General Public License) Free use, but derivatives must stay open-source Cannot make proprietary forks Nonprofits, community software
BSD 3-Clause Use freely (even in closed-source), must not misrepresent authorship No warranty Academic tools, closed-source apps
Creative Commons (CC0, CC-BY) Mostly for non-code works (e.g., docs, images) Some versions require attribution Artwork, documentation
  • Ethical issues
    • Plagiarism vs. proper attribution
    • Commercial exploitation of open‑source
    • Respecting author intent & license terms

6. Safe Computing

  • PII (Personally Identifiable Information): SSNs, emails, driver’s licenses
  • Cookies: session vs. persistent; first‑party vs. third‑party
  • Passwords: length, case, digits, specials
  • Encryption
    • Symmetric (AES) vs. Asymmetric (RSA) vs. Hashing (SHA‑256)
  • Phishing: email, website spoofing, smishing; always verify URLs & senders
  • Verification: MFA, digital signatures, CAPTCHA

7. Binary Search Algorithm

  • Binary vs. Linear Search
    • Requires sorted list; cuts search space in half → O(log n)
    • Linear is O(n), but works unsorted
  • Steps
    1. Compare target to middle element
    2. Recurse on left or right half
    3. Stop when found or bounds cross
  • Real‑world uses: dictionaries, databases, search indices

8. Lists & Filtering Algorithms

  • Lists: ordered, mutable collections (e.g. shopping cart, email inbox)
  • Common operations: append, insert, remove, pop, slice, sort, reverse, len
  • Traversal: for item in list: → process each element
  • Filtering: loop + condition → build new list of matches (e.g. even numbers)
  • Applications: database query filters, search‑as‑you‑type

9. Random Number Generation

  • Purpose: simulations, games, security, testing
  • Function: random(a, b) → uniform integer in [a, b]
  • Use cases: cryptography (non‑predictable), Monte Carlo methods, randomized algorithms

10. Big O & Algorithmic Efficiency

  • Why Efficiency Matters
    • Faster user experiences, lower resource use, and better scalability.
    • Crucial on mobile or high‑load servers; every ms and MB counts.
  • Big O Notation
    • Describes how time/space grows with input size.
    • Focuses on worst‑case, ignores constants (e.g., O(n + 5) → O(n)).
    • Common classes:
      • O(1): constant (e.g., array lookup)
      • O(log n): logarithmic (binary search)
      • O(n): linear (simple loops)
      • O(n log n): linearithmic (merge sort, quicksort)
      • O(n²): quadratic (bubble sort)