Study Guide

Images lesson w/ homeworks

• 5 min read

Team Teach: Core Concepts in CSP

A reimagined overview of the big ideas from our collaborative lessons—each section links to the original deep-dive resources for further exploration.

Upsides of Computing

Deep dive: Positive Impacts on Society

Summary:
Computing helps in healthcare, business, education, and the environment by using data and automation.

  • AI-powered diagnostics for faster, more accurate medical decisions
  • Automated workflows that free up human effort
  • Platforms for seamless global collaboration
  • IoT networks monitoring air, water, and wildlife health
  • Online learning systems expanding educational reach
  • Real-time analytics supporting disaster relief

Downsides of Technology

Deep dive: Unintended Consequences

Summary:
Technology can harm privacy, jobs, and the environment if not managed carefully.

  • Mass surveillance eroding individual privacy
  • Automation displacing workers in routine roles
  • Social media overuse impacting mental health
  • Growing cybercrime, phishing, and ransomware
  • Algorithmic bias producing unfair outcomes
  • Escalating e-waste and resource depletion

Bridging the Digital Gap

Deep dive: Closing the Digital Divide

Summary:
Free and light tools let people code on old or low-cost devices.

  • Cloud IDEs (Replit, Jupyter) requiring only a browser
  • Linux distributions revitalizing Chromebooks and old PCs
  • Windows Subsystem for Linux (WSL) for Unix tools on Windows
  • Cross-platform editors like VS Code and Atom
  • Offline documentation for limited-connectivity regions
  • Community-led workshops sharing practical skills

Algorithmic Bias

Deep dive: Computing Bias

Summary:
Algorithms can treat some groups unfairly if their training data isn’t balanced.

  • Training sets that underrepresent certain demographics
  • Testing only on popular hardware configurations
  • Black-box models hiding biased decision logic
  • Feedback loops that amplify existing prejudices
  • Development guidelines emphasizing fairness
  • Regular audits to detect and correct bias

Harnessing the Crowd

Deep dive: Crowdsourcing in Practice

Summary:
Many people can work on small tasks to solve big problems together.

  • Foldit gamers solving protein structures in HIV research
  • Volunteer editors building and curating Wikipedia
  • Crowdsourced labeling for machine-learning datasets
  • Competition platforms (Kaggle, Innocentive) spurring innovation
  • Microtask services like Amazon Mechanical Turk
  • Crowdfunding creative and technical projects

Law & Ethics in Code

Deep dive: Ethical and Legal Issues

Summary:
Code must respect copyrights, patents, and fair-use rules.

  • Copyright vs. patent: creative works vs. inventions
  • Open-source licenses (GPL, MIT, Apache) and their terms
  • Best practices for citing third-party code
  • Ownership concerns around AI-generated content
  • Digital Rights Management (DRM) to enforce usage rules
  • Ethical guidelines for collaborative and academic projects

Staying Secure

Deep dive: Safe Computing Essentials

Summary:
Use MFA, encryption, and regular updates to keep data and systems safe.

  • Multi-Factor Authentication (MFA) for hardened accounts
  • SSL/TLS encryption to protect data in transit
  • Regular patching of OS and applications
  • VPNs and firewalls to isolate and guard networks
  • Phishing awareness training for end users
  • Collecting only essential personal data

Binary Search Fundamentals

Deep dive: Binary Search Explained

Summary:
Binary search finds an item by splitting a sorted list in half each time.

  • Divide-and-conquer approach: compare to midpoint
  • Time complexity: O(log n)
  • Prerequisite: data must be sorted
  • Handling duplicates: adjust pointers to find first/last match
  • Safe midpoint calc: mid = low + (high - low)//2
  • Built-in support via Python’s bisect module

Data Filtering Techniques

Deep dive: Practical Filtering

Summary:
Filtering picks items that meet a condition from a list.

  • Python list comprehensions for concise filters
  • Predicate functions for complex selection logic
  • Linear time performance: O(n)
  • Chaining conditions to refine results
  • SQL WHERE and Pandas .loc[] for large datasets
  • Use cases: spam detection, data cleaning, eligibility checks

Randomized Methods

Deep dive: Random Algorithms

Summary:
Random algorithms use chance to solve problems or make fair choices.

  • Monte Carlo simulations estimating probabilities
  • Random forests building diverse model ensembles
  • Cryptographic key generation with secure randomness
  • Shuffling and sampling for unbiased selection
  • Genetic algorithms evolving solutions iteratively
  • Tools: Python’s random and secrets modules

Simulation Models

Deep dive: Simulations Unpacked

Summary:
Simulations mimic real systems so we can test without risk.

  • Engineering stress tests in ANSYS or SolidWorks
  • VR surgical simulations for healthcare training
  • Climate and environmental modeling
  • Agent-based economic forecasts
  • Physics engines powering realistic games
  • Educational demos like dice rolls and RPS

Understanding Big O

Deep dive: Complexity Concepts

Summary:
Big O shows how an algorithm’s time or space needs grow with more data.

  • O(1): constant-time operations
  • O(log n): divide-and-conquer strategies
  • O(n): single-pass loops
  • O(n log n): efficient sorting algorithms
  • O(n²+): nested loops and exhaustive searches
  • Space complexity: memory usage trends

Graphs & Heuristic Searches

Deep dive: Graphs & A* Search

Summary:
Graphs show connections, and heuristics find good paths fast.

  • Nodes (vertices) and edges (connections)
  • Directed vs. undirected, weighted vs. unweighted graphs
  • Applications: social networks, GPS routing, recommendations
  • Greedy best-first search for quick heuristics
  • A* combining cost and heuristic estimates
  • Tackling NP-hard problems with approximation

Practice Questions

  1. What are three positive societal impacts of computing? (Upsides of Computing)
Answer - AI-powered diagnostics in healthcare - Automation streamlining business processes - Environmental monitoring via IoT sensors
  1. Name two risks associated with rapid technological growth. (Downsides of Technology)
Answer - Widespread data collection eroding privacy - Automation leading to job displacement
  1. List four strategies for coding on low-budget hardware. (Bridging the Digital Gap)
Answer - Use browser-based IDEs (Replit, Jupyter) - Install lightweight Linux on older machines - Enable WSL on Windows for Unix tools - Adopt cross-platform editors like VS Code
  1. Describe one example of algorithmic bias and how to mitigate it. (Algorithmic Bias)
Answer - Facial recognition misidentifying certain groups; mitigate with diverse datasets and inclusive testing.
  1. Give two real-world applications of crowdsourcing. (Harnessing the Crowd)
Answer - Foldit protein-folding game contributions - Volunteer-edited articles on Wikipedia
  1. What are the main differences between copyright and patent protections? (Law & Ethics in Code)
Answer - **Copyright:** Covers creative works like code and media - **Patent:** Protects inventions and technical processes
  1. Explain why MFA and encryption are critical for cybersecurity. (Staying Secure)
Answer - MFA adds extra verification, reducing unauthorized access - Encryption ensures data confidentiality during transmission
  1. Outline the steps of binary search on a sorted list of 16 items. (Binary Search Fundamentals)
Answer 1. Compare target with the middle element (index 7) 2. Choose left or right half (8 elements) 3. Repeat halving (8 → 4 → 2 → 1) until found or range is empty
  1. How does a list comprehension filter out even numbers? (Data Filtering Techniques)
Answer ```python [x for x in numbers if x % 2 == 0]