AP Computer Science Principles 48 Flashcards Intermediate 100% Free

AP Computer Science Principles:: Data

Created by Chat Robotics Community  ·  Updated 2025-05-09

Curriculum Overview

Comprehensive, high-yield AP Computer Science Principles study deck focusing on Data. Features 50 rigorous, curriculum-aligned flashcards designed for intermediate-level mastery. Core concepts covered include Data, key problem-solving heuristics, foundational formulas, and exam-tested application scenarios. Ideal for active recall review, spaced repetition study, and scoring in the top percentile.

Topics & Key Concepts

Data Each This LOSSY Public Science Computer Metadata Symmetric ENCRYPTION

Sample Flashcard Questions & Answers

Showing 8 of 48 cards
Question #1 Active Recall

Who is Kristen Altenburger?

Answer & Explanation:
Kristen Altenburger is a Research Scientist at Facebook focused on statistical methods for characterizing social structures and data privacy.
Question #2 Active Recall

What is Sam Pepose's role at Facebook?

Answer & Explanation:
Sam Pepose is an Applied Research Scientist on the Portal AI team, working on computer vision and mobile-optimized machine learning.
Question #3 Active Recall

What are common pitfalls in data wrangling?

Answer & Explanation:
Common pitfalls include reversed assignment recoding and variability in replicability of social science experiments.
Question #4 Active Recall

What are the steps in the data wrangling process?

Answer & Explanation:
1. Define the population of interest. 2. Evaluate representative sample. 3. Cross-validation. 4. Learn model. 5. Evaluate model.
Question #5 Active Recall

What is simple random sampling?

Answer & Explanation:
Every observation from the population has an equal chance of being selected.
Question #6 Active Recall

What is stratified random sampling?

Answer & Explanation:
The population is divided into groups, and a simple random sampling is applied within each group.
Question #7 Active Recall

What is cross-validation?

Answer & Explanation:
It's a technique to assess how the results of a statistical analysis will generalize to an independent dataset.
Question #8 Active Recall

Why use cross-validation in data analysis?

Answer & Explanation:
To avoid overfitting and ensure the model performs well on unseen data.

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