AP CSP makes a clear distinction between data and information:

• Data – raw, unprocessed facts (numbers, clicks, temperatures, survey answers)
• Information – data that has been processed, organized, or interpreted to be useful

For example, a table of temperatures collected every hour is data. A graph showing average temperature per day, and a conclusion about a heat wave, is information.

Data can be collected from many sources:

• Surveys & forms
• Sensors (GPS, temperature, accelerometers)
• Web clicks & user interactions
• APIs and external data sets

Data Cleaning

Real-world data is often messy. Cleaning data may include:

• Removing duplicate records
• Handling missing values
• Fixing obvious errors or outliers
• Standardizing formats (dates, units, categories)

Visual representations of data — charts, graphs, maps — help us find patterns, trends, and outliers that might be hidden in raw tables.

Common visualization types:

• Line graph – changes over time
• Bar chart – comparing categories
• Pie chart – parts of a whole
• Scatter plot – relationships between two variables
• Heat maps – density/intensity across an area

Exam themes:

• Which visualization best supports a claim?
• What conclusion can be drawn from this graph?
• Is the data representation misleading?

Big data refers to datasets that are too large or complex to be processed on a single machine using traditional tools.

Examples:

• Millions of GPS locations from phones
• Click data from a major website
• Medical records from hospitals nationwide
• Social media posts over many years

What big data enables:

• Detecting trends and patterns
• Training machine learning models
• Personalized recommendations (music, shopping)
• Forecasting traffic, weather, or disease spread

A simulation is a computer model that imitates a real-world process or system. Instead of experimenting on the real thing (which might be expensive, dangerous, or impossible), we experiment on the model.

Examples of simulations:

• Weather forecasting
• Disease spread in a population
• Traffic flow in a city
• Stock market behavior
• Physics engines in games

Why use simulations?

• They are cheaper and safer than real-world experiments
• They allow many “what if” scenarios
• They can run faster than real time

Both data and simulations have limitations. The AP exam loves questions about what you can and cannot conclude from data.

Common limitations:

• Sampling bias – data is collected from an unrepresentative group
• Measurement error – tools or methods are inaccurate
• Outdated data – conditions have changed over time
• Overfitting – model matches past data too closely and fails on new data

Bias examples:

• A survey only sent to people online
• A dataset missing certain groups of people
• Algorithms that reflect historical inequalities

Computers are great at performing repetitive operations on large datasets. Common operations include:

• Aggregation – computing summaries, such as sums, counts, averages, minimums, and maximums
• Filtering – keeping only data that meets a condition (e.g. “only students with GPA ≥ 3.5”)
• Clustering – grouping similar data points together
• Classifying – assigning data to categories or labels

These operations turn raw data into useful information, which is exactly what the AP exam focuses on.

Question 1

Why is data cleaning important before analyzing a dataset?

• A. It always increases the amount of data collected.
• B. It ensures visualizations automatically generate accurate predictions.
• C. Dirty or inconsistent data may lead to incorrect or misleading conclusions.
• D. It prevents the data from being stored in large datasets.

Correct Answer: C
Cleaning removes inconsistencies, errors, and duplicates that can distort interpretations.

Question 2

Which type of visualization is best for showing how a variable changes over time?

• A. Bar chart
• B. Line graph
• C. Pie chart
• D. Scatter plot

Correct Answer: B
Line graphs are specifically designed to display trends across time.

Question 3

Which scenario best represents the use of “big data”?

• A. A teacher records grades for 30 students.
• B. A single user logs their daily step count.
• C. A company analyzes millions of customer transactions per day.
• D. A scientist manually compares two datasets.

Correct Answer: C
Big data refers to extremely large datasets requiring distributed processing.

Question 4

What is a major limitation of using simulations to represent complex real-world systems?

• A. Simulations always run slower than the real world.
• B. Simulations require human participants to operate.
• C. Simulations may omit important variables, reducing how accurately they reflect reality.
• D. Simulations cannot be run more than once.

Correct Answer: C
Models simplify the world and may exclude important data, which limits accuracy.

Question 5

An analyst removes duplicate entries and standardizes all date formats in a dataset. What operation is this?

• A. Aggregation
• B. Data cleaning
• C. Simulation
• D. Visualization

Correct Answer: B
Data cleaning fixes errors and inconsistencies before analysis.