Using Programs with Data Chapter Notes | AP Computer Science Principles - Grade 9 PDF Download

Introduction

This chapter examines how programs process and analyze data, a critical topic in the AP Computer Science Principles course. It covers data mining, the use of programs like spreadsheets and text analysis tools, data transformation techniques, and the discoveries enabled by data analysis. Understanding these concepts is essential for leveraging data to uncover patterns and insights in various applications.

Using Programs with Data

• The demand for data processing has led to many programs designed for analyzing large data sets, a process called data mining, which uncovers patterns and relationships.
• Spreadsheet programs like Google Sheets or Microsoft Excel allow users to record, modify, organize, and perform calculations on numerical data using equations.
• Text analysis tools process text data to find patterns, categorize content, or classify it (e.g., determining the tone of writing, sorting product reviews, detecting public opinion trends, or identifying anonymous authors).
• Data processing programs can create visualizations like tables, line graphs, or bar graphs to make trends and patterns more apparent, especially in large data sets.
• Search tools, such as those in Google Images, simplify finding specific data by filtering based on criteria like color, date, with different tools tailored to the search engine’s purpose (e.g., academic journals vs. image searches).
• Some programs offer data filtering capabilities to extract subsets of data based on time (e.g., winter results), value (e.g., values below 30), or other criteria (e.g., positive values only).

Transforming Data

• Data transformation involves editing or modifying data to extract additional information.
• Examples of data transformation:
  • Modifying every element in a data set, such as multiplying numbers by a constant (e.g., converting liters to milliliters) or adding non-arithmetic data like grade levels to student records.
  • Filtering data by categories like time, value, or quality (e.g., selecting students in specific extracurricular activities).
  • Combining or comparing data, such as comparing average SAT scores across colleges in different states.
  • Creating visualizations like graphs, charts, or word clouds to represent data.
  • Data transformation is often iterative and interactive, allowing users to choose filters or subsets and process data multiple times (e.g., sorting by date, then by location).
• Manipulating data through combining, clustering, or classifying reveals new patterns and information not visible in raw data.

Data Analysis Discoveries

By analyzing data, you can discover:

• Patterns: Recurring events, such as a product selling well in a specific season or region over years.
• Trends: Consistent changes over time, like rising or falling patterns, or fluctuations in data.
• Outliers: Data points that deviate significantly from the norm, which can skew analysis if not addressed.
• Correlations: Relationships between variables, indicating potential connections for further study.