Data, Information, Knowledge, and Wisdom

• Data are raw, unprocessed facts lacking inherent meaning, making them not very useful on their own.
• Information is structured and contextualized data, making it more useful.
• Knowledge is information that is meaningful and can be applied effectively.
• Wisdom is the most applied form of information, providing the greatest insight.

Data About Data

• Data types define the format and limitations of data.

Good Information

• Good information is accurate, meaning it is correct, complete, and unbiased.
• Good information is timely, meaning it is produced in time for its intended purpose.
• Good information is relevant, meaning it has context relevance (purpose) and subject relevancy (audience).
• Good information should be just barely sufficient, providing enough detail for the purpose without being overwhelming.
• Good information should be worth its cost, with the value exceeding the expected expenses.

Computational Thinking

• Computational thinking involves breaking down complex problems, identifying patterns, abstracting details, and creating algorithms to find solutions.
• Computational thinking enables the reproducibility of similar experiments.
• Computational thinking provides a way to think about transforming inputs into outputs.
• Computational thinking involves understanding a complex problem and developing potential solutions.

Stages of Computational Thinking

• Decomposition involves breaking down a complex problem into more manageable sub-components.
• Pattern Recognition involves identifying patterns within the problem domain.
• Pattern Generalization (Abstraction) involves focusing only on essential information to generalize the problem.
• Algorithm Development involves creating a step-by-step guide to solve the problem which can be abstract or specific.
• Testing involves applying the algorithm to different cases and comparing outcomes with the desired results.

Computer Storage

• Computers use binary language (0s and 1s) to store data.

File Formats

• Proprietary file formats require specific software to access the data and may have limited compatibility.
• Non-proprietary file formats do not require specific software and maintain consistent formatting across different platforms.

File Structure

• An absolute path specifies a file's location from the root directory, using /.
• A relative path specifies a file's location based on the current working directory, using ../ and /.

Command Line Infrastructure

• Command line infrastructure involves using a text-based interface to execute commands, manage files, and control system processes.

Logical Operations

• Boolean is a data type with TRUE or FALSE values.
• The order of logical operations is NOT, AND, then OR.

Reproducibility

• Reproducibility refers to the ability to reproduce data or information.
• Reproducibility is useful for reporting problems and finding their root cause.

Reporting Problems

• Problem reports should be concise and clearly describe the issue.
• Reports should include where the problem occurred, how it occurred, and any troubleshooting steps taken.

Scientific Research

• Reproducibility in scientific research allows others to conduct research with independent data.
• Reproducibility allows reimplementation based on provided reports.
• Reproducibility allows running provided code and proves the validity of claims.
• Reproducibility improves trust and transparency in research.
• Barriers to reproducibility include complexity, technological change, human error, and intellectual property rights.

Variables

• Variables are containers that store specific data, either as a single value or a set of values.

Classification of data

• Scalar variables store a single value.
• Vector variables store values of the same data type in a one-dimensional array.
• Matrix variables store values of the same data type in rows and columns (two-dimensional array).
• Array variables store values of the same data type in multi-dimensional structures.
• Data frame variables store values of different data types, similar to a table.

Data Exploration

• Data exploration is the process of analyzing data to understand its patterns, structure, and relationships.
• Data exploration identifies patterns and trends, detects missing or incorrect data, and helps decide on the best analysis techniques.

Chart Types

• Pie charts show proportions.
• Box plots show distribution, including median, range, and outliers and enable comparison between categories.
• Scatter plots show patterns of correlation.
• Histograms show distribution based on bins.
• Column/bar charts show distribution based on category and subcategories.
• Line charts show trends over time.