Data
Data is represented information, observations, measurements, or other values that can be stored, processed, transmitted, analyzed, or interpreted.
Data for All
Information represented in a form that can be collected, stored, processed, or communicated.
- How it is used
- Uses everyday data creation to examine how organizations collect information, target people, and shape choices about control.
- How deep it goes
- Traces data from daily actions through storage, combination, and use, without detailing technical implementation.
- Examples
- Uses mobile apps, online shopping, photographs, location data, and pandemic-era prediction failures.
- What you can do after
- Practice/perform more deliberate sharing by limiting unnecessary disclosures and considering requests to review, manage, or delete data.
Grokking Simplicity
An inert representation of information that requires interpretation to acquire meaning and can be stored or transmitted.
- How it is used
- Separates inert facts from calculations and actions while analyzing problems, writing code, and reading existing code.
- How deep it goes
- Defines data, contrasts it with executable code, and applies the distinction to records, messages, shopping, and email workflows.
- Examples
- Uses receipts, messages, inventories, customer orders, recipes, coupon records, subscriber records, and planned emails.
- What you can do after
- Evaluate code by classifying facts as data and represent them with simple JavaScript values such as objects, arrays, and strings.
Data Storytelling with Altair and AI
Data is a collection of recorded observations, measurements, or other representations of facts before interpretation.
- How it is used
- Uses data as the raw starting point for building audience-focused data stories through the DIKW pyramid.
- How deep it goes
- Defines data as DIKW’s base and develops filtering, percentages, trends, and insight extraction without treating data engineering internals.
- Examples
- Uses pet participation counts, athletic training records, salmon aquaculture exports, and inline Vega-Lite values as data artifacts.
- What you can do after
- Apply it by selecting relevant data, extracting an audience-relevant insight, and progressing it toward a call to action.
Read how Data Storytelling with Altair and AI explains data →
The Art of AI Product Development
Information used as input for training, fine-tuning, evaluating, or operating an AI product.
- How it is used
- Treats data as the raw material connecting AI models to user and business value.
- How deep it goes
- Maps data modalities and labeled versus unlabeled data, without descending into collection or model-training implementation details.
- Examples
- Uses question-to-SQL pairs, movie-review sentiment data, model comparisons, and a conversational financial-data assistant.
- What you can do after
- Evaluate data suitability against user needs, task requirements, quality risks, governance constraints, and model alternatives.
Grokking AI Algorithms, Second Edition
Data is recorded information, observations, measurements, or signals that can serve as inputs for processing and action.
- How it is used
- Supplies inputs for AI decisions and machine-learning patterns, with suitability and representation affecting results.
- How deep it goes
- Defines data types, distinguishes data from information, and follows collection and preparation without detailing storage internals.
- Examples
- Uses patient temperature, apartment rents, and a diamond table to show interpretation, patterns, and features.
- What you can do after
- Practice/perform basic data selection, interpretation, and preparation for a machine-learning question.
Read how Grokking AI Algorithms, Second Edition explains data →
Grokking Statistics
Data is a collection of recorded observations, measurements, or other information.
- How it is used
- Frames data as the foundation for statistical description, inference, services, predictions, and alerts.
- How deep it goes
- Defines data, distinguishes structured from unstructured forms, and explains limits of inference without developing collection methods or formal models.
- Examples
- Uses digital interactions, photographs, temperatures, exam scores, documents, audio, images, and tabular datasets as examples.
- What you can do after
- Evaluate data as evidence by identifying its form, source, quality, and limits before interpreting patterns or building models.
Get Programming: Learn to code with Python
Data is information represented in a form that can be stored, processed, analyzed, or transmitted by a computer.
- How it is used
- Uses data as program input that can be stored, cleaned, and analyzed instead of entered one item at a time.
- How deep it goes
- Explains a file-based workflow from reading lines through cleanup and analysis, while stopping short of detailed data formats or advanced analysis.
- Examples
- Uses friends' contact records, spreadsheets, weather data, election data, and name-mashup inputs as concrete examples.
- What you can do after
- Apply it via Python to read structured files, store values, clean entries, and perform simple analyses.
Read how Get Programming: Learn to code with Python explains data →
Data Without Labels
Data are recorded facts, measurements, observations, or other representations of phenomena that can be analyzed.
- How it is used
- Supports analysis, machine learning, and business decision-making after appropriate cleaning, organization, and interpretation.
- How deep it goes
- Defines data, classifications, management, quality attributes, and transformation into insight, while deferring detailed analytical methods.
- Examples
- Uses mobile calls, banking, online activity, telecom churn, manufacturing defects, text, images, and structured datasets as examples.
- What you can do after
- Evaluate data quality and structure, prepare raw data, and interpret relationships as actionable business insights.
Related concepts
- machine learningused by · Machine learning is a field of computational methods in which systems learn patterns or relationships from data to make predictions, decisions, or other outputs.
- unlabeled dataincludes · Unlabeled data consists of examples that lack target labels or task-specific annotations.
- labeled dataincludes · Labeled data is a set of examples or observations paired with target values, categories, or other task-relevant annotations.
- data analysisused by · Data analysis is the systematic examination, transformation, and interpretation of data to derive information, identify patterns, or support conclusions and decisions.
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