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Recognize that data can exist as quantitative, ordinal, categorical, and other values. Data also can be “nontraditional” forms such as graphical or other media.

Recognize that data represent decisions about measurement and inclusion involving people who are and are not immediately present.

Recognize that variability is a foundational component of data.

Recognize that data captures certain aspects of a model of a target phenomenon or set of objects in the world but does not represent it completely.

Recognize that data “fuels” AI, that AI can be compared to a function machine (math), algorithm (CS), or a prediction model (statistics) that relies on data to both operate and improve itself, and that AI tools can also be used to analyze complex data in research.

Recognize that data can pose risks but also benefits for individuals and groups, and understand its potential uses, limitations, and risks, including unintended consequences.

Recognize all data contains bias but data collection and analysis methods can increase or mitigate the effects of biases.

Recognize data empowers discovery, decision-making, and advocacy across fields.

Recognize that making sense with data requires engaging with it in a particular way that includes combinations of the concepts and practices in the other four strands.

Recognize that the investigative process is not linear but cyclic and iterative, with many of the phases repeating and looping back.

Recognize that inferences from data are dynamic, evolving with new data and additional analysis.

Recognize that the context surrounding the data and the investigation shapes interpretation. Many fields (biology vs. psychology; economics vs. sociology) have created very different frameworks to organize problems. Considering multiple approaches may reveal useful insights from the same data.

Cultivate the motivation to engage with data in all areas of life and understand how data impacts your own experiences.

Identify and address data quality issues to ensure accuracy and reliability, progressing from simple error identification to using systematic approaches.

Organize raw data into structured formats using categories, tables, and systematic recording methods.

Transform and manipulate data through sorting, grouping, filtering, and combining datasets.

Calculate and analyze group-level statistics from detailed data to reveal patterns and relationships.

Identify problems and formulate questions that guide meaningful data collection and analysis.

Explore various ways to generate data through simulations, sensors, and automated collection methods.

Develop systematic plans that specify what data to collect, how to collect it, and from what sources to answer investigation questions.

Explore, locate, evaluate, and retrieve datasets collected by others to address research questions and data investigations.

Collect, measure, and document data accurately using appropriate tools and methods.

Evaluate and interpret others' datasets by examining collection methods, context, and quality.

Collect and use data ethically, considering privacy, fairness, and potential impacts.

Work with datasets at increasing levels of cleanliness and identify how datasets need to be curated to address messiness issues.

Explore datasets containing various types of data and understand how each type serves different analytical purposes.

Work with datasets of increasing size in both number of observations and variables and arrange data in increasingly complex formats to facilitate meaningful analysis.

Manipulate and combine data in increasingly complex ways to reveal new insights and patterns.

Analyze large datasets by measuring their central tendency while considering the context and distribution of the data.

Examine dataset variability by applying measures of spread to identify and quantify outliers.

Identify the distribution of data points, including clusters, gaps, symmetry, skewness, and modes. Use these patterns to understand data spread and their impact on measures like the mean and median.

Organize data into frequency tables based on shared characteristics. Summarize data using counts, fractions, relative frequencies, or proportions to enable comparisons and generalizations. Understand the implications of choices made when creating and interpreting frequency tables.

Identify and describe missing data numerically and categorically. Distinguish between missing values and true zeros. Understand how missing data impacts relationships, patterns, and models in data interpretation.

Recognize metadata as information about data, including its source, type, and structure. Use metadata to organize, summarize, and analyze data effectively, supporting interpretation and decision-making.

Identify similarities and differences between variables and explore potential associations. Use distributions, numerical summaries, and simulations to compare groups based on numerical or categorical data.

Represent data visually and numerically to describe how outcomes occur and compare groups. Use variability to interpret distribution shape, support statistical reasoning, and assess population estimates.

Organize, visualize, and analyze data to identify patterns, trends, and associations. Use statistical measures and graphs to interpret relationships and make predictions.

Examine data beyond numbers, including sounds, textures, and text. Categorize sensory inputs, track word frequencies, and analyze data from sensors and IoT devices to identify patterns and trends.

Use data to build decision trees, explore classification and clustering, and understand how machine learning optimizes predictions through algorithms like gradient descent.

Identify differences within data by sorting, grouping, and organizing characteristics. Use statistical and simulation methods to represent and analyze variability, connecting it to real-world uncertainty and probabilistic processes.

Examine differences between groups by analyzing measures of spread, such as range and standard deviation. Utilize visualizations like box plots and apply statistical methods, including mean, median, and standard deviation, to compare datasets, assess variability, and uncover patterns in data distributions and models.

Recognize measurement errors and natural variability in data. Assess data quality, identify outliers, and refine models using statistical and contextual analysis.

Explore how AI model outputs vary based on training data, labeling, and bias. Understand how generative AI and pre-trained models use large datasets to make inferences and how variability in data impacts outcomes.

Use digital tools to summarize data and create visualizations. Apply these tools to identify patterns, clean and prepare data, perform analysis, and build models for simulations to explore relationships and trends.

Examine how digital tools influence access, privacy, and bias, shaping opportunities and challenges in technology use. Consider the broader ethical and societal impacts of AI, including its role in decision-making, accountability, and policy.

Assess the technical limitations of digital tools and compare no-code, low-code, and high-code solutions based on their capabilities and use cases.

Choose the appropriate no-code, low-code, or high-code digital tool based on the task. Use multiple tools throughout the data investigation process and explore how digital tools are applied in the workforce.

Explore how block coding and computer code automate and enhance data analysis. Understand how coding enables reproducible processes and compare its advantages and limitations to no-code and low-code tools.

Understand how digital tools can support a broad range of diverse learners. Evaluate their effectiveness and impact, and explore inclusive data representations.

Analyze patterns and relationships in data using graphs, tables, and models. Explore tools like decision trees and neural networks, assess assumptions, and distinguish correlation from causation in real-world contexts.

Develop an understanding of patterns and relationships. Use data and technology to build and refine models. Advance these skills by constructing complex models that incorporate multiple variables, assess assumptions, and improve predictions.

When communicating with others, employ both plain-language and clear vocabulary to regularly describe degrees of uncertainty, both formally and informally as a thinking habit.

When encountering new data, integrate probabilistic thinking into everyday situations by explicating prior assumptions and the impact of new data / evidence on those assumptions.

When making a decision about uncertain outcomes in the future, integrate probabilistic thinking into everyday decisions by applying expected value (magnitude x probability) to appropriate situations.

Clearly describe the basic logic of statistical significance to others, differentiating between significance, the size of an effect, and the statistical power of an analysis. Recognize what statistical significance can reveal and cannot reveal about a phenomenon.

Comfortably identify the purpose of sampling and simulation for making arguments about data, and employ techniques using software to differentiate a real-data result from random chance or “happenstance.”

Comfortably separate correlation from causation in a wide variety of situations, building a “first-reaction” thinking habit over time.

When identifying a potential cause of a phenomenon, clearly describe the usefulness of randomization for constructing an argument with data.

Clearly describe how to leverage additional variables or additional outside data to make a logical argument, and identify potential risks of overdoing it.

Identify and create the type of questions that can be answered by data, and are eventually verifiable using a combination of modeling and experimentation.

Regularly practice identifying alternative explanations for a result from data, both for interim steps and post-analysis conclusions.

Clearly explain the limitations and caveats of a conclusion from data, including the risks of extending the conclusion to another group or situation.

Ensure that increasingly complex analysis steps remain useful for the original question, and that the method does not distract from the problem.

Regularly identify generalization issues, with frequent comparisons between significant real-world examples and a current analysis.

Given a dataset, identify constraints and opportunities for what can be logically inferred about a broader population.

When full information is hidden or inaccessible, recognize the logical relationship between a sufficient number of chances and a sufficiently large sample to reasonably represent something.

When information is completely hidden or unavailable, be aware of possible underlying issues in the sample and apply strategies to identify and address them.

Following an initial analysis, list and implement opportunities for increasing the strength of an argument, a generalization claim, or ideas for a new analysis. Explore risks of the same approaches as well.

Recognize that important information may be hidden or may even change a major conclusion when data is filtered into categories and/or groups.

Recognize the relationship between many trials, uncertainty, and whether a claim is a “fact.”

Practice creating visualizations to summarize many things at once, relationships between things in one place, or exceedingly complex ideas in one place. Recognize that visuals can be more efficient or compelling than other forms of communication.

Create data visualizations to directly support the analysis steps of data.

Identify conventional components and best practices of data visualization from a user-centered or audience perspective.

Comfortably read graphs with accuracy and make sense of data visualizations by answering questions about how the data is represented with precision.

Identify how layout (ordering, scale, and axes) choices increase clarity or potentially mislead an audience.

Align the type of data (numeric, categorical, string, other) to a visualization type designed for that use-case.

Understand the relationship between a data visualization and its associated narrative.

Structure effective stories about data when complex jargon and technical ideas are involved.

Tailor storytelling for different audiences.

Regularly interrogate the point of view of a data author, and transparently share your own.

Recognize how data can provide evidence for/persuade others toward positive change and how it can benefit society.

Engage in civic practice and dispositions through recognition of the role data plays in civic society.

Appreciate how AI and other data-driven technology may affect people and resources globally.