Standards

Programmers select and combine control structures, such as loops, event handlers, and conditionals, to create more complex program behavior. [See: Method Chaining.]

Programs use procedures to organize code, hide implementation details, and make code easier to reuse. Procedures can be repurposed in new programs. Defining parameters for procedures can generalize behavior and increase reusability. [See: Defining Functions; Defining Table Functions.]

Programmers create variables to store data values of selected types. A meaningful identifier is assigned to each variable to access and perform operations on the value by name. Variables enable the flexibility to represent different situations, process different sets of data, and produce varying outputs. [See: Defining Functions.]

Comprehensive troubleshooting requires knowledge of how computing devices and components work and interact. A systematic process will identify the source of a problem, whether within a device or in a larger system of connected devices. [See: Checking Your Work.]

People design algorithms and tools to automate the collection of data by computers. When data collection is automated, data is sampled and converted into a form that a computer can process. For example, data from an analog sensor must be converted into a digital form. The method used to automate data collection is influenced by the availability of tools and the intended use of the data. [See: Threats to Validity.]

People transform, generalize, simplify, and present large data sets in different ways to influence how other people interpret and understand the underlying information. Examples include visualization, aggregation, rearrangement, and application of mathematical operations. [See: Data Displays and Lookups; If-Expressions; Measures of Center; Spread of a Data Set.]

Computer models can be used to simulate events, examine theories and inferences, or make predictions with either few or millions of data points. Computer models are abstractions that represent phenomena and use data and algorithms to emphasize key features and relationships within a system. As more data is automatically collected, models can be refined. [See: Scatter Plots; Correlations.]

Programmers consider tradeoffs related to implementation, readability, and program performance when selecting and combining control structures. [See: Method Chaining; If-Expressions.]

Complex programs are designed as systems of interacting modules, each with a specific role, coordinating for a common overall purpose. These modules can be procedures within a program; combinations of data and procedures; or independent, but interrelated, programs. Modules allow for better management of complex tasks. [See: Defining Functions; Defining Table Functions; Method Chaining.]

Troubleshooting complex problems involves the use of multiple sources when researching, evaluating, and implementing potential solutions. Troubleshooting also relies on experience, such as when people recognize that a problem is similar to one they have seen before or adapt solutions that have worked in the past. [See: Checking Your Work.]

Data is constantly collected or generated through automated processes that are not always evident, raising privacy concerns. The different collection methods and tools that are used influence the amount and quality of the data that is observed and recorded. [See: Ethics and Privacy.]

The accuracy of predictions or inferences depends upon the limitations of the computer model and the data the model is built upon. The amount, quality, and diversity of data and the features chosen can affect the quality of a model and ability to understand a system. Predictions or inferences are tested to validate models. [See: Linear Regression; Threats to Validity.]

Data can be transformed to remove errors, highlight or expose relationships, and/or make it easier for computers to process. [See: Data Displays and Lookups; Visualizing the “Shape” of Data; Spread of a Data Set; Scatter Plots.]

The design and use of computing technologies and artifacts can improve, worsen, or maintain inequitable access to information and opportunities. [See: Ethics and Privacy.]

Laws govern many aspects of computing, such as privacy, data, property, information, and identity. These laws can have beneficial and harmful effects, such as expediting or delaying advancements in computing and protecting or infringing upon people’s rights. International differences in laws and ethics have implications for computing. [See: Ethics and Privacy.]

Fostering an Inclusive Computing Culture [See: Ethics and Privacy; Threats to Validity.]

Recognizing and Defining Computational Problems [See: Method Chaining; If-Expressions; Grouped Samples.]

Developing and Using Abstractions [See: Defining Functions; Defining Table Functions.]

Creating Computational Artifacts [See: Displaying Categorical Data; Histograms; Spread of a Data Set; Scatter Plots; Correlations.]

Testing and Refining Computational Artifacts [See: Checking Your Work.]

Communicating About Computing [See: Introduction to Computational Data Science; Choosing Your Dataset.]