Search Results for "computer based test"

JavaScript and TypeScript finite state machines and statecharts for the modern web. Statecharts are a formalism for modeling stateful, reactive systems. This is useful for declaratively describing the behavior of your application, from the individual components to the overall application logic. XState is a library for creating, interpreting, and executing finite state machines and statecharts, as well as managing invocations of those machines as actors. The following fundamental computer...

Briefer is an open-source collaborative data platform that brings notebooks, dashboards, and interactive data apps into a unified workspace that combines the flexibility of code with the simplicity of visual exploration. It’s designed so technical users can write Markdown, SQL, and Python side by side for data analysis, visualization, and reporting, while non-technical viewers can interact with results through inputs, dropdowns, and date pickers without writing any code. Users work in a...

By using easy-to-understand views, SandDance helps you find insights about your data, which in turn help you tell stories supported by data, build cases based on evidence, test hypotheses, dig deeper into surface explanations, support decisions for purchases, or relate data into a wider, real world context. SandDance uses unit visualizations, which apply a one-to-one mapping between rows in your database and marks on the screen. Smooth animated transitions between views help you to maintain context as you interact with your data. ...

...Get a sense of the shape of each feature of your dataset using Facets Overview, or explore individual observations using Facets Dive. Explore Facets Overview and Facets Dive on the UCI Census Income dataset, used for predicting whether an individual’s income exceeds $50K/yr based on their census data. The census data contains features such as age, education level, and occupation for each individual. Overview gives users a quick understanding of the distribution of values across the features of their dataset(s). Uncover several uncommon and common issues such as unexpected feature values, missing feature values for a large number of observation, training/serving skew and train/test/validation set skew.