The spread and pattern of data values in a dataset, often visualized through graphs or statistical measures. Critical for understanding the characteristics of data and informing appropriate analysis techniques in digital product development.
A professional who designs, builds, and maintains systems for processing large-scale data sets. Essential for enabling data-driven decision-making and supporting advanced analytics in organizations.
A symmetrical, bell-shaped distribution of data where most observations cluster around the mean. Fundamental in statistics and crucial for many analytical techniques used in digital product design and data-driven decision making.
A graphical representation of the distribution of numerical data, typically showing the frequency of data points in successive intervals. Important for analyzing and interpreting data distributions, aiding in decision-making and optimization in product design.
A statistical distribution where most occurrences take place near the mean, and fewer occurrences happen as you move further from the mean, forming a bell curve. Crucial for data analysis and understanding variability in user behavior and responses.
Also known as the 68-95-99.7 Rule, it states that for a normal distribution, nearly all data will fall within three standard deviations of the mean. Important for understanding the distribution of data and making predictions about data behavior in digital product design.
A statistical theory that states that the distribution of sample means approximates a normal distribution as the sample size becomes larger, regardless of the population's distribution. Important for making inferences about population parameters and ensuring the validity of statistical tests in digital product design.
A sorting algorithm that distributes elements into a number of buckets, sorts each bucket individually, and then combines the buckets to get the sorted list. Useful for understanding more advanced algorithmic techniques and their applications.
A statistical rule stating that nearly all values in a normal distribution (99.7%) lie within three standard deviations (sigma) of the mean. Important for identifying outliers and understanding variability in data, aiding in quality control and performance assessment in digital product design.
An intermediary that gathers and provides information to users, typically in an online context. Important for helping users make informed decisions based on aggregated data.
A decentralized digital ledger that records transactions across many computers in a way that ensures the security and transparency of data. Crucial for understanding and implementing secure, transparent digital transactions and applications.
3-Tiered Architecture is a software design pattern that separates an application into three layers: presentation, logic, and data. Crucial for improving scalability, maintainability, and flexibility in software development.
The four key elements of marketing: Product, Price, Place, and Promotion, used to develop marketing strategies. Important for creating comprehensive marketing strategies that effectively promote digital products.
The process of determining which tasks should be performed by humans and which by machines in a system. Essential for optimizing system efficiency and usability.
Simple Object Access Protoco (SOAPl) is a protocol for exchanging structured information in web services. Crucial for enabling communication between applications over a network.
ModelOps (Model Operations) is a set of practices for deploying, monitoring, and maintaining machine learning models in production environments. Crucial for ensuring the reliability, scalability, and performance of AI systems throughout their lifecycle, bridging the gap between model development and operational implementation.
Numeronym for the word "Observability" (O + 11 letters + N), the ability to observe the internal states of a system based on its external outputs, facilitating troubleshooting and performance optimization. Crucial for monitoring and understanding system performance and behavior.
Model-Based Systems Engineering (MBSE) is a methodology that uses visual modeling to support system requirements, design, analysis, and validation activities throughout the development lifecycle. Essential for managing complex systems, improving communication among stakeholders, and enhancing the overall quality and efficiency of systems engineering processes.
A relative estimation technique used in Agile project management to quickly assess the size and complexity of tasks by assigning them T-shirt sizes (e.g., small, medium, large). Crucial for efficient project planning and workload management.
A brief daily meeting in Agile project management where team members share updates on their progress, plans for the day, and any obstacles they face. Essential for maintaining communication, transparency, and coordination within Agile teams.
The ability of a system, product, or process to handle increased loads or expand without compromising performance or efficiency. Essential for ensuring that products and systems can grow and adapt to increasing demands.
A decision-making strategy where individuals allocate resources proportionally to the probability of an outcome occurring, rather than optimizing the most likely outcome. Important for understanding decision-making behaviors and designing systems that guide better resource allocation.
Objectives and Key Results (OKR) is a goal-setting framework for defining and tracking objectives and their outcomes. Essential for aligning organizational goals, improving focus and engagement, and driving measurable results across teams and individuals.