Big Data
Extremely large data sets that can be analyzed computationally to reveal patterns, trends, and associations. Crucial for gaining insights and making data-driven decisions.
33 topics found for:
“computational analytics”
AWS
Amazon Web Services (AWS) is a comprehensive cloud computing platform provided by Amazon that offers a wide range of services including computing power, storage, and databases. Crucial for enabling scalable, cost-effective, and flexible IT infrastructure solutions for businesses of all sizes.
Big Data Engineer
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.
BI
Business Intelligence (BI) encompasses technologies, applications, and practices for the collection, integration, analysis, and presentation of business information. Crucial for making data-driven decisions and improving business performance.
Empirical Rule
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.
InfoVis
Information Visualization (InfoVis) is the study and practice of visual representations of abstract data to reinforce human cognition. Crucial for transforming complex data into intuitive visual formats, enabling faster insights and better decision-making.
Cross Validation
A statistical method used to assess the generalizability of a model to unseen data, involving partitioning a dataset into subsets for training and validation. Essential for evaluating model performance and preventing overfitting in digital product analytics.
Polynomial Regression
A form of regression analysis where the relationship between the independent variable and the dependent variable is modeled as an nth degree polynomial. Useful for modeling non-linear relationships in digital product data analysis.
Analytics
The systematic computational analysis of data or statistics to understand and improve business performance. Essential for data-driven decision making in design, product management, and marketing.
Computational Thinking
A problem-solving process that includes logical reasoning, pattern recognition, abstraction, and algorithmic thinking. Important for developing efficient and effective solutions in digital product design and development.
Data Science
An interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Essential for driving data-informed decision making, predicting trends, and uncovering valuable insights in digital product design and development.
IoT
Internet of Things (IoT) refers to a network of interconnected physical devices embedded with electronics, software, sensors, and network connectivity, enabling them to collect and exchange data. Essential for creating smart, responsive environments and improving efficiency across various industries by enabling real-time monitoring, analysis, and automation.
Factor Analysis
A statistical method used to identify underlying relationships between variables by grouping them into factors. Crucial for simplifying data and identifying key variables in research.
ERP
Enterprise Resource Planning (ERP) are integrated software systems that manage business processes across various departments, such as finance, HR, and supply chain. Essential for improving operational efficiency and providing a unified view of business operations.
Digital Transformation
The integration of digital technology into all areas of a business, fundamentally changing how it operates and delivers value to customers. Essential for staying competitive and relevant in a rapidly evolving digital landscape.
Confidence Interval
A range of values, derived from sample statistics, that is likely to contain the value of an unknown population parameter. Essential for making inferences about population parameters and understanding the precision of estimates in product design analysis.
Augmented Intelligence
A design pattern that combines human and machine intelligence to enhance decision-making and problem-solving. Important for leveraging AI to support and amplify human capabilities.
AIaaS
AI as a Service (AIaaS) is a service model where AI tools and algorithms are provided over the internet by a third-party provider. Essential for making advanced AI capabilities accessible to businesses.
Product Mix Strategy
The approach a company takes to manage and market its portfolio of products, ensuring each product supports the overall business strategy. Important for optimizing the range of products offered to maximize market reach and profitability.
Morphological Analysis
A problem-solving method that explores all possible solutions by examining the structure and relationships of different variables. Useful for generating innovative design solutions and exploring a wide range of possibilities in digital product development.
Ingroup Bias
A cognitive bias where people favor members of their own group over those in other groups. Important for designing inclusive and equitable experiences for users.
Information Ecology
The study of the relationships between people, practices, values, and technologies within an information environment. Helps in understanding and designing systems that are sustainable and adaptive to human and environmental changes.
Information Aversion
The tendency to avoid information that one perceives as potentially negative or anxiety-inducing. Important for designing experiences that encourage information-seeking behavior.
Enterprise Architecture
Enterprise Architecture (EA) is a strategic framework used to align an organization's business strategy with its IT infrastructure. Crucial for optimizing processes, improving agility, and ensuring that technology supports business goals.
Emerging Trends
Newly developing patterns or shifts in technology, behavior, or design that have the potential to influence future practices and strategies. Important for staying ahead of the curve and adapting to changes in the industry.
Data Architect
A professional responsible for designing and managing data structures, storage solutions, and data flows within an organization. Important for ensuring efficient data management and supporting data-driven decision-making in digital product design.
BPMS
Business Process Management Software (BPMS) refers to tools and systems that help organizations design, model, execute, monitor, and optimize their business processes. Essential for improving operational efficiency and ensuring that digital products support effective business processes.
Actor-Observer Bias
A cognitive bias that causes people to attribute their own actions to situational factors while attributing others' actions to their character. Essential for helping designers recognize their own situational influences on interpreting user behavior and feedback.
5 Whys
A problem-solving method that involves asking "why" five times to identify the root cause of a problem. Useful for designers and product managers to uncover underlying issues and improve processes and solutions.
UCD
User-Centered Design (UCD) is an iterative design approach that focuses on understanding users' needs, preferences, and limitations throughout the design process. Crucial for creating products that are intuitive, efficient, and satisfying for the intended users.
Three-Sigma Rule
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.
Outliers
Data points that differ significantly from other observations and may indicate variability in a measurement, experimental errors, or novelty. Crucial for identifying anomalies and ensuring the accuracy and reliability of data in digital product design.
Matched Pairs Design
An experimental design where subjects are paired based on certain characteristics, and then one is assigned to the treatment and the other to the control group. Important for reducing variability and improving the accuracy of experimental results.