Geometry is a branch of mathematics that deals with the properties of lines, space, points, shapes and surfaces. DWT: Discrete wavelet transform option used in the wavelet transformation when the signal is fairly smooth, that is, the information is mainly contained in the low frequencies. Discrete variable: A variable that can only assume certain settings or levels (as opposed to a continuous variable that can have a value anywhere between two numerical limits). Least squares estimate: A method to estimate model parameters by minimizing the sum of squares of the differences between the actual response value and the value predicted by the model. Jack-knifing: A method for finding the confidence interval of an estimated model parameter, by iteratively keeping out parts of the underlying data, making estimates from the subsets and comparing these estimates. Data analytics: The process of examining large data sets to uncover hidden patterns, unknown correlations, trends, customer preferences and other useful business insights. Hotellings T2: A multivariate generalization of student’s t-test. Natural Language Generation or NLG is when data is turned to the English language for better understanding. Batch conditions: Batch conditions pertain to the whole batch and are therefore used in the batch level model (BLM). Time series data: A sequence of measurements taken at different times, and often, but not necessarily at equally spaced intervals. Multivarient analysis is a technique used for analysis of data that contains two or more independent variables in order to predict a value of a dependent variable. See: K-space. In big data terms, gamification is often a powerful way of incentivizing data collection. Individual blocks contain details of the preceding block such as the cryptographic hash, timestamp and transaction data. Explanatory variable: Variables (x) used to 'explain' the variation in the dependent variables (y). Real Time Analytics is simply the analysis of data immediately after it's made available, thereby leaving no room for any delays. Bilinear modeling: Matrices modeled as a product of two low rank matrices, e.g. Web API: An interface based on web technology to read or set data. Historical data refers to data that has been collected in the past. This site uses cookies to give our users the best experience on our website. Also see Principal Component Analysis. Forecasting is the process of making predictions of the future by analyzing past data and understanding current and past trends. The aim in analyzing all this data is to uncover patterns and connections that might otherwise be invisible, and that might provide valuable insights about the users who … Factor analysis: Has an aim similar to PCA, but assumes an underlying model with a specified number of factors which are linear combinations of the original variables. Insights | Glossary | Data Analytics. The term behavioral relates to behavior. Synonym: K-space. A graph database is a database that includes graphs containing nodes (representing an entity like a person) and edges (representing the relationship between nodes). Multivariate data analysis: A set of statistical techniques used to analyze data sets that contain more than one variable. It assumes a one-way causal effect from predictor variables (independent variables) to a response of another variable (dependent variable). If you have been in a conversation on machine learning, you have probably heard terms like feature, sample, and variable. Neuromorphic hardware refers to any electronic device which imitates the natural biological structures of a human being's nervous system. In the monitoring phase the new incoming, measured, data are used to detect whether the process is in control or not. In the Observations page of the Workset dialog the identifiers can be used to set classes. TTC . Used in the analysis of time series data. Elasticity is a measurement of how sensitive a variable is to changes in any other variable. A mathematical term, univariate is used to describe data which consists of observations based on a single characteristic. However, while the data may be easy to get hold of, that doesn’t make it easy to interpret and use, especially for new comers. Based on this, companies can identify gaps in current processes and chart out a strategy to achieve the set targets. Unstructured, native and of various sizes, a file in a data lake has no fixed limit. Analytics: This term refers to the course of depicting conclusions based on the raw data. Dataset: A dataset is the base of all multivariate data analysis, often also called a data matrix. Drill down: The procedure of model interpretation through inspection of multivariate parameters, followed by zooming-in on certain parts of the underlying data by double-clicking in plots or charts to open up visualizations of relevant parts of the real measurements. Clutering can be very useful for high-volume databases as it offers a backup in case of a server failure. Predictive Analytics is term used when information from the given data is taken into account in order to determine its future outcomes and trends. Response variable: See: dependent variable. Discriminant analysis: A statistical analysis technique used to predict class membership from labeled data. An independent variable can be manipulated to test the effects it has on dependent variables. Augmented Analytics refers to when machine learning and natural processing languages are used to enhance data analytics and sharing. In other words, it is a hybrid system that blends actual reality with virtual reality. Continuous variable: A variable whose value can be any of an infinite number of values, typically within a particular range. The following are terms and concepts used in Workplace Analytics. 0 (zero) is both a number and the numerical digit used to represent that number in numerals. In other words, PaaS is short form for platform-as-a-service often used by companies for their data and marketing. Contingency table: A table which contains counts or frequencies of different events or outcomes. Batch Context Generator: A system that automatically detects and contextualizes batches from triggers on tags in the system. hbspt.cta._relativeUrls=true;hbspt.cta.load(2718060, '30c5b038-a870-4d5e-910b-46262b38ab6f', {}); Sign up for one of our free webinars to learn more about data analytics from OPLS to design of experiments (DOE) to batch process analytics. The qualitative settings do not have a natural order and can therefore not be converted into a single numerical (quantitative) variable. Data analytics. “Analytics has emerged as a catch-all term for a variety of different business intelligence (BI)- and application-related initiatives. Python is a high-level programming language helps one learn and integerate systems effectively, with particular emphasis on code readability. MOCA, Multiblock Orthogonal Component Analysis: Generalization of OPLS to cover multiple blocks of data and search for their joint and unique variablities. Characteristic vector analysis: See: Principal component analysis. … In other words, it is a software for artificial intelligence workers. Phase iterations: The modelling and monitoring of complex phases that can happen more than once or be split and then merged again. Regression can be used to explain the past and predict future events. The term ‘data analytics’ (or ‘DA’) is part of our analytics consulting service and it is generally used to define the process of using an algorithmic or mechanical process to derive insights that can then be leveraged from a business-like perspective; it represents one of the first steps within our Performance Management service, … A fiscal year, or financial year, is a specific period used for accounting and tax purposes. A Real Variable is to a variable wherein real numbers are assigned as values. Continuous process verification: The need to keep all critical attributes and their correlation under control during the production. 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