Predictive analytics Definitions

  • A

    A/B testing (split testing)

    A/B testing is a statistical method used to assess proposed changes to a product or service.

  • advanced analytics

    While the traditional analytical tools that comprise basic business intelligence (BI) examine historical data, tools for advanced analytics focus on forecasting future events and behaviors

  • association rules (in data mining)

    Association rules are if/then statements that help uncover relationships between seemingly unrelated data in a transactional database, relational database or other information repository.  

  • B

    backpropagation algorithm

    Backpropagation (backward propagation) is an important mathematical tool for improving the accuracy of predictions in data mining and machine learning. Essentially, backpropagation is an algorithm used to calculate derivatives quickly.

  • big data analytics

    Big data analytics is the process of examining large and varied data sets -- i.e., big data -- to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions.

  • business analytics (BA)

    Business analytics (BA) is the practice of iterative, methodical exploration of an organization's data, with an emphasis on statistical analysis.

  • D

    data journalism

    Data journalism in an approach to writing for the public in which the journalist analyzes large data sets to identify potential news stories.

  • data sampling

    Data sampling is a statistical analysis technique used to select, manipulate and analyze a representative subset of data points in order to identify patterns and trends in the data set being examined as a whole.

  • data science as a service (DSaaS)

    Data science as a service (DSaaS) is the delivery of analytical reports and data products with embedded analytics produced by a service provider's team of data scientists to a client enterprise.

  • data scientist

    A data scientist is a professional responsible for collecting, analyzing and interpreting large amounts of data to identify ways to help a business improve operations and gain a competitive edge over rivals.

  • deep analytics

    Deep analytics is the application of sophisticated data processing techniques to yield information from large and typically multi-source data sets comprised of both unstructured and semi-structured data.

  • deep learning (deep neural networking)

    Deep learning is an aspect of artificial intelligence (AI) that is concerned with emulating the learning approach that human beings use to gain certain types of knowledge. At its simplest, deep learning can be thought of as a way to automate predictive analytics.

  • E

    edge analytics

    Edge analytics applies algorithms to data at the point of collection in order to trigger actions and determine what should be sent back to a central data repository and what should be discarded.

  • ensemble modeling

    Ensemble modeling is the process of running two or more related but different analytical models and then synthesizing the results into a single score or spread in order to improve the accuracy of predictive analytics and data mining applications.

  • F

    funnel analysis

    Funnel analysis is a way to measure and improve the performance of customer interactions in a step-wise progression from the initial customer contact to a predetermined conversion metric.

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