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Data Science and Data Analytics Glossary

Data Prep and Analytics

Business Analytics

Business analytics is the process analyzing data using statistical and quantitative methods to make decisions that drive better business outcomes.

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Business Intelligence

Business intelligence is the cumulative outcome of an organization's data, software, infrastructure, business processes, and human intuition that delivers actionable insights.

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Data Analytics

Data analytics is the process of exploring, transforming, and analyzing data to identify meaningful insights and efficiencies that support decision-making.

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Data Blending

Data blending is the act of bringing data together from a wide variety of sources into one useful dataset to perform deeper, more complex analyses.

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Data Catalog

How a Data Catalog Helps an Organization Make the Best Use of Data Assets

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Data Cleansing

Data cleansing, also known as data cleaning or scrubbing, identifies and fixes errors, duplicates, and irrelevant data from a raw dataset.

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Data Exploration

Data exploration is a way to get to know data before working with it. Through survey and investigation, large datasets are readied for deeper, more structured analysis.

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Data Lineage

Track where an organization’s data comes from, the journey it takes through the system, and keep business data compliant and accurate.

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Data Preparation

Data preparation is the act of cleaning and consolidating raw data prior to using it for business analysis. Learn why it's critical and how it works.

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Data Profiling

Data profiling helps discover, understand, and organize data by identifying its characteristics and assessing its quality.

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Data Wrangling

Data wrangling is the act of transforming, cleansing, and enriching data to make it more applicable, consumable, and useful to make smarter business decisions.

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Descriptive Analytics

Descriptive analytics answers the question “What happened?” by drawing conclusions from large, raw datasets. The findings are then visualized into accessible line graphs, tables, pie and bar charts, and generated narratives.

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ETL

ETL is the process used to copy, combine, and convert data from different sources and formats and load it into a new destination such as a data warehouse or data lake.

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Spatial Analytics

Spatial analysis models problems geographically, allowing a company to analyze the locations, relationships, attributes, and proximities in geospatial data to answer questions and develop insights.

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Data Science and Machine Learning

Advanced Analytics

Advanced analytics uses sophisticated techniques to uncover insights, identify patterns, predict outcomes, and generate recommendations.

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AutoML

Automated machine learning, or AutoML, makes ML accessible to non-experts by enabling them to build, validate, iterate, and explore ML models through an automated experience.

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Data Science

Data science is a form of applied statistics that incorporates elements of computer science and mathematics to extract insight from both quantitative and qualitative data.

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Data Science vs Machine Learning

Data science and machine learning are buzzwords in the technology world. Both enhance AI operations across the business and industry spectrum. But which is best?

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Feature Engineering

With feature engineering, organizations can make sense of their data and turn it into something beneficial.

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Machine Learning

Machine learning is the iterative process a computer uses to identify patterns in a dataset given specific constraints.

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MLOps Machine Learning Operations

MLOps is a cross-functional, collaborative, and iterative process that operationalizes data science by managing machine learning (ML) and other types of models to be reusable software artifacts that can be deployed and continuously monitored via a repeatable process.

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Predictive Analytics

Predictive analytics is a type of data analysis that uses machine learning, statistical algorithms, and other techniques to predict what will happen in the future.

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Prescriptive Analytics

Prescriptive analytics answers the question “What should/can be done?” by using machine learning, graph analysis, simulation, heuristics, and other methods.

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Supervised vs Unsupervised Learning

Supervised and unsupervised learning models work in unique ways to help businesses better engage with their consumers.

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Whatever your skillset, Alteryx brings simple and powerful analytics and data science to everyone and enables results beyond what was imagined.

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