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Feb 14, 2019 | By AndrewKramer
For years, data scientists have struggled to deploy their models in a timely manner before they become obsolete. Traditionally, models must be manually recoded, a time-intensive process that can take months, if not longer, to complete. Alteryx Promote solves this model deployment challenge by allowing data scientists to quickly turn complex Machine Learning models into a RESTful API from the development environment of their choice.
Feb 13, 2019 | By AshleyK
It’s no coincidence that a release as sweet as 2019.1 happened to coincide with a day focused on candy hearts and chocolate. Just like the 36 million heart shaped boxes of chocolate being given out around the world, we’re introducing Alteryx Analytics 2019.1 with a little something sweet for everyone!
Feb 8, 2019 | By SydneyF
A guiding principle in scientific fields and general problem solving is Occam’s razor (also known as the law of parsimony). Credited to 14th-century friar William Ockham, all that Occam’s razor states is "simple solutions are more likely to be correct than complex ones." Razor refers to the process of distinguishing between two hypotheses by “shaving away” any unnecessary assumptions.
Jan 31, 2019 | By SydneyF
There are two types of model errors when making an estimate; bias and variance. Understanding both of these types of errors, as well as how they relate to one another is fundamentally important to understanding model overfitting, underfitting, and complexity. Various sources of error can lead to bias and variance in a model. Understanding how these sources of error help us improve the data fitting process, resulting in more accurate models.
Jan 24, 2019 | By SydneyF
When there are missing values in a typical data set, you have a few options on how to handle them. You can create a new category for the missing values, you can remove the observations with missing values, or you can interpolate values for the missing observations. But what about spatial data? What if you have a spatial data set for a continuous feature (e.g., annual rainfall), but that data set doesn't include a value for a point that you need.
Nov 1, 2018 | By Alteryx
Why, when, and how are three critical questions for companies that want to win using analytics. But they must also truly understand how to answer those questions to gain a true enterprise analytics competency and shake up their industry. This competency gives organizations the ability to perform meaningful data analysis, by department and by individual, giving your analysts the freedom to ask even more questions and get answers quickly.
Oct 1, 2018 | By Alteryx
Chief Data Officer is a new and rapidly expanding role and many organisations are finding that it is an uncomfortable fit into the existing C-suite. Bringing together views, opinions and practitioners experience for the first time, The Chief Data Officer's Playbook offers a compelling guide to anyone looking to understand the current (and possible future) CDO landscape.
Oct 1, 2018 | By Alteryx
The Modern Analytics Lifecycle, when paired with a self-service analytics platform, can transform companies from status-quo to data-driven organizations that empower their business analysts and data scientists to push previously untouched boundaries.
Sep 1, 2018 | By Alteryx
Using Alteryx Server on AWS is a true game-changer for many AWS administrators, IT professionals, analysts, and data scientists. Alteryx Server on AWS is a cost effective and flexible way to manage and deploy various configurations of Alteryx Server.
Feb 14, 2019 | By Alteryx
Break free from the drudgery of cleansing, preparing, and blending your data with clunky solutions. Alteryx Designer enables you to quickly bring all your data sources together, no matter the structure or location, and perform advanced, forward-thinking analytics.
Perform advanced analytics like a badass with drag-and-drop, code-free (and code-friendly) capabilities. Perform logistic regressions and build models with a full suite of advanced analytic tools. For those specific needs, our code-friendly tools help you collaborate with data scientists to package R and Python code across your analysis. Dive into predictive analytics now.
Transform your analysis from ordinary to extraordinary with built-in location intelligence and geo-spatial capabilities. Enrich every analysis with our ready to use Location Insights dataset from industry data sources like US Census Bureau, Experian, TomTom, and Dun & Bradstreet. Quickly and easily reveal key location, consumer, and business insights without the help of a GIS specialist.
Data prep is the most crucial step in any analysis—bad data leads to bad insight. Stop wasting 80% of your time preparing data before analysis. Cleanse, prepare, and blend datasets using drag and drop tools with repeatability, so you can focus on game-changing insight, not mundane data prep.