Domino Data Lab

SEPTEMBER 18, 2019

This post covers data exploration using machine learning and interactive plotting. Models are at the heart of data science. Data exploration is vital to model development and is particularly important at the start of any data science project. Interactive Data Visualization in Python.

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DataRobot Blog

OCTOBER 3, 2017

A Director of Information Analytics Services at a large, multinational healthcare services company is responsible for collecting and changing data schematics from third-party sources, matching and integrating mixed profiles of users, and mapping it to a conformed format. DataRobot Data Prep. free trial. Try now for free.

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CIO Business Intelligence

MARCH 25, 2022

I bring the tech and cyber expertise to those boards, and also the digital piece,” adds Martin, a member of the CIO Hall of Fame since 2017. “It It’s giving companies an opportunity to rethink how they interact with customers, connect with supply chains, and drive internal operational efficiencies. It actually makes me work harder.

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Smart Data Collective

MARCH 23, 2022

However, we have witnessed a significant uptick in ADA cases being filed against website owners since 2017. Evan Morris of Towards Data Science discussed this in one of his recent articles. between Q1 of 2017 and Q1 of 2018. AI technology has made it easier to conform to ADA standards. That’s a lot of cash!

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Domino Data Lab

JULY 2, 2019

In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. The approach they’ve used applies to other popular data science APIs such as NumPy , Tensorflow , and so on.

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datapine

APRIL 14, 2022

After all, these are some pretty massive industries with many examples of big data analytics, and the rise of business intelligence software is answering what data management needs. However, the usage of data analytics isn’t limited to only these fields. Download our free summary outlining the best big data examples!

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CIO Business Intelligence

APRIL 15, 2022

In 2017, The Economist declared that data, rather than oil, had become the world’s most valuable resource. Organizations across every industry have been and continue to invest heavily in data and analytics. But like oil, data and analytics have their dark side. The refrain has been repeated ever since.

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Smart Data Collective

JUNE 10, 2019

Python is one of the most important languages for data science. This language allows the developer to create interactive websites and is a highly useful web tool alongside CSS and HTML. Back in 2017, Google declared Kotlin to be the official Android Development Language. There are several reasons they are correct.

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The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

TF Lattice offers semantic regularizers that can be applied to models of varying complexity, from simple Generalized Additive Models, to flexible fully interacting models called lattices, to deep models that mix in arbitrary TF and Keras layers. The drawback of GAMs is that they do not allow feature interactions.

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Domino Data Lab

SEPTEMBER 9, 2019

Data science teams in industry must work with lots of text, one of the top four categories of data used in machine learning. That’s excellent for supporting really interesting workflow integrations in data science work. Usually it’s human-generated text, but not always. get_data(). ?corpus

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Domino Data Lab

AUGUST 8, 2019

Paco Nathan ‘s latest monthly article covers Sci Foo as well as why data science leaders should rethink hiring and training priorities for their data science teams. In this episode I’ll cover themes from Sci Foo and important takeaways that data science teams should be tracking. Introduction.

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Domino Data Lab

JANUARY 6, 2019

In Paco Nathan ‘s latest column, he explores the theme of “learning data science” by diving into education programs, learning materials, educational approaches, as well as perceptions about education. He is also the Co-Chair of the upcoming Data Science Leaders Summit, Rev. Learning Data Science.

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Jen Stirrup

APRIL 2, 2019

Research evidence has shown that consumers interact with advertising in complex ways, especially since we have such short attention spans (Weilbacher, 2003). to 3.9% (UK Government, 2017). UK Government 2017, Executive Summary. It’s about focusing on the real , not the soundbite. Real never goes out of fashion.

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Smarten

JANUARY 3, 2019

These business users have adopted business intelligence and advanced analytical tools to gather and analyze data from varied data sources and use that analysis to identify the root cause of problems, identify opportunities, solve problems and share crucial data to support business decisions.

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Domino Data Lab

APRIL 3, 2019

The top three items are essentially “the devil you know” for firms which want to invest in data science: data platform, integration, data prep. Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. Rinse, lather, repeat.

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Smarten

APRIL 24, 2019

Augmented Analytics allows organizations to integrate data from numerous data sources and to use that data to analyze and display results in a clear manner so the business can make unbiased decisions and establish objective metrics. Users can compare results against plans and forecasts.

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AWS Big Data

AUGUST 7, 2023

Time series data is plottable on a line graph and such time series graphs are valuable tools for visualizing the data. Data scientists use them to identify forecasting data characteristics. For more information, see ElectricityLoadDiagrams20112014 Data Set (Dua, D. and Karra Taniskidou, E.

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Rita Sallam

AUGUST 17, 2016

For the past couple of years, Gartner has been describing the next wave of BI market disruption, smart data discovery, which Beyondcore has pioneered (along with IBM Watson Analytics, SparkBeyond and DataRPM). Here is what we know about Workday’s plans for Platfora: Workday will no longer sell Platfora as a standalone offering.

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Domino Data Lab

OCTOBER 23, 2019

Eighty percent of this problem is collecting the data and then transforming the data. The other 20 percent is ML- and data science–related tasks like finding the right model, doing EDA, and feature engineering. Gathering the Data. there is a list of data sources to extract and transform. In Figure 6.1,

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The Unofficial Google Data Science Blog

APRIL 23, 2024

However, if we experiment with both parameters at the same time we will learn something about interactions between these system parameters. Interactions between parameters in different YouTube recommendation subsystems definitely exist, but important interactions and quadratic effects seem relatively rare. Efron and C.

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Peter James Thomas

AUGUST 12, 2018

As Neil puts it in his article: […] technology is never a solution to social problems, and interactions between human beings are inherently social. Ideas for avoiding Big Data failures and for dealing with them if they happen (2017). For example: The confluence of BI and change management (2009). Follow @peterjthomas.

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O'Reilly on Data

MARCH 19, 2020

Reinforcement learning fell by 5% in 2019; it’s up hugely—1,500+%—since 2017, however. Aggregating artificial intelligence and machine learning topics accounts for nearly 5% of all usage activity on the platform, a touch less than, and growing 50% faster than, the well-established “data science” topic (see Figure 2).

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Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Other good related papers include: “ Towards A Rigorous Science of Interpretable Machine Learning ”.

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Domino Data Lab

AUGUST 1, 2021

The need for interaction – complex decision making systems often rely on Human–Autonomy Teaming (HAT), where the outcome is produced by joint efforts of one or more humans and one or more autonomous agents. In IJCAI 2017 Workshop on Explainable Artificial Intelligence (XAI), pages 24–30, Melbourne, Australia, 2017.

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Domino Data Lab

FEBRUARY 4, 2019

In Paco Nathan ‘s latest column, he explores the role of curiosity in data science work as well as Rev 2 , an upcoming summit for data science leaders. Welcome back to our monthly series about data science. and dig into details about where science meets rhetoric in data science.

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Insight

DECEMBER 30, 2019

In Figure 1, you can see the results of the Harris corner detector applied to an image of Jessie Graff competing in the 2017 American Ninja Warrior Finals: Figure 1?—?Harris Harris corner detection applied to an image of Jessie Graff competing in the 2017 American Ninja Warrior Finals. Original image is on the left.

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O'Reilly on Data

JANUARY 25, 2021

The data includes all usage of our platform, not just content that O’Reilly has published, and certainly not just books. We’ve explored usage across all publishing partners and learning modes, from live training courses and online events to interactive functionality provided by Katacoda and Jupyter notebooks. What about data science?

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Data Science and Beyond

JANUARY 8, 2017

As of early 2017, fewer than half. Popularity is a function of inbound links (weighted by site credibility), and of user interaction with the presented results (e.g., The rise of data science increases the availability of statistical and scientific tools to small and large businesses. Why is that?

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