Smart Data Collective

MARCH 23, 2022

Evan Morris of Towards Data Science discussed this in one of his recent articles. Evan Morris of Towards Data Science discussed this in one of his recent articles. Morris points out that AI helps with automated testing. One of the overlooked benefits of AI in website optimization pertains to ADA compliance.

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CIO Business Intelligence

JANUARY 10, 2023

Based on Gartner data, the overall supply of tech workers has increased only by a few percentage points at most. In key function areas, like data science, software engineering, and security, talent supply remains as tight or tighter than before.” IT leaders say the best approach is to focus on adaptability.

IT 124

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Domino Data Lab

AUGUST 5, 2019

This Domino Data Science Field Note covers Chris Wiggins ‘s recent data ethics seminar at Berkeley. Data Scientists, Tradeoffs, and Data Ethics. As more companies become model-driven , data scientists are uniquely positioned to drive innovation and to help their companies remain economically strong.

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CIO Business Intelligence

APRIL 18, 2022

In 2006, British mathematician Clive Humby proclaimed, “Data is the new oil.”. He and his wife, Edwinna Dunn, own Dunnhumby, a global customer data science company that helped Tesco create its Clubcard, the world’s first supermarket loyalty card. And using data typically creates more data. Collibra. “At

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CIO Business Intelligence

JULY 20, 2022

The problem: She had no formal training and experience in computer science. Now a benefits engineer as part of Gusto’s Engineering Product Design and Data department, McGrath puts time in mentoring other women interested in making similar career changes. What might be a hurdle for many did not stop McGrath or her employers at Gusto.

IT 131

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Insight

OCTOBER 25, 2019

Image credit: [link] As the title suggests, this is a story about a question that may resonate well with many machine learning practitioners trying to build applications in the real world, where clean and annotated data on a specific problem can be sparse— How do we leverage the power of AI when we have very little data?

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Domino Data Lab

OCTOBER 7, 2020

This could be lots of rows (samples) and few columns (variables) like credit card transaction data, or lots of columns (variables) and few rows (samples) like genomic sequencing in life sciences research. Each variable in a data set is a dimension with the set of variables defining the space in which the samples fall.

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Domino Data Lab

JULY 17, 2021

The ability of a system to recognize a person by their voice is a non-intrusive way to collect their biometric information. Unlike fingerprint detection, retinal scans or face recognition, speaker recognition just uses a microphone to record a person’s voice thereby circumventing the need for expensive hardware.

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DataRobot

MAY 14, 2021

Machines bring unparalleled power, speed, and efficiency to processing large data sets and routine tasks based on a set of rules. In 2021, we are embracing the code-first data scientist. This is another first for Data Science and Machine Learning technology. We just wrapped DataRobot’s latest AI Experience Worldwide.

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Rita Sallam

APRIL 2, 2023

The first featured analytics and BI platform Gartner Magic Quadrant leaders while the other showcased high interest data science and machine learning platforms. The Bake-Offs are fast-paced, fun and informative sessions that let you see leading vendors side-by-side using scripted demos and a common data set in a controlled setting.

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Optimization Machine Learning Insurance Risk 52

Smarten

MAY 18, 2022

If your business is focused on data-driven, fact-based decisions, your business users may be leveraging an analytics solution to gather, find and analyze data. Business goals include improving results and productivity, and getting the best results out of your data, as well as gaining meaningful insight into data.

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Domino Data Lab

APRIL 7, 2019

We assume that you are looking for widely used, proven-in-the-field NLP libraries that are in production – code that just works because many others have tested it already. We assume that you are looking for widely used, proven-in-the-field NLP libraries that are in production – code that just works because many others have tested it already.

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Insight

MARCH 12, 2020

With breaking this bottleneck in mind, I’ve used my time as an Insight Data Science Fellow to build the AIgent, a web-based neural net to connect writers to representation. In this article, I will discuss the construction of the AIgent, from data collection to model assembly. author of the Harry Potter series?—?sales

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The Unofficial Google Data Science Blog

OCTOBER 7, 2015

A/B testing is used widely in information technology companies to guide product development and improvements. For questions as disparate as website design and UI, prediction algorithms, or user flows within apps, live traffic tests help developers understand what works well for users and the business, and what doesn’t.

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O'Reilly on Data

OCTOBER 19, 2021

As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. Much has been written about struggles of deploying machine learning projects to production. The new category is often called MLOps.

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DataKitchen

MAY 10, 2021

Data errors infringe on work-life balance. Data errors also affect careers. If you have been in the data profession for any length of time, you probably know what it means to face a mob of stakeholders who are angry about inaccurate or late analytics. Data sources must deliver error-free data on time.

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Domino Data Lab

OCTOBER 2, 2019

This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by Andrew Kelleher and Adam Kelleher. A complementary Domino project is available. . Introduction. Chapter Introduction: Causal Inference.

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The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. In this post we explore how and why we can be “ data-rich but information-poor ”. There are many reasons for the recent explosion of data and the resulting rise of data science.

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Insight

MARCH 13, 2020

Learn how the best teams adapt to interviewing while they WFH At Insight , we partner with companies to build and scale their tech teams. In the last few weeks, teams that we work with have been transitioning to remote work to keep their teams safe and do what they can to limit the spread of coronavirus. Interested in more advice?

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Cloudera

AUGUST 6, 2018

In recent years, we have heard a great deal about how new and sophisticated understanding of how to interpret the onslaught of data produced in the modern age could help us turn the corner on major social and environmental problems. While we have been using data to improve philanthropy for years, a great deal has changed very quickly.

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The Unofficial Google Data Science Blog

JULY 17, 2018

Through this example, we discuss some of the special considerations impacting a data scientist when designing solutions to improve decision-making deep within software infrastructure. Every large-scale system has its own constraints and pressures that the data scientist will need to accommodate.

Data Science 85

Data Science Snapshot Data Processing Optimization 85

Insight

JULY 23, 2019

I frequently run into this issue in my data science workflow with complex objects in libraries, like TensorFlow. Image used with permission from [link] Have you ever had a new Python object that you wanted to quickly familiarize yourself with? We can use the built-in callable function to filter them.

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The Unofficial Google Data Science Blog

JULY 22, 2020

Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. The website wants to make sure they have the infrastructure to handle the feature while testing if engagement increases enough to justify the infrastructure.

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datapine

OCTOBER 21, 2020

One of the most notable areas where data analytics is making big changes is healthcare. Health professionals, just like business entrepreneurs, are capable of collecting massive amounts of data and look for the best strategies to use these numbers. Exclusive Bonus Content: Boost your healthcare business with big data!

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The Unofficial Google Data Science Blog

DECEMBER 14, 2021

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. This blog post introduces the notions of representational uncertainty and interventional uncertainty to paint a fuller picture of what the practicing data scientist is up against. or "does this product change benefit users?"

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IBM Big Data Hub

FEBRUARY 22, 2024

A recent IBM survey found that the top barriers preventing successful AI deployment include limited AI skills and expertise, data complexity, and ethical concerns. AI tools are now utilized in national security and to help protect against data breaches and cyberattacks. These are table stakes for the DoD or any government agency.

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Metadata Measurement Risk Modeling 101

AWS Big Data

MARCH 28, 2023

In a data warehouse, a dimension is a structure that categorizes facts and measures in order to enable users to answer business questions. A slowly changing dimension (SCD) is a data warehousing concept that contains relatively static data that can change slowly over a period of time.

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Domino Data Lab

APRIL 21, 2021

Historically, fraud detection has been performed by analysts who are responsible for identifying and tracing suspicious patterns in the data. In this article, we’ll discuss the challenge organizations face around fraud detection, how machine learning can be used to identify and spot anomalies that the human eye might not catch.

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The Unofficial Google Data Science Blog

JUNE 30, 2016

The Fundamental Problem of Causal Inference The primary goal of many data analysis efforts is to estimate the effect of an intervention on the distribution of some outcome. To do this, you have a data set at the person level containing, among other variables, an indicator of ad exposure, and whether the person bought the truck.

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Insight

MARCH 8, 2019

As part of a 7-week program designed to help transition to a thriving career in data, Insight Fellows spend four weeks ideating, developing, and refining a project that solves a real-world problem. L to R: Insight Fellows Tayler Sale, Cristiana Principato, Susanna McDonald, Hannah Lee.

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Domino Data Lab

MAY 8, 2019

Admittedly, throughout large swaths of computer science, reductionism serves quite well. Admittedly, throughout large swaths of computer science, reductionism serves quite well. For example, common practices for collecting data to build training datasets tend to throw away valuable information along the way. Introduction.

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The Unofficial Google Data Science Blog

APRIL 23, 2024

Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. by CHRIS HAULK It is sometimes useful to think of a large-scale online system ( LSOS ) as an abstract system with parameters $X$ affecting responses $Y$.

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Cloudera

MAY 17, 2021

Modernizing your data warehousing experience with the cloud means moving from dedicated, on-premises hardware focused on traditional relational analytics on structured data to a modern platform. You also want to be sure your data warehouse isn’t locked to today, but is part of a growing, enterprise data cloud. .

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Domino Data Lab

MAY 20, 2021

Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. Insufficient training data in the minority class — In domains where data collection is expensive, a dataset containing 10,000 examples is typically considered to be fairly large.

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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

DECEMBER 2, 2020

Data scientists use clustering to identify malfunctioning servers, group genes with similar expression patterns, and perform various other applications. There are many families of data clustering algorithms, and you may be familiar with the most popular one: k-means. I have scattered 100 data points in the interval [1,3]X[2,4].

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CIO Business Intelligence

MARCH 22, 2023

Industries increasingly rely on data and AI to enhance processes and decision-making. Conventional IT and data teams query data containing PII, but only a select few require access. Protecting sensitive data in the modern AI/ML pipeline has different requirements. Safeguarding PII is not a new problem.

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