DataRobot

OCTOBER 20, 2021

What Is Data Literacy? Data literacy is the ability to understand data science and AI applications critically using basic data visualization, communication, and reasoning skills. And beyond uniting diverse stakeholders, data literacy also empowers sectoral regulators to provide industry-specific guidance.

Machine Learning 52

Machine Learning Metrics Risk Deep Learning 52

O'Reilly on Data

NOVEMBER 13, 2018

Data Platforms. Over the last 12-18 months, companies that use a lot of ML and employ teams of data scientists have been describing their internal data science platforms (see, for example, Uber , Netflix , Twitter , and Facebook ). How to build analytic products in an age when data privacy has become critical”.

Machine Learning 220

Machine Learning Risk Management Statistics 220

Sisense

APRIL 10, 2020

One of the most-asked questions from aspiring data scientists is: “What is the best language for data science? People looking into data science languages are usually confused about which language they should learn first: R or Python. NLP can be used on written text or speech data. R or Python?”.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

CIO Business Intelligence

NOVEMBER 22, 2022

Data observability becomes business-critical Data observability extends the concept of data quality by closely monitoring data as it flows in and out of the applications. A key data observability attribute is that it acts on metadata, providing a safe way to monitor data directly within applications.

Data Strategy 145

Data Strategy Strategy Enterprise Consulting 145

The Unofficial Google Data Science Blog

OCTOBER 31, 2016

By PATRICK RILEY For a number of years, I led the data science team for Google Search logs. We were often asked to make sense of confusing results, measure new phenomena from logged behavior, validate analyses done by others, and interpret metrics of user behavior. Something like histograms, CDFs, Q-Q plots, etc.

Metrics 107

Metrics Measurement Statistics Data Collection 107

DataRobot Blog

JULY 27, 2022

In this technical post, we’ll focus on some changes we’ve made to allow custom models to operate as an algorithm on Algorithmia, while still feeding predictions, input, and other metrics back to the DataRobot MLOps platform —a true best of both worlds. Data Science Expertise Meets Scalability.

Metrics 52

Metrics Deep Learning Modeling Machine Learning 52

Cloudera

FEBRUARY 16, 2022

Each project consists of a declarative series of steps or operations that define the data science workflow. We can think of model lineage as the specific combination of data and transformations on that data that create a model. Each user associated with a project performs work via a session.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

Sisense

SEPTEMBER 3, 2020

Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization. Overall, however, what often characterizes them is a focus on data collection, manipulation, and analysis, using standard formulas and methods, and acting as gatekeepers of an organization’s data.

Statistics 97

Statistics Metrics Visualization Finance 97

IBM Big Data Hub

AUGUST 7, 2023

Therefore, IBM observes that more clients tend to consult AI leaders to help establish governance and enhance AI and data science capabilities, an operating model in the form of co-delivery partnerships. This results in many groups using a large gamut of AI-based tools that are not fully integrated into a cohesive system and platform.

Optimization 55

Optimization Forecasting Data-driven Strategy 55

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. Instead, I built the AIgent.

Modeling 59

Modeling Metadata Publishing Sales 59

Jet Global

AUGUST 14, 2019

And how can the data collected across multiple touchpoints, from retail locations to the supply chain to the factory be easily integrated? Enter data warehousing. So how is the data extracted? By using Structured Query Language, or SQL, the language used to manipulate and extract data stored in cubes.

Enterprise 70

Enterprise OLAP Business Intelligence Cost-Benefit 70

IBM Big Data Hub

AUGUST 28, 2023

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. Information retrieval The first step in the text-mining workflow is information retrieval, which requires data scientists to gather relevant textual data from various sources (e.g., What is text mining?

Data mining 53

Data mining Machine Learning Deep Learning Marketing 53

Insight

JANUARY 14, 2020

In short, I was faced with two major difficulties regarding data collection: I didn’t have nearly enough images, and the images I did have were not representative of a realistic gym environment. We pass 3 parameters: loss, optimizer , and metrics. The documentation for Keras’ metric functions can be found here.

Metrics 58

Metrics Optimization Modeling Testing 58

Jet Global

OCTOBER 2, 2019

And how can the data collected across multiple touchpoints, from retail locations to the supply chain to the factory be easily integrated? Enter data warehousing. So how is the data extracted? By using Structured Query Language, or SQL, the language used to manipulate and extract data stored in cubes.

Enterprise 40

Enterprise OLAP Business Intelligence Cost-Benefit 40

Rocket-Powered Data Science

JULY 6, 2021

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase.

Machine Learning 279

Machine Learning Data-driven IoT Deep Learning 279

O'Reilly on Data

MAY 14, 2020

The foundation of any data product consists of “solid data infrastructure, including data collection, data storage, data pipelines, data preparation, and traditional analytics.” data platform, metrics, ML/AI research, and applied ML). Avinash Kaushik’s Web Analytics 2.0

Management 363

Management Experimentation B2B Machine Learning 363

datapine

AUGUST 29, 2022

Whether you are a complete novice or a seasoned BI professional, you will find here some books on data analytics that will help you cultivate your understanding of this essential field. Before we delve deeper into the best books for data analytics, here are three big data insights to put their relevance and importance into perspective.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

CIO Business Intelligence

JANUARY 20, 2023

The potential use cases for BI extend beyond the typical business performance metrics of improved sales and reduced costs. BI focuses on descriptive analytics, data collection, data storage, knowledge management, and data analysis to evaluate past business data and better understand currently known information.

Business Intelligence 143

Business Intelligence Dashboards Data mining OLAP 143

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. – back to the structure of the dataset. Let’s look through some antidotes. Ergo, less interpretable.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

AWS Big Data

MARCH 26, 2024

The following figure shows some of the metrics derived from the study. In this post, we discuss how you can use purpose-built AWS services to create an end-to-end data strategy for C360 to unify and govern customer data that address these challenges. Organizations using C360 achieved 43.9% reduction in sales cycle duration, 22.8%

Data Strategy 69

Data Strategy Strategy Data Warehouse Prescriptive Analytics 69

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. References.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

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.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

The Unofficial Google Data Science Blog

JULY 18, 2023

Once we’ve answered that, we will then define and use metrics to understand the quality of human-labeled data, along with a measurement framework that we call Cross-replication Reliability or xRR. Last, we’ll provide a case study of how xRR can be used to measure improvements in a data-labeling platform.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

Domino Data Lab

MARCH 3, 2019

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Welcome back to our monthly series about data science! This month, the theme is not specifically about conference summaries; rather, it’s about a set of follow-up surveys from Strata Data attendees.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Domino Data Lab

JUNE 23, 2019

Companies with successful ML projects are often companies that already have an experimental culture in place as well as analytics that enable them to learn from data. Ensure that product managers work on projects that matter to the business and/or are aligned to strategic company metrics. That’s another pattern.

Management 110

Management Machine Learning Experimentation Metrics 110

CIO Business Intelligence

AUGUST 15, 2023

Whether a project aims to improve suicide prevention using data science or to create new revenue streams by reimagining an organization’s core business, CIO 100 Award winners demonstrate the innovative spirit of today’s IT in the face of rapidly evolving organizational challenges.

IT 96

IT Manufacturing IoT Cost-Benefit 96

IBM Big Data Hub

NOVEMBER 14, 2023

Data products and data mesh Data products are assembled data from sources that can serve a set of functional needs that can be packaged into a consumable unit. Each data product has its own lifecycle environment where its data and AI assets are managed in their product-specific data lakehouse.

Strategy 105

Strategy Cost-Benefit Data-driven Measurement 105

Andrew White

JANUARY 9, 2022

This research does not tell you where to do the work; it is meant to provide the questions to ask in order to work out where to target the work, spanning reporting/analytics (classic), advanced analytics and data science (lab), data management and infrastructure, and D&A governance. We write about data and analytics.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Birst BI

JUNE 18, 2018

Read Gartner’s latest research, “Doing Machine Learning Without Hiring (More) Data Scientists,” which carves out four recommendations for organizations wishing to launch data science initiatives. We rely on data and analytics to assist in all three areas.

Slice and Dice 76

Slice and Dice Machine Learning Sales Data-driven 76

datapine

OCTOBER 14, 2020

An interactive dashboard is a data management tool that tracks, analyzes, monitors, and visually displays key business metrics while allowing users to interact with data, enabling them to make well-informed, data-driven, and healthy business decisions. Benefit from amazing interactive dashboards! 4) Cross Tab Filters.

Dashboards 235

Dashboards Interactive Reporting KPI 235

bridgei2i

JANUARY 25, 2022

I’ve worked on projects without MLOps and I had to like, always go back and forth between the engineering and data science teams explaining them about each other’s requirements, because both of them understood so little about each other’s jobs. Personally, you know, I can relate to this so much. Kishore: Hmm.

Machine Learning 52

Machine Learning Modeling Data Processing Enterprise 52

AWS Big Data

FEBRUARY 21, 2023

Regardless of the division or use case it is related to, dimensional data models can be used to store data obtained from tracking various processes like patient encounters, provider practice metrics, aftercare surveys, and more. Amazon Redshift RA3 instances and Amazon Redshift Serverless are perfect choices for a data vault.

Data Warehouse 65

Data Warehouse Data Lake Cost-Benefit Metadata 65

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,

Statistics 59

Statistics Machine Learning Testing Modeling 59

Cloudera

JANUARY 11, 2018

Cloudera often partners with these companies to define the desired path, set reasonable milestones and success metrics, and aide solution design. “Bringing together as much of this data and information as possible will help organizations gain a richer, more detailed picture of customers. ” And teases at the reward.

Strategy 40

Strategy Data Warehouse Cost-Benefit Marketing 40

Rocket-Powered Data Science

JULY 21, 2023

Here are four specific metrics from the report, highlighting the potentially huge enterprise system benefits coming from implementing Splunk’s observability and monitoring products and services: Four times as many leaders who implement observability strategies resolve unplanned downtime in just minutes, not hours or days.

Machine Learning 190

Machine Learning Metrics Reporting Internet of Things 190

datapine

JANUARY 31, 2022

Although the oil company has been producing massive amounts of data for a long time, with the rise of new cloud-based technologies and data becoming more and more relevant in business contexts, they needed a way to manage their information at an enterprise level and keep up with the new skills in the data industry.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Domino Data Lab

AUGUST 1, 2021

Because of its architecture, intrinsically explainable ANNs can be optimised not just on its prediction performance, but also on its explainability metric. Instead, you should focus on how techniques like PDPs and LIME can be used to gain insights into the model’s inner workings and how you can add those to your data science toolbox.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139