O'Reilly on Data

NOVEMBER 13, 2018

Considerations for a world where ML models are becoming mission critical. In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in New York last September. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations.

Machine Learning 220

Machine Learning Risk Management Statistics 220

CIO Business Intelligence

JANUARY 17, 2024

The first was becoming one of the first research companies to move its panels and surveys online, reducing costs and increasing the speed and scope of data collection. According to Mohammed, the results of this digital transformation journey are measurable and impressive. js and React.js.

Digital Transformation 118

Digital Transformation Dashboards Reporting Data Science 118

Smart Data Collective

DECEMBER 17, 2021

The process of Marketing Analytics consists of data collection, data analysis, and action plan development. Understanding your marketing data to make more informed and successful marketing strategy decisions is a systematic process. Types of Data Used in Marketing Analytics. Preparing the Data for Analysis.

Marketing Analytics 96

Marketing Analytics Marketing Data-driven Analytics 96

O'Reilly on Data

MARCH 31, 2020

All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. After training, the system can make predictions (or deliver other results) based on data it hasn’t seen before. Machine learning adds uncertainty.

Management 332

Management Machine Learning Experimentation Metrics 332

FineReport

MARCH 7, 2024

However, due to regulatory controls on sensitive data like phone numbers and technical challenges in cross-platform integration of Internet and mobile reporting data, our current matching rates are relatively low, reaching around 20% in ideal scenarios, excluding telecom data.

Dashboards 52

Dashboards Reporting Advertising Statistics 52

Rocket-Powered Data Science

JULY 7, 2019

Autonomous Vehicles: Self-driving (guided without a human), informed by data streaming from many sensors (cameras, radar, LIDAR), and makes decisions and actions based on computer vision algorithms (ML and AI models for people, things, traffic signs,…). Examples: Cars, Trucks, Taxis. They cannot process language inputs generally.

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

Smart Data Collective

MARCH 28, 2022

There are four main types of data analytics: Predictive data analytics: It is used to identify various trends, causation, and correlations. It can be further classified as statistical and predictive modeling, but the two are closely associated with each other. Improved decision-making will create more successful outcomes.

Data Analytics 86

Data Analytics Software Analytics Statistics 86

Insight

MARCH 12, 2020

The AIgent was built with BERT, Google’s state-of-the-art language model. In this article, I will discuss the construction of the AIgent, from data collection to model assembly. Data Collection The AIgent leverages book synopses and book metadata. Instead, I built the AIgent. features) and metadata (i.e.

Modeling 59

Modeling Metadata Publishing Sales 59

Sisense

SEPTEMBER 3, 2020

Data scientists usually build models for data-driven decisions asking challenging questions that only complex calculations can try to answer and creating new solutions where necessary. As you can imagine, data scientists with all these aptitudes can be of considerable value to their employers. Business Analyst.

Statistics 97

Statistics Metrics Visualization Finance 97

Smart Data Collective

MAY 4, 2023

Big data has been discussed by business leaders since the 1990s. It refers to datasets too large for normal statistical methods. Professionals have found ways to use big data to transform businesses. Professionals have found ways to use big data to transform businesses. They are especially great for web data mining.

Data mining 79

Data mining Big Data Data-driven Measurement 79

BizAcuity

JULY 7, 2020

Each fragment of the business model needs to be closely observed to ensure that it is equipped with adequate depth in human resources. With respect to developments or changes in inbound markets, gaming data, player statistics, economic recovery speed, and more, adjust and reiterate core strategies. This too shall pass.

Revenue Optimization 98

Revenue Optimization Measurement Interactive Marketing 98

CIO Business Intelligence

FEBRUARY 13, 2023

The name references the Greek letter sigma, which is a statistical symbol that represents a standard deviation. The process aims to bring data and statistics into the mesh to help objectively identify errors and defects that will impact quality. Six Sigma was trademarked by Motorola in 1993.

Management 83

Management Statistics Manufacturing Measurement 83

Smart Data Collective

AUGUST 31, 2020

At Smart Data Collective, we have talked extensively about the benefits of big data in digital marketing. We have focused a lot on using data analytics for SEO. However, there are a lot of other benefits of using big data in marketing. You shouldn’t limit yourself to using data analytics in your SEO strategy.

Data-driven 87

Data-driven Marketing Big Data Broadcasting 87

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. On the flip side, you sometimes have a small volume of data.

Metrics 107

Metrics Measurement Statistics Data Collection 107

Juice Analytics

MARCH 30, 2021

Data analyst —Data authors can’t create great art if they don’t like working with their materials. Data authors need to be comfortable with core statistical concepts and comfortable with manipulating data. Getting involved with deep data analysis can reveal the important messages and accurate ways to convey them.

Cost-Benefit 131

Cost-Benefit Statistics Metrics Dashboards 131

IBM Big Data Hub

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. How the models are stored.

Data Science 56

Data Science Machine Learning Cost-Benefit Deep Learning 56

IBM Big Data Hub

AUGUST 7, 2023

Tackling complexities in clinical trial site selection: A playground for a new technology and AI operating model Enrollment strategists and site performance analysts are responsible for constructing and prioritizing robust end-to-end enrollment strategies tailored to specific trials. To do so they require data, which is in no shortage.

Optimization 58

Optimization Forecasting Data-driven Strategy 58

Domino Data Lab

OCTOBER 2, 2019

As data science work is experimental and probabilistic in nature, data scientists are often faced with making inferences. You’ll measure this effect by looking at a quantity called the average treatment effect (ATE). You can use a regression model. What you really want to measure is the difference in outcomes.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

Depict Data Studio

JULY 28, 2020

School leaders requested a “survey of families and staff to see where their comfort level is with these three models that we’ve developed, what they’re concerned about and their needs are.” The school system was considering three models: 100% in-person, 100% virtual, or a hybrid. The Reporting Model. A hybrid model.

Visualization 102

Visualization Reporting Modeling Statistics 102

Alation

APRIL 25, 2023

As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Using bad data, or the incorrect data can generate devastating results. between 2022 and 2029.

Data Governance 52

Data Governance Marketing Machine Learning Sales 52

The Unofficial Google Data Science Blog

JULY 18, 2023

E ven after we account for disagreement, human ratings may not measure exactly what we want to measure. Overview Human-labeled data is ubiquitous in business and science, and platforms for obtaining data from people have become increasingly common. And for thousands of years, measurement was as simple as this.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

Sisense

OCTOBER 15, 2020

Every data professional knows that ensuring data quality is vital to producing usable query results. Streaming data can be extra challenging in this regard, as it tends to be “dirty,” with new fields that are added without warning and frequent mistakes in the data collection process. Now, let’s connect to Redshift.

Dashboards 98

Dashboards IoT Optimization Internet of Things 98

Alation

APRIL 25, 2023

As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Using bad data, or the incorrect data can generate devastating results. between 2022 and 2029.

Data Governance 40

Data Governance Marketing Machine Learning Sales 40

Occam's Razor

APRIL 8, 2013

We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. First, you figure out what you want to improve; then you create an experiment; then you run the experiment; then you measure the results and decide what to do.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

The Unofficial Google Data Science Blog

APRIL 17, 2017

Selection and aggregation of forecasts from an ensemble of models to produce a final forecast. We conclude with an example of our forecasting routine applied to publicly available Turkish Electricity data. Finally, the time series model may give more accurate forecasts than an explanatory or mixed model.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

Alation

OCTOBER 12, 2022

Companies collect and analyze vast amounts of data to make informed business decisions. From product development to customer satisfaction, nearly every aspect of a business uses data and analytics to measure success and define strategies. Some examples of quantitative data include: Counts or units, stored as raw numbers.

Statistics 52

Statistics Sales Testing Marketing 52

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Introduction. 2018-06-21).

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

erwin

APRIL 21, 2020

By combining physical system catalogs, critical data elements, and key performance measures with clearly defined product and sales goals, you can manage the effectiveness of your business and ensure you understand what critical systems are for business continuity and measuring corporate performance.

Measurement 97

Measurement Sales Data Governance Metadata 97

erwin

APRIL 21, 2020

Data is critical to these decisions, such as how to ramp up and support remote employees, re-engineer processes, change entire business models, and adjust supply chains. So one of the biggest lessons we’re learning from COVID-19 is the need for data collection, management and governance.

Measurement 96

Measurement Sales Data Governance Metadata 96

Domino Data Lab

AUGUST 12, 2021

For data, this refinement includes doing some cleaning and manipulations that provide a better understanding of the information that we are dealing with. In a previous blog , we have covered how Pandas Profiling can supercharge the data exploration required to bring our data into a predictive modelling phase.

Machine Learning 126

Machine Learning Reporting Statistics Visualization 126

Andrew White

JANUARY 9, 2022

Data mesh seems to mean a network of systems and hubs connected to help cope with silos. Perhaps a data mesh is what a pure architecture might design. First, how we measure emissions and carbon footprint is about data design and policy. In other words, D&A plays a key role in the foundational measuring angle.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Domino Data Lab

JUNE 23, 2019

All you need to know, for now, is that machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to learn based on data by being trained on past examples. These measurement-obsessed companies have an advantage when it comes to AI.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

OCTOBER 23, 2019

As a result, there has been a recent explosion in individual statistics that try to measure a player’s impact. This chapter will explore the numbers behind the numbers using ML and then creating an API to serve out the ML model. Eighty percent of this problem is collecting the data and then transforming the data.

Statistics 59

Statistics Machine Learning Testing Modeling 59

Domino Data Lab

AUGUST 20, 2019

Feature engineering is useful for data scientists when assessing tradeoff decisions regarding the impact of their ML models. It is a framework for approaching ML as well as providing techniques for extracting features from raw data that can be used within the models. Feature Engineering Terminology and Motivation.

Testing 68

Testing Modeling Interactive Measurement 68

datapine

JUNE 4, 2020

A CTO report is based on a curated collection of data and statistics that exist within the dashboard and offer a wealth of information based on established KPIs that can be easily automated and shared across the board, in order to monitor relevant IT performance. Besides, these dashboards can also be used as CTO reports.

Dashboards 130

Dashboards Reporting Metrics Visualization 130

O'Reilly on Data

OCTOBER 18, 2022

What if a named entity recognition (NER) system, based on a cutting-edge large language model (LLM), fails for Chinese, Cyrillic, or Arabic text? Audit has been an integral aspect of model risk management (MRM) in consumer finance for years, and colleagues at BLDS and QuantUniversity have been conducting model audits for some time.

Risk Management 291

Risk Management Risk Testing Measurement 291

Jet Global

MAY 15, 2020

A financial Key Performance Indicator (KPI) or metric is a quantifiable measure that a company uses to gauge its financial performance over time. Under modern day reporting standards, companies are formally obligated to present their financial data in the following statements: balance sheet, income statement, and cash flow statement.

Metrics 98

Metrics Finance Reporting KPI 98