O'Reilly on Data

DECEMBER 12, 2019

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

Smarten

APRIL 19, 2024

OpenAI – Azure OpenAI as the foundational entity for creating GPT models and is based on Large Language Models (LLM). GPT – Is based on a Large Language Model (LLM). Benefits include customized and optimized models, data, parameters and tuning. Open AI was developed by Microsoft.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

Smarten

AUGUST 5, 2022

Can Predictive Analytics Provide Accurate Results for My Business Without Burdening My Users? If your business is struggling to forecast and predict outcomes and results, your management team is probably considering predictive analytics. Understanding Assisted Predictive Modeling.

Predictive Analytics 52

Predictive Analytics Predictive Modeling Analytics Forecasting 52

CIO Business Intelligence

MAY 9, 2024

Driving business benefits Companies seeking CAIOs are looking to reap myriad benefits from AI adoption, ranging from improved decision-making, to increased efficiency of business processes, higher-quality services, profitability, talent management, customer experience, and innovation.

Risk Management 129

Risk Management Risk Cost-Benefit Technology 129

CIO Business Intelligence

JUNE 14, 2023

The certification focuses on the seven domains of the analytics process: business problem framing, analytics problem framing, data, methodology selection, model building, deployment, and lifecycle management. They can also transform the data, create data models, visualize data, and share assets by using Power BI.

Big Data 126

Big Data Data Analytics Analytics Predictive Modeling 126

O'Reilly on Data

MARCH 24, 2020

In a world focused on buzzword-driven models and algorithms, you’d be forgiven for forgetting about the unreasonable importance of data preparation and quality: your models are only as good as the data you feed them. The model and the data specification become more important than the code.

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346

O'Reilly on Data

MAY 18, 2020

This article answers these questions, based on our combined experience as both a lawyer and a data scientist responding to cybersecurity incidents, crafting legal frameworks to manage the risks of AI, and building sophisticated interpretable models to mitigate risk. Because statistics: Last is the inherently probabilistic nature of ML.

Risk 359

Risk Modeling Data Processing Software 359

datapine

AUGUST 14, 2019

To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations. In the past, data scientists had to rely on powerful computers to manage large volumes of data. perfect for statistical computing and design. Our Top Data Science Tools.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

Smarten

MAY 31, 2022

The technology research firm, Gartner has predicted that, ‘predictive and prescriptive analytics will attract 40% of net new enterprise investment in the overall business intelligence and analytics market.’ It is meant to identify crucial relationships and opportunities and risks and help the organization to accurately predict: Growth.

Predictive Analytics 67

Predictive Analytics Analytics Predictive Modeling Prescriptive Analytics 67

O'Reilly on Data

JUNE 18, 2019

There has been a significant increase in our ability to build complex AI models for predictions, classifications, and various analytics tasks, and there’s an abundance of (fairly easy-to-use) tools that allow data scientists and analysts to provision complex models within days. Data integration and cleaning.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

IBM Big Data Hub

SEPTEMBER 19, 2023

Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. It focuses on data collection and management of large-scale structured and unstructured data for various academic and business applications.

Data Science 85

Data Science Data Analytics Prescriptive Analytics Analytics 85

CIO Business Intelligence

APRIL 22, 2022

Data science is a method for gleaning insights from structured and unstructured data using approaches ranging from statistical analysis to machine learning. For example, data analysts should be on board to investigate the data before presenting it to the team and to maintain data models. What is data science? Data science salaries.

Data Science 139

Data Science Statistics Machine Learning Visualization 139

Smarten

MARCH 13, 2024

If you are an IT professional, a business manager or an executive, you have probably been following the progress of the Citizen Data Scientist movement. For a number of years, Gartner and other technology research and analysis firms have predicted and monitored the growth of this phenomenon. ’ So, how is it going?

Analytics 52

Analytics Predictive Modeling Strategy Enterprise 52

Smarten

APRIL 12, 2023

SnapShot provides an English auto generated interpretation of trends, contributions, anomalies and volatility and enables management by exception by sending alerts via email only when an established target threshold is breached.

Snapshot 52

Snapshot Key Performance Indicator KPI Business Intelligence 52

CIO Business Intelligence

JULY 24, 2023

With our first agreement, we started becoming a technology, data-oriented, and cloud organization,” says Ana Rosa Victoria Bruno, innovation manager at LaLiga, one of the world’s top football leagues, with a worldwide audience of more than 2.8 It has also developed predictive models to detect trends, make predictions, and simulate results.

Broadcasting 96

Broadcasting Recreation/Entertainment Testing Machine Learning 96

Smarten

SEPTEMBER 28, 2022

Predictive Analytics utilizes various techniques including association, correlation, clustering, regression, classification, forecasting and other statistical techniques. Maintenance Management. Crime Type Prediction. Plan and forecast accurately. Product/Service Cross-Selling. Customer Targeting. Marketing Optimization.

Predictive Analytics 52

Predictive Analytics Analytics Predictive Modeling Forecasting 52

BizAcuity

APRIL 1, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. Widely used to discover trends, patterns, check assumptions and spot anomalies or outliers, EDA involves a variety of techniques including statistical analysis, and machine learning to gain a better understanding of data.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

BI-Survey

MARCH 16, 2021

Increasing dynamics demand adjustments to the corporate management process – as well as strategic planning and forecasting – to meet growing requirements. Thus, forecasts are becoming a central management tool, which is why 85 percent of companies regularly create forecasts for the entire company or part of it.

Forecasting 52

Forecasting Management Uncertainty Predictive Modeling 52

BI-Survey

MARCH 16, 2021

Increasing dynamics demand adjustments to the corporate management process – as well as strategic planning and forecasting – to meet growing requirements. Thus, forecasts are becoming a central management tool, which is why 85 percent of companies regularly create forecasts for the entire company or part of it.

Forecasting 52

Forecasting Management Uncertainty Predictive Modeling 52

Domino Data Lab

OCTOBER 7, 2020

Guest Post by Bill Shannon, Founder and Managing Partner of BioRankings. Statistical methods for analyzing this two-dimensional data exist. This statistical test is correct because the data are (presumably) bivariate normal. Statistics developed in the last century are based on probability models (distributions).

Statistics 145

Statistics Testing Predictive Modeling Modeling 145

BizAcuity

JANUARY 18, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. Widely used to discover trends, patterns, check assumptions and spot anomalies or outliers, EDA involves a variety of techniques including statistical analysis, and machine learning to gain a better understanding of data.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

Domino Data Lab

NOVEMBER 17, 2020

Guest Post by Bill Shannon, Founder and Managing Partner of BioRankings. Classical statistics, developed in the 20 th century for small datasets, do not work for data where the number of variables is much larger than the number of samples (Large P Small N, Curse of Dimensionality, or P >> N data). Introduction.

Statistics 131

Statistics Measurement Testing Predictive Modeling 131

CIO Business Intelligence

OCTOBER 13, 2023

Responsibilities include building predictive modeling solutions that address both client and business needs, implementing analytical models alongside other relevant teams, and helping the organization make the transition from traditional software to AI infused software.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

Smarten

JUNE 25, 2018

This article provides a brief explanation of the definition and uses of the Descriptive Statistics algorithms. What is a Descriptive Statistics? Descriptive statistics helps users to describe and understand the features of a specific dataset, by providing short summaries and a graphic depiction of the measured data. 4) Quartile.

Statistics 40

Statistics Enterprise Measurement Business Intelligence 40

CIO Business Intelligence

APRIL 25, 2022

The data science path you ultimately choose will depend on your skillset and interests, but each career path will require some level of programming, data visualization, statistics, and machine learning knowledge and skills. The curriculum of each bootcamp is designed by data scientists and industry hiring managers and partners.

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145

IBM Big Data Hub

JULY 6, 2023

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Ultimately, data science is used in defining new business problems that machine learning techniques and statistical analysis can then help solve.

Machine Learning 60

Machine Learning Data Science Statistics Deep Learning 60

Smarten

DECEMBER 5, 2022

Predictive Algorithms CAN Be Easy for Business Users. When a business or a manager is trying to illustrate the value of augmented analytics to its business users, there is nothing more helpful than providing examples. Descriptive Statistics. Here’s How! Simple Linear Regression. Karl Pearson Correlation. SVM Classification.

Predictive Analytics 52

Predictive Analytics Predictive Modeling Forecasting Testing 52

IBM Big Data Hub

MAY 1, 2023

The early versions of AI were capable of predictive modelling (e.g., The four categories of predictive modelling, robotics, speech and image recognition are collectively known as algorithm-based AI or Discriminative AI. List down your ideas for how AI can improve the way insurance is managed. Ignoring AI is costly.

Insurance 85

Insurance Predictive Modeling Cost-Benefit Statistics 85

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 85

Data Analytics Software Analytics Statistics 85

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

Others argue that there will still be a unique role for the data scientist to deal with ambiguous objectives, messy data, and knowing the limits of any given model. Nor can we learn prediction intervals across a large set of parallel time series, since we are trying to generate intervals for a single global time series.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

CIO Business Intelligence

JULY 26, 2023

Empowering agents with data Re/Max’s Ligon, who previously served as CIO of Prudential Real Estate and Berkshire Hathaway Home Services, oversees a cloud estate that includes Oracle Financials, Personify for membership management, and Inside Real Estate, a third-party industry SaaS platform tailored for brokers and agents.

Data Lake 98

Data Lake Digital Transformation Machine Learning Data Architecture 98

Business Over Broadway

SEPTEMBER 27, 2018

In this scenario, marketing analytics can only be conducted within one data silo at a time, decreasing your model’s predictive power / increasing your model’s error. Analytics in these types of projects may be less valuable due to lack of generalizability (to the other customers) and poor models (e.g.,

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

CIO Business Intelligence

JULY 28, 2022

Data scientists have extensive academic backgrounds — often in computer science, statistics, and mathematics. They specialize in building powerful algorithms, and analyzing, processing, and modeling data so they can then interpret the results to create actionable plans. These people are making up a data science support system.

Data Science 143

Data Science IT Machine Learning Data-driven 143

Jedox

MAY 17, 2022

Financial planners , Chief Financial Officers, and analysts have all struggled to build accurate methods for predicting what’s likely to happen. Prior to the dawn of advanced statistical analysis and machine learning, predictive analytics efforts fell into 4 broad categories: Guessing , which is the default that most people revert to.

Predictive Analytics 83

Predictive Analytics Finance Forecasting Analytics 83

Domino Data Lab

AUGUST 22, 2019

The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. While the field of computational linguistics, or Natural Language Processing (NLP), has been around for decades, the increased interest in and use of deep learning models has also propelled applications of NLP forward within industry.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

BI-Survey

NOVEMBER 24, 2020

The solid integration of corporate planning and its integration with analytics is the basis of modern, data-driven corporate management. Predictive models are designed to improve the results of planning as well as the planning processes themselves to achieve meaningful results more quickly.

Forecasting 94

Forecasting Predictive Modeling Statistics Data-driven 94

BI-Survey

JANUARY 29, 2023

In the context of corporate planning, predictive planning and forecasting, it is therefore a major trend to use predictive models based on statistical methods and ML for forecasting and thorough analysis. Convince managers of the potential of the approach. This avoids disappointment and frustration.

Forecasting 52

Forecasting Predictive Modeling Measurement Finance 52