O'Reilly on Data

DECEMBER 12, 2019

Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1] 1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. That’s where model debugging comes in. Interpretable ML models and explainable ML.

Machine Learning 285

Machine Learning Modeling Testing Risk Management 285

datapine

MARCH 25, 2022

While some experts try to underline that BA focuses, also, on predictive modeling and advanced statistics to evaluate what will happen in the future, BI is more focused on the present moment of data, making the decision based on current insights. What Is Business Intelligence And Analytics? Usage in a business context.

Business Intelligence 264

Business Intelligence Analytics Statistics Dashboards 264

Rocket-Powered Data Science

OCTOBER 6, 2023

What is the point of those obvious statistical inferences? The point is that the 100% association between the event and the preceding condition has no special predictive or prescriptive power. How do predictive and prescriptive analytics fit into this statistical framework?

Business Analytics 157

Business Analytics Prescriptive Analytics Analytics Statistics 157

CIO Business Intelligence

JUNE 7, 2022

The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance.

Data Analytics 135

Data Analytics Diagnostic Analytics Management Analytics 135

Smart Data Collective

MAY 23, 2021

While this can be classed as data science, one difference is that data science tends to use a predictive model to make its analysis, while AI can be capable of analyzing based on learned knowledge and facts. Data science covers a broad range of techniques, including statistics, design and development. Data science.

Data Science 118

Data Science Cost-Benefit Statistics Predictive Modeling 118

Domino Data Lab

JULY 28, 2021

Through a marriage of traditional statistics with fast-paced, code-first computer science doctrine and business acumen, data science teams can solve problems with more accuracy and precision than ever before, especially when combined with soft skills in creativity and communication. Math and Statistics Expertise.

Data Science 110

Data Science Statistics Predictive Analytics Recreation/Entertainment 110

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

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. With those stakes and the long forecast horizon, we do not rely on a single statistical model based on historical trends.

Forecasting 115

Forecasting Data Science Statistics Uncertainty 115

CIO Business Intelligence

MAY 9, 2024

Companies want candidates who can drive innovation, deliver meaningful business results, and work closely with other leaders to manage risks. And they must develop and upskill talent to ensure the workforce is well-versed in the innovation and risk associated with AI use. The same can be said for AI talent in general, Daly stresses.

Risk Management 138

Risk Management Cost-Benefit Risk Technology 138

Smarten

JULY 30, 2024

Assisted Predictive Modeling Enables Business Users to Predict Results with Easy-to-Use Tools! Gartner predicted that, ‘75% of organizations will have deployed multiple data hubs to drive mission-critical data and analytics sharing and governance.’ That’s why your business needs predictive analytics.

Predictive Analytics 62

Predictive Analytics Prescriptive Analytics Predictive Modeling Analytics 62

Domino Data Lab

JANUARY 21, 2021

Predictive modeling efforts rely on dataset profiles , whether consisting of summary statistics or descriptive charts. Results become the basis for understanding the solution space (or, ‘the realm of the possible’) for a given modeling task. Each dataset has properties that warrant producing specific statistics or charts.

Statistics 106

Statistics Unstructured Data Data Science Visualization 106

IBM Big Data Hub

JANUARY 25, 2024

The math demonstrates a powerful truth All predictive models, including AI, are more accurate when they incorporate diverse human intelligence and experience. Consider the diversity prediction theorem. .” So, it’s not just volume, but diversity that improves predictions. How are you assessing fairness?

Predictive Modeling 83

Predictive Modeling Risk Modeling Statistics 83

Business Over Broadway

SEPTEMBER 27, 2018

pharmacogenomics) and risk assessment of genetic disorders (e.g., Analytics applied to these types of data help you generate better predictive models because your integrated data contain all the key variables that are useful in predicting customer churn. Machine Learning and Predictive Modeling of Customer Churn.

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

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.

Statistics 40

Statistics Enterprise Measurement Business Intelligence 40

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

datapine

DECEMBER 4, 2019

Applied to business, it is used to analyze current and historical data in order to better understand customers, products, and partners and to identify potential risks and opportunities for a company. The commercial use of predictive analytics is a relatively new thing. Last but not least, there is the human factor again.

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212

Birst BI

OCTOBER 30, 2018

A personal crystal ball that predicts your days ahead is what financial services firms everywhere want. Every day, these companies pose questions such as: Will this new client provide a good return on investment, relative to the potential risk? Is this existing client a termination risk? Will this next trade return a profit?

Predictive Analytics 73

Predictive Analytics Descriptive Analytics Analytics Metrics 73

AWS Big Data

DECEMBER 19, 2024

In a decentralized data mesh environment, there is a risk of service teams creating resources in service accounts they are not authorized to manage, which may lead to governance issues and data mismanagement.

Data Governance 52

Data Governance Publishing Data-driven Metadata 52

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

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. recommending similar Netflix shows based on your previous choices) or robotics (e.g.,

Insurance 75

Insurance Predictive Modeling Cost-Benefit Statistics 75

Smarten

AUGUST 21, 2019

Assisted Predictive Modeling – These tools enable the average business user to leverage sophisticated predictive algorithms without requiring statistical or data science skills. Users can highlight trends and patterns, test hypotheses and theories to reduce business risk, and easily predict and forecast results.

Analytics 51

Analytics Predictive Modeling Visualization Statistics 51

BizAcuity

APRIL 1, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. In fact, online casinos as an industry carries the biggest risk of money laundering. For predictive analytics to deliver high accuracy, a lot depends on the combination of domain knowledge and technical expertise.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

BizAcuity

JANUARY 18, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. In fact, online casinos as an industry carries the biggest risk of money laundering. Predictive Analytics. Predictive Analytics can help businesses in reducing risk (eg.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

David Menninger's Analyst Perspectives

NOVEMBER 7, 2024

A major practical benefit of using AI is putting predictive analytics within easy reach of any organization. Predictive analytics applies machine learning to statistical modeling and historical data to make predictions about future outcomes.

Forecasting 130

Forecasting Machine Learning Finance Predictive Analytics 130

Smarten

SEPTEMBER 28, 2022

Predictive Analytics utilizes various techniques including association, correlation, clustering, regression, classification, forecasting and other statistical techniques. Businesses must control quality or risk losing customers and market share and exposing the enterprise to legal risk and liability. Quality Control.

Predictive Analytics 52

Predictive Analytics Analytics Predictive Modeling Forecasting 52

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 54

Machine Learning Data Science Statistics Deep Learning 54

BI-Survey

MARCH 16, 2021

To achieve this, many organizations strive for a more intensive use of predictive algorithms, statistical methods and machine learning (ML) models. 75 percent of companies confirm that predictive models provide good forecasts for them, even in volatile markets.

Forecasting 52

Forecasting Management Uncertainty Predictive Modeling 52

BI-Survey

MARCH 16, 2021

To achieve this, many organizations strive for a more intensive use of predictive algorithms, statistical methods and machine learning (ML) models. 75 percent of companies confirm that predictive models provide good forecasts for them, even in volatile markets.

Forecasting 52

Forecasting Management Uncertainty Predictive Modeling 52

IBM Big Data Hub

DECEMBER 20, 2023

Instead of using explicit instructions for performance optimization, ML models rely on algorithms and statistical models that deploy tasks based on data patterns and inferences. In other words, ML leverages input data to predict outputs, continuously updating outputs as new data becomes available. temperature, salary).

Machine Learning 73

Machine Learning Modeling Deep Learning Predictive Modeling 73

Domino Data Lab

APRIL 21, 2021

Rules-based fraud detection (top) vs. classification decision tree-based detection (bottom): The risk scoring in the former model is calculated using policy-based, manually crafted rules and their corresponding weights. Let’s also look at the basic descriptive statistics for all attributes. 3f" % x) dataDF.describe().

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Smarten

JUNE 28, 2018

Business Problem: A bank wants to group loan applicants into high/medium/low risk based on attributes such as loan amount, monthly installments, employment tenure, the number of times the applicant has been delinquent in other payments, annual income, debt to income ratio etc. Use Case – 1. Use Case – 2.

IT 40

IT Business Intelligence Predictive Modeling Statistics 40

Smarten

JUNE 27, 2018

For example, if an outlier indicates a risk or a mistake, that outlier should be identified and the risk or mistake should be addressed. All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

IT 40

IT Business Intelligence Predictive Modeling Statistics 40

Smarten

JUNE 25, 2018

Loan applicants in a bank might be grouped as low, medium, and high risk applicants based on applicant age, annual income, employment tenure, loan amount, the number of times a payment is delinquent etc. About Smarten.

Enterprise 40

Enterprise IT Business Intelligence Predictive Modeling 40

Smarten

JUNE 29, 2018

Based on these labels, the bank can easily make a decision on whether to give a loan to an applicant and the credit limit and interest rate each applicant is eligible for based on the amount of risk involved. Use Case – 2.

IT 40

IT Business Intelligence Predictive Modeling Statistics 40

Smarten

JULY 10, 2018

Use Case(s): Predict if loan default based on attributes of applicant; predict likelihood of successful treatment of new patient based on patient attributes and more. Descriptive Statistics: What is Descriptive Statistics and How Do You Choose the Right One for Enterprise Analysis?

Analytics 40

Analytics Forecasting Statistics Sales 40

DataRobot Blog

DECEMBER 1, 2022

Training and Testing Different AI Models. As DataRobot starts building predictive models, a large repository of open source and proprietary packages will experiment with various modeling techniques. The model selection process will test several models to see which one is likely to yield the best results.

Machine Learning 52

Machine Learning Statistics Marketing Sales 52

Smarten

JUNE 27, 2018

Based on these labels, the bank can easily make a decision on whether to give loan to an applicant and how much credit to extend, as well as the interest rate each applicant is eligible for based on the amount of risk involved. Use Case – 2.

Business Analytics 40

Business Analytics Analytics IT Business Intelligence 40

Smarten

JUNE 29, 2018

Based on these labels, the bank can easily make a decision on whether to give a loan to an applicant and the credit limit and interest rate for each applicant, based on the amount of risk. Use Case – 2.

Enterprise 40

Enterprise Business Intelligence Predictive Modeling Statistics 40