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 134

Data Analytics Diagnostic Analytics Management Analytics 134

Smarten

APRIL 19, 2024

Assisted Predictive Modeling and Auto Insights to create predictive models using self-guiding UI wizard and auto-recommendations The Future of AI in Analytics The C=suite executive survey revealed that 93% felt that data strategy is critical to getting value from generative AI, but a full 57% had made no changes to their data.

Analytics 59

Analytics Predictive Modeling KPI Machine Learning 59

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. It helps to automate and makes the usage of the R programming statistical language easier and much more effective. perfect for statistical computing and design.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

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

O'Reilly on Data

JUNE 18, 2019

The good news is that researchers from academia recently managed to leverage that large body of work and combine it with the power of scalable statistical inference for data cleaning. HoloClean adopts the well-known “noisy channel” model to explain how data was generated and how it was “polluted.”

Machine Learning 263

Machine Learning Data Quality Statistics Modeling 263

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

Domino Data Lab

OCTOBER 7, 2020

Statistical methods for analyzing this two-dimensional data exist. This statistical test is correct because the data are (presumably) bivariate normal. When there are many variables the Curse of Dimensionality changes the behavior of data and standard statistical methods give the wrong answers. Data Has Properties.

Statistics 145

Statistics Testing Predictive Modeling Modeling 145

Sisense

APRIL 10, 2020

R is a tool built by statisticians mainly for mathematics, statistics, research, and data analysis. Here, we will implement the XG-Boost algorithm, an algorithm that learns on the basis of training data (which we loaded earlier in both R and Python programming languages) with the help of probability and statistics.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

O'Reilly on Data

MARCH 20, 2019

The objective here is to brainstorm on potential security vulnerabilities and defenses in the context of popular, traditional predictive modeling systems, such as linear and tree-based models trained on static data sets. If an attacker can receive many predictions from your model API or other endpoint (website, app, etc.),

Modeling 225

Modeling Machine Learning Predictive Modeling Consulting 225

IBM Big Data Hub

DECEMBER 20, 2023

ML is a computer science, data science and artificial intelligence (AI) subset that enables systems to learn and improve from data without additional programming interventions. In other words, ML leverages input data to predict outputs, continuously updating outputs as new data becomes available.

Machine Learning 73

Machine Learning Modeling Deep Learning Predictive Modeling 73

O'Reilly on Data

DECEMBER 12, 2019

More structured approaches to sensitivity analysis include: Adversarial example searches : this entails systematically searching for rows of data that evoke strange or striking responses from an ML model. Figure 1 illustrates an example adversarial search for an example credit default ML model.

Machine Learning 196

Machine Learning Modeling Testing Risk Management 196

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. deep learning) there is no guaranteed explainability. It can be implemented as either unsupervised (e.g.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Domino Data Lab

JULY 9, 2019

Diving into examples of building and deploying ML models at The New York Times including the descriptive topic modeling-oriented Readerscope (audience insights engine), a prediction model regarding who was likely to subscribe/cancel their subscription, as well as prescriptive example via recommendations of highly curated editorial content.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

O'Reilly on Data

MARCH 24, 2020

HoloClean decouples the task of data cleaning into error detection (such as recognizing that the location “cicago” is erroneous) and repairing erroneous data (such as changing “cicago” to “Chicago”), and formalizes the fact that “data cleaning is a statistical learning and inference problem.”

Machine Learning 351

Machine Learning Statistics Data Quality Modeling 351