IBM Big Data Hub

MARCH 7, 2023

From the moment of birth to discharge, healthcare professionals can collect so much data about an infant’s vitals—for instance, heartbeat frequency or every rise and drop in blood oxygen level. The worldwide statistics on premature births are staggering— the University of Oxford estimates that neonatal sepsis causes 2.5

Risk 52

Risk Technology Informatics Predictive Modeling 52

Sisense

APRIL 10, 2020

R is a tool built by statisticians mainly for mathematics, statistics, research, and data analysis. These visualizations are useful for helping people visualize and understand trends , outliers, and patterns in data. These libraries are used for data collection, analysis, data mining, visualizations, and ML modeling.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 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 52

Data Governance Marketing Machine Learning Sales 52

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

Domino Data Lab

JANUARY 21, 2021

Producing insights from raw data is a time-consuming process. Predictive modeling efforts rely on dataset profiles , whether consisting of summary statistics or descriptive charts. The Importance of Exploratory Analytics in the Data Science Lifecycle. imputation of missing values). There is no clear end state.

Statistics 106

Statistics Unstructured Data Data Science Predictive Modeling 106

DataRobot Blog

DECEMBER 1, 2022

Artificial intelligence (AI) can help improve the response rate on your coupon offers by letting you consider the unique characteristics and wide array of data collected online and offline of each customer and presenting them with the most attractive offers. Training and Testing Different AI Models.

Machine Learning 52

Machine Learning Marketing Modeling Statistics 52

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

BI-Survey

NOVEMBER 24, 2020

It not only increases the speed and transparency of decisions and their quality, but it is also the foundation for the use of predictive planning and forecasting powered by statistical methods and machine learning. Faster information, digital change and data quality are the greatest challenges.

Forecasting 94

Forecasting Predictive Modeling Statistics Data-driven 94

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. In the real world, things are never this easy, and it is a battle to get the data into the correct shape as we will see later in this chapter. 05) in predicting changes in attendance. The R-squared value of.282

Statistics 59

Statistics Machine Learning Testing Modeling 59

Darkhorse

JULY 6, 2017

So what is data wrangling? Let’s imagine the process of building a data lake. Let’s further pretend you’re starting out with the aim of doing a big predictive modeling thing using machine learning. First off, data wrangling is gathering the appropriate data. I hope you enjoy that sort of thing.

Data Lake 74

Data Lake Sales Machine Learning Visualization 74

Domino Data Lab

AUGUST 20, 2019

Real-world datasets can be missing values due to the difficulty of collecting complete datasets and because of errors in the data collection process. Recentering the data means that we translate the values so that the extremes are different and the intermediate values are moved in some consistent way. Discretization.

Testing 68

Testing Modeling Interactive Measurement 68

O'Reilly on Data

MARCH 24, 2020

Beyond the autonomous driving example described, the “garbage in” side of the equation can take many forms—for example, incorrectly entered data, poorly packaged data, and data collected incorrectly, more of which we’ll address below. The model and the data specification become more important than the code.

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346

Jet Global

MAY 1, 2023

Let’s just give our customers access to the data. You’ve settled for becoming a data collection tool rather than adding value to your product. While data exports may satisfy a portion of your customers, there will be many who simply want reports and insights that are available “out of the box.”

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52