O'Reilly on Data

JUNE 18, 2019

Depending completely on human labeling for these examples is simply a non-starter; as ML models get more complex and the underlying data sources get larger, the need for more data increases, the scale of which cannot be achieved by human experts. Ihab Ilyas on “Why data preparation frameworks rely on human-in-the-loop systems”.

Machine Learning 279

Machine Learning Data Quality Statistics Modeling 279

CIO Business Intelligence

JULY 5, 2022

Data analytics is used across disciplines to find trends and solve problems using data mining , data cleansing, data transformation, data modeling, and more. Whereas BI studies historical data to guide business decision-making, business analytics is about looking forward.

Business Analytics 136

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 136

CIO Business Intelligence

OCTOBER 2, 2024

Smart cities in the age of AI harness AI’s ability to analyze vast data streams, enabling intelligent decision-making and efficient resource management. Raw data collected through IoT devices and networks serves as the foundation for urban intelligence. Then there are advanced connectivity solutions.

IoT 123

IoT Internet of Things Predictive Modeling Data-driven 123

BizAcuity

NOVEMBER 13, 2024

As businesses increasingly rely on data for competitive advantage, understanding how business intelligence consulting services foster data-driven decisions is essential for sustainable growth. Business intelligence consulting services offer expertise and guidance to help organizations harness data effectively.

Consulting 52

Consulting Data-driven Cost-Benefit Business Intelligence 52

CIO Business Intelligence

OCTOBER 4, 2022

In the new report, titled “Digital Transformation, Data Architecture, and Legacy Systems,” researchers defined a range of measures of what they summed up as “data architecture coherence.” more machine learning use casesacross the company.

Machine Learning 118

Machine Learning Data Architecture Uncertainty Data-driven 118

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.

Data Analytics 105

Data Analytics Software Analytics Statistics 105

DataRobot

APRIL 27, 2021

These automation specialists are now upskilling in data science , leveraging DataRobot, to rapidly build highly predictive models, in a no-code environment, and embed them into their automation workflows. It forces banks to spend time chasing false positives and hunting for investigators’ notes.

Machine Learning 98

Machine Learning Cost-Benefit Predictive Modeling Data Science 98

Smarten

NOVEMBER 22, 2019

Data literacy focuses on encouraging and nurturing data competencies and making your team members comfortable with the use of analytical tools, technology solutions and data comprehension and presentation, including a comfort level with data collection and analysis, data sharing and data-driven business decisions.

Predictive Modeling 69

Predictive Modeling Data-driven Visualization Forecasting 69

CIO Business Intelligence

APRIL 22, 2022

For most organizations, it is employed to transform data into value in the form of improved revenue, reduced costs, business agility, improved customer experience, the development of new products, and the like. Data science gives the data collected by an organization a purpose. Data science vs. data analytics.

Data Science 140

Data Science Statistics Machine Learning Visualization 140

BI-Survey

NOVEMBER 24, 2020

Automation is accompanied by the use of – and investment in – modern technology and includes potential use cases such as automatic data collection from different data sources, workflows to control processes and process participants and early warning mechanisms based on thresholds.

Forecasting 94

Forecasting Predictive Modeling Statistics Data-driven 94

CIO Business Intelligence

MARCH 19, 2025

For instance, cloud storage strategies can be adjusted to prefer providers with carbon-neutral commitments, and AI model training can be optimized to reduce computational costs. Beyond environmental impact, social considerations should also be incorporated into data strategies.

IT 59

IT Data Governance Data-driven Metrics 59

datapine

JANUARY 31, 2022

They used the data collected to build a logistic-regression and unsupervised learning models, so as to determine the potential relationship between drivers and outcomes. The gathered data includes everything from customers’ waiting times, peak demand hours, traffic for each city, a driver’s speed during a trip, and much more.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

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. Results become the basis for understanding the solution space (or, ‘the realm of the possible’) for a given modeling task.

Statistics 106

Statistics Unstructured Data Data Science Visualization 106

CIO Business Intelligence

JULY 17, 2023

Dickson’s team wrote one AI model, for example, that was released to a small subset of its dealer network “to predict when they could potentially win more business for us both,” he says, adding that Generac’s work with Databricks has furthered the company’s journey into predictive modeling.

IoT 98

IoT Manufacturing Digital Transformation Sales 98

CIO Business Intelligence

APRIL 25, 2022

It’s a fast growing and lucrative career path, with data scientists reporting an average salary of $122,550 per year , according to Glassdoor. Here are the top 15 data science boot camps to help you launch a career in data science, according to reviews and data collected from Switchup. Data Science Dojo.

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145

IBM Big Data Hub

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

Data Science 76

Data Science Data Analytics Prescriptive Analytics Analytics 76

Sisense

APRIL 10, 2020

These libraries are used for data collection, analysis, data mining, visualizations, and ML modeling. There are also a wide array of libraries available for both languages for text processing, text analysis, and text modeling. Python has 200+ standard libraries and nearly infinite third-party libraries.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

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

Cloudera

FEBRUARY 16, 2022

We can think of model lineage as the specific combination of data and transformations on that data that create a model. This maps to the data collection, data engineering, model tuning and model training stages of the data science lifecycle.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

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

IBM Big Data Hub

AUGUST 14, 2023

With ML analytics models, your organization can gain additional insight into user behavior with predictive modeling and baselines of what is normal for a user. UBA’s Machine Learning Analytics add-on extends the capabilities of QRadar by adding use cases for ML analytics.

Cost-Benefit 55

Cost-Benefit Machine Learning Consulting Data mining 55

Jedox

JULY 23, 2019

Once cleansed, its possible to enrich the data. For example a customer name and ID is not as valuable as the customer’s postal code, income bracket or other spending habits which could be enriched from data collected from Google for example. Financial Modeling.

Technology 52

Technology Forecasting Finance Data-driven 52

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 Statistics Marketing Sales 52

CIO Business Intelligence

AUGUST 15, 2023

The Behavioral Health Acuity Risk (BHAR) model leverages a machine learning technique called random forests, which can be natively hosted in the electronic health record and updated in near-real time, with results immediately available to clinical staff.

IT 98

IT Manufacturing IoT Cost-Benefit 98

IBM Big Data Hub

JULY 5, 2023

That data is then fed into AI-enabled CMMS, where advanced data analysis tools and processes like machine learning (ML) spot issues and help resolve them. This information is then used to build predictive models of asset performance over time and help spot potential problems before they arise.

Strategy 52

Strategy Management IoT Cost-Benefit 52

IBM Big Data Hub

AUGUST 28, 2023

Information retrieval The first step in the text-mining workflow is information retrieval, which requires data scientists to gather relevant textual data from various sources (e.g., The data collection process should be tailored to the specific objectives of the analysis.

Data mining 53

Data mining Machine Learning Deep Learning Marketing 53

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

OCTOBER 23, 2019

A further diagnostic step is to plot the predicted values of the linear regression versus the actual values. an lmplot of the predicted and actual is shown, and it is obvious that this isn’t that great a prediction model. With all the data collected, there are some interesting new plots to test out.

Statistics 59

Statistics Machine Learning Testing Modeling 59

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. Some of the benefits of rescaling become more prominent when we move beyond predictive modeling and start making statistical or causal claims. Filling missing values.

Testing 68

Testing Modeling Interactive Measurement 68

Domino Data Lab

AUGUST 1, 2021

The plot below is an example of PDPs that show the impact of changes in features like temperature, humidity, and wind speed on the predicted number of rented bikes. PDPs for the bicycle count prediction model (Molnar, 2009). Creating a PDP for our model is fairly straightforward.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

IBM Big Data Hub

MARCH 7, 2023

At Innocens BV, the belief is that earlier identification of sepsis-related events in newborns is possible, especially given the vast amount of data points collected from the moment a baby is born. Years’ worth of aggregated data in the NICU could help lead us to a solution.

Risk 52

Risk Technology Informatics Predictive Modeling 52

IBM Big Data Hub

FEBRUARY 14, 2024

Banks and other lenders can use ML classification algorithms and predictive models to suggest loan decisions. Many stock market transactions use ML with decades of stock market data to forecast trends and ultimately suggest whether and when to buy or sell.

Cost-Benefit 82

Cost-Benefit Insurance Machine Learning Unstructured Data 82

Jet Global

MAY 9, 2024

Machine Learning Pipelines : These pipelines support the entire lifecycle of a machine learning model, including data ingestion , data preprocessing, model training, evaluation, and deployment. API Data Pipelines : These pipelines retrieve data from various APIs and load it into a database or application for further use.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

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