IBM Big Data Hub

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

Machine Learning 55

Machine Learning Data Science Statistics Deep Learning 55

IBM Big Data Hub

JUNE 28, 2023

We are at an inflection point, where we have witnessed 100,000-fold reduction in cost since the human genome was first sequenced in 2001. Today, the rate of data volume increase is similar to the rate of decrease in sequencing cost. gene expression; microbiome data) and any tabular data (e.g.,

Informatics 63

Informatics Consulting Cost-Benefit Data Architecture 63

Alation

MARCH 29, 2022

The request model started to fray. As Business Objects founder Bernard Liautaud notes in e-Business Intelligence: Turning Information Into Knowledge Into Profit (McGraw-Hill, 2001), the lack of ad hoc data access causes IT staff to drown in requests. The Emergence of Self-Service BI.

Business Intelligence 96

Business Intelligence Reporting Data mining Data-driven 96

AWS Big Data

OCTOBER 11, 2023

In this post, we discuss ways to modernize your legacy, on-premises, real-time analytics architecture to build serverless data analytics solutions on AWS using Amazon Managed Service for Apache Flink. The following diagram provides the high-level architecture of a legacy call center analytics platform.

Management 105

Management Metadata Analytics Dashboards 105

Smart Data Collective

JULY 10, 2019

Big data is changing the nature of healthcare. One of the biggest developments was the implementation of the Medical Information Mart for Intensive Care , which took data from 50,000 patients dating back to 2001. Big data will have an even more profound impact in the near future. These are some of the issues under debate.

Big Data 71

Big Data Measurement Predictive Modeling Manufacturing 71

Domino Data Lab

AUGUST 8, 2019

Across the board, organizations struggle with hiring enough data scientists. Meanwhile, many organizations also struggle with “late in the pipeline issues” on model deployment in production and related compliance. then building machine learning models to recommend methods and potential collaborators to scientists.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

Domino Data Lab

JANUARY 6, 2019

Also, clearly there’s no “one size fits all” educational model for data science. Laura Noren, who runs the Data Science Community Newsletter , presented her NYU postdoc research at JuptyerCon 2018, comparing infrastructure models for data science in research and education. They use data infrastructure at work.

Data Science 56

Data Science Machine Learning Reporting Visualization 56