Smarten

APRIL 26, 2022

Now, consider the relevance of the knowledge gained from a degree achieved in 2000 in an IT related discipline. In an era of smart phones, apps, machine learning, natural language processing, DevOPs, AI and GPS, how can one compete? How much has changed since then? Consider how quickly the development environment has changed.

Technology 67

Technology Software Enterprise Machine Learning 67

IBM Big Data Hub

JANUARY 10, 2023

Leveraging data to replace the ‘gut feel’ on which too many business decisions are made enables change practitioners to separate perceptions from reality and decide which processes need the most focus. This Index proved that in almost 2000 companies, organizational health is closely linked to performance.

Management 52

Management Data-driven Data mining Business Objectives 52

Peter James Thomas

AUGUST 18, 2018

That was the Science, here comes the Technology… A Brief Hydrology of Data Lakes. One of the early promises of a Data Lake approach was that – once all relevant data had been ingested – this would be directly leveraged by Data Scientists to derive insight.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

Rita Sallam

SEPTEMBER 11, 2020

The United States grew the least at only 2% from 2000 to 2016. Don’t miss the Data Science and Machine Learning Bake-Off on September 15th at 1:pm EDT. In 2016, life expectancy in the United States (78.5) was almost 4yrs less than the average of all other countries combined (82).

Statistics 96

Statistics Machine Learning Sales Data Science 96

Insight

MARCH 27, 2019

Even after many experiments, a machine learning (ML) engineer may still not discover the best option. Want to advance your career in Data Science and Artificial Intelligence? Learn more about the Artificial Intelligence program at Insight! Discovering the proper method requires time-consuming experimentation.

Optimization 75

Optimization Cost-Benefit Modeling Strategy 75

Domino Data Lab

MAY 20, 2021

Machine Learning algorithms often need to handle highly-imbalanced datasets. The dataset and code used in this blog post are available at [link] and all results shown here are fully reproducible, thanks to the Domino reproducibility engine, which is part of the Domino Data Science platform. Machine Learning, 57–78.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Cloudera

JUNE 25, 2019

On June 18th, Cloudera provided an exclusive preview of these capabilities, and more, with the introduction of Cloudera Data Platform (CDP), the industry’s first enterprise data cloud. Over 2000 customers and partners joined us in this live webinar featuring a first-look at our upcoming cloud-native CDP services.

Enterprise 89

Enterprise Machine Learning Recreation/Entertainment IoT 89

CIO Business Intelligence

JULY 24, 2023

“Growth, profitability, productivity, and engagement are all now part of the CIO role that 20 years ago was all about internal operations,” says Herlihy, who has been an IT executive since landing his first CIO position in 2000.

IT 94

IT Sales Business Objectives Marketing 94

Sisense

FEBRUARY 24, 2021

In fact, in a 2019 edition of Industrial Management & Data Systems, a research team led by Yu Nie noted that prior to the year 2000, there were only six chief data officers in the world. Clearly, data is becoming more important to organizations. The role of the chief data officer.

Data Warehouse 69

Data Warehouse Big Data Metrics Visualization 69

Domino Data Lab

DECEMBER 2, 2020

DBSCAN is implemented in the popular Python machine learning library Scikit-Learn , and because this implementation is scalable and well-tested, I will be using it to demonstrate how DBSCAN works in practice. from sklearn.datasets import make_moons #moons_X: Data, moon_y: Labels moons_X, moon_y = make_moons(n_samples = 2000).

Metrics 116

Metrics KDD Testing Machine Learning 116

Domino Data Lab

AUGUST 29, 2019

It also covers using Keras to construct a deep Q-learning network that learns within a simulated video game environment. Recent feats in machine learning, like developing a program to defeat a human in a game Go, have been powered by reinforcement learning. Essential Theory of Reinforcement Learning.

Optimization 111

Optimization Modeling Deep Learning Metrics 111

The Unofficial Google Data Science Blog

APRIL 23, 2024

Biostatistics, 1(1):27-34, 03 2000. [2] Journal of Machine Learning Research, 17(83):1–5, 2016. [23] If you do try this out, please let us know how it goes in the comments. References [1] Andrew Gelman. Should we take measurements at an intermediate design point? 2] Nist / sematech. R package version 1.0.1. [22]

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Peter James Thomas

AUGUST 30, 2018

Based on Kaggle’s State of Data Science Survey 2017 (Sample size: 10,153). The text in the above exhibit is not that clear [2] , so here are the 20 top challenges [3] faced by those running Data Science teams in human-readable form: #. Dirty Data. Lack of Data Science talent in the organization.

Data Science 75

Data Science Data Quality Machine Learning Business Intelligence 75

Jet Global

MAY 9, 2024

Data pipelines are designed to automate the flow of data, enabling efficient and reliable data movement for various purposes, such as data analytics, reporting, or integration with other systems. For example, migrating customer data from an on-premises database to a cloud-based CRM system. What is an ETL pipeline?

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Domino Data Lab

SEPTEMBER 9, 2019

Data science teams in industry must work with lots of text, one of the top four categories of data used in machine learning. That’s excellent for supporting really interesting workflow integrations in data science work. Usually it’s human-generated text, but not always.

Deep Learning 110

Deep Learning Machine Learning Data Science Visualization 110

Jet Global

SEPTEMBER 1, 2023

Predictive analytics refers to the use of historical data, machine learning, and artificial intelligence to predict what will happen in the future. Enhanced Skill Development: Building your own software allows your application team to develop new skills in data science, machine learning, and analytics.

Predictive Analytics 52

Predictive Analytics Risk Analytics Software 52