IBM Big Data Hub

JULY 6, 2023

.” “Data science” was first used as an independent discipline in 2001. The fields have evolved such that to work as a data analyst who views, manages and accesses data, you need to know Structured Query Language (SQL) as well as math, statistics, data visualization (to present the results to stakeholders) and data mining.

Machine Learning 60

Machine Learning Data Science Statistics Deep Learning 60

Domino Data Lab

MAY 20, 2021

The problem with this approach is that in highly imbalanced sets it can easily lead to a situation where most of the data has to be discarded, and it has been firmly established that when it comes to machine learning data should not be easily thrown out (Banko and Brill, 2001; Halevy et al., Chawla et al. References. link] Chawla, N.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Domino Data Lab

AUGUST 8, 2019

He’s been out of Wolfram for a while and writing exquisite science books including Elements: A Visual Explanation of Every Known Atom in the Universe and Molecules: The Architecture of Everything. Consider the following timeline: 2001 – Physics grad students are getting hired in quantity by hedge funds to work on Wall St.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

O'Reilly on Data

MAY 27, 2020

Each of the classroom’s library books has a color coded sticker on its spine reflecting its Lexile score—a visual announcement of its official complexity level, and thus of which students might be officially ready to read it. This whole scoring system also changes the story about who librarians and teachers are.

Risk 355

Risk Testing Reporting Measurement 355

Domino Data Lab

APRIL 3, 2019

Also, while surveying the literature two key drivers stood out: Risk management is the thin-edge-of-the-wedge ?for My read of that narrative arc is that some truly weird tensions showed up circa 2001: Arguably, it’s the heyday of DW+BI. A very big mess since circa 2001, and now becoming quite a dangerous mess. a second priority?at

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

Domino Data Lab

JULY 22, 2019

He also really informed a lot of the early thinking about data visualization. It involved a lot of work with applied math, some depth in statistics and visualization, and also a lot of communication skills. You see these drivers involving risk and cost, but also opportunity. I can point to the year 2001. All righty.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Domino Data Lab

JANUARY 6, 2019

What are the projected risks for companies that fall behind for internal training in data science? In terms of teaching and learning data science, Project Jupyter is probably the biggest news over the past decade – even though Jupyter’s origins go back to 2001! Data visualization for prediction accuracy ( credit: R2D3 ).

Data Science 56

Data Science Machine Learning Reporting Visualization 56