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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Ultimately, data science is used in defining new business problems that machine learning techniques and statistical analysis can then help solve.

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Self-Service BI vs Traditional BI: What’s Next?

Alation

The 1980s ushered in the antithesis of this version of computing — personal computing and distributed database management — but also introduced duplicated data and enterprise data silos. This led to the birth of separate systems for reporting: the enterprise data warehouse.

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Data Science, Past & Future

Domino Data Lab

He was saying this doesn’t belong just in statistics. It involved a lot of work with applied math, some depth in statistics and visualization, and also a lot of communication skills. But for most enterprise, using machine learning…not really. Tukey did this paper. It’s a great read. ” But that changed.

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Themes and Conferences per Pacoid, Episode 12

Domino Data Lab

There’s been a flurry of tech startups, open source frameworks, enterprise products, etc., Consider the following timeline: 2001 – Physics grad students are getting hired in quantity by hedge funds to work on Wall St. aiming at tools for solving problems best characterized as skills gaps and company culture disconnects.

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Themes and Conferences per Pacoid, Episode 5

Domino Data Lab

The program supports hands-on data science class sizes of more than 1,200 students based on JupyterHub , and this sets a bar for enterprise infrastructure. This is also the case for a large segment of enterprise which must take competition and security concerns seriously. UC Berkeley intro data science course (credit: Fernando Pérez ).