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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. The request model started to fray.

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Data Science, Past & Future

Domino Data Lab

how “the business executives who are seeing the value of data science and being model-informed, they are the ones who are doubling down on their bets now, and they’re investing a lot more money.” He was saying this doesn’t belong just in statistics. But for most enterprise, using machine learning…not really.

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Themes and Conferences per Pacoid, Episode 12

Domino Data Lab

Meanwhile, many organizations also struggle with “late in the pipeline issues” on model deployment in production and related compliance. There’s been a flurry of tech startups, open source frameworks, enterprise products, etc., then building machine learning models to recommend methods and potential collaborators to scientists.

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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. Also, clearly there’s no “one size fits all” educational model for data science. The Berkeley model addresses large university needs in the US.