Domino Data Lab

AUGUST 8, 2019

Paco Nathan ‘s latest monthly article covers Sci Foo as well as why data science leaders should rethink hiring and training priorities for their data science teams. In this episode I’ll cover themes from Sci Foo and important takeaways that data science teams should be tracking. Sci Foo 2019.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

Smart Data Collective

APRIL 14, 2020

The future of data science jobs continues to be brighter than ever in 2020. According to Glassdoor’s list of Best Jobs in America for the past four years, “data scientist” topped in terms of job demand, job satisfaction, and pay with an average base salary of more than $100,000 per year. What about the Ph.D. Academic Degree.

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Smart Data Collective

OCTOBER 14, 2019

Regardless of if you’re a data science professional or an IT department who wants to help your company have more successful data science projects, it’s essential to have some data science tools under your belt to avail of when needed. In short, it makes big data analysis more accessible.

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Data Science Machine Learning Big Data Interactive 68

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. back to the structure of the dataset. Let’s look through some antidotes. Ergo, less interpretable.

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Domino Data Lab

MARCH 3, 2019

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Welcome back to our monthly series about data science! This month, the theme is not specifically about conference summaries; rather, it’s about a set of follow-up surveys from Strata Data attendees.

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O'Reilly on Data

MARCH 20, 2019

Especially in today’s overheated and turnover-prone data “science” market.) Distributed systems and models : For better or worse, we live in the age of big data. Many organizations are now using distributed data processing and machine learning systems. O’Reilly Ideas (2019). DZone (2018). URL: [link].

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O'Reilly on Data

MAY 21, 2019

Thus, many developers will need to curate data, train models, and analyze the results of models. With that said, we are still in a highly empirical era for ML: we need big data, big models, and big compute. A typical data pipeline for machine learning. Key features of many data science platforms.

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Machine Learning Technology Deep Learning Data Science 238

Peter James Thomas

APRIL 9, 2019

Maybe I will do better with 2019’s highlights! Given the advent of the Maths & Science section, there are now seven categories into which I have split articles. These are as follows: General Data Articles. Data Visualisation. Statistics & Data Science. Analytics & Big Data.

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datapine

DECEMBER 4, 2019

Applied to business, it is used to analyze current and historical data in order to better understand customers, products, and partners and to identify potential risks and opportunities for a company. The accuracy of the predictions depends on the data used to create the model. Mobile Analytics.

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datapine

NOVEMBER 27, 2019

Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. The rise of self-service analytics democratized the data product chain. 2019 was a particularly major year for the business intelligence industry. Suddenly advanced analytics wasn’t just for the analysts.

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O'Reilly on Data

JANUARY 25, 2021

Year-over-year (YOY) growth compares January through September 2020 with the same months of 2019. Let’s look at the data, starting at the highest level: O’Reilly online learning itself. Usage of O’Reilly online learning grew steadily in 2020, with 24% growth since 2019. AI, Machine Learning, and Data.

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Andrew White

JANUARY 11, 2021

As such a head of analytics, BI and data science may emerge. Are you anticipating continued separation of “BI/Analytics” teams from “Data Science” teams or are those roles merging in the years ahead? Many data science labs are set up as shared services. That’s the idea.

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