Analytics Vidhya

MARCH 5, 2023

Introduction NumPy is an open-source library in python and a must-learn if you want to enter the data science ecosystem. Array programming, or […] The post Advanced NumPy: Broadcasting and Strides appeared first on Analytics Vidhya.

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Rocket-Powered Data Science

JULY 19, 2023

I recently saw an informal online survey that asked users which types of data (tabular, text, images, or “other”) are being used in their organization’s analytics applications. The results showed that (among those surveyed) approximately 90% of enterprise analytics applications are being built on tabular data.

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David Menninger's Analyst Perspectives

SEPTEMBER 13, 2019

The Oracle Analytics Summit 2019 was the inaugural user event for Oracle Analytics customers, and they also broadcast the video for thousands of others. Executives talked about some big organizational changes, including Bruno Aziza joining last year to lead the analytics organization. You can watch the keynote at [link].

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Smart Data Collective

APRIL 2, 2023

Organizations are looking for consultants who are capable of coming up with custom in-house data science applications. Professionals who have already completed at least some training in the field, such as a data science course in Hyderabad or an apprenticeship in Silicon Valley, are poised to make a big splash in this field.

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IBM Big Data Hub

OCTOBER 4, 2018

The IBM Data Science Elite team is working with Niagara on a model that could predict the risk of film breakage for given equipment settings.

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Analytics Vidhya

OCTOBER 26, 2020

This article was published as a part of the Data Science Blogathon. Introduction “Data is the new oil” ~ that’s no secret and is. appeared first on Analytics Vidhya. The post How Can You Optimize your Spark Jobs and Attain Efficiency – Tips and Tricks!

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Optimization Data Science Publishing Broadcasting 271

The Unofficial Google Data Science Blog

AUGUST 31, 2016

By DAVID ADAMS Since inception, this blog has defined “data science” as inference derived from data too big to fit on a single computer. Thus the ability to manipulate big data is essential to our notion of data science. pt_rdd = parsed_input_rdd. deepcopy(c_centers) b_c_centers = sc. c_centers = [t.

Data Science 66

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