SLIDESHARE . NET {}

Matching Content Categories {📚}

• Technology & Computing
• Virtual Reality
• Science

Content Management System {📝}

What CMS is slideshare.net built with?

Custom-built

No common CMS systems were detected on Slideshare.net, but we identified it was custom coded using Next.js (JavaScript).

Traffic Estimate {📈}

What is the average monthly size of slideshare.net audience?

🌠 Phenomenal Traffic: 5M - 10M visitors per month


Based on our best estimate, this website will receive around 5,000,019 visitors per month in the current month.
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How Does Slideshare.net Make Money? {💸}

Display Ads {🎯}

The website utilizes display ads within its content to generate revenue. Check the next section for further revenue estimates.

Ads are managed by ascendeum.com. Particular relationships are as follows:

Direct Advertisers (58)

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Reseller Advertisers (71)

pubmatic.com, rubiconproject.com, appnexus.com, openx.com, smartadserver.com, sharethrough.com, video.unrulymedia.com, indexexchange.com, triplelift.com, google.com, media.net, lijit.com, yieldmo.com, contextweb.com, sonobi.com, themediagrid.com, onetag.com, smaato.com, xandr.com, freewheel.tv, yahoo.com, risecodes.com, loopme.com, 33across.com, pubnative.net, admanmedia.com, amxrtb.com, rhythmone.com, adform.com, conversantmedia.com, emxdgt.com, aps.amazon.com, trustedstack.com, zetaglobal.net, improvedigital.com, sovrn.com, gumgum.com, ogury.com, inmobi.com, kueez.com, gannett.com, richaudience.com, districtm.io, ottadvisors.com, adyoulike.com, xad.com, iqzone.com, lemmatechnologies.com, imds.tv, advertising.com, ad-generation.jp, beachfront.com, mintegral.com, condenast.com, copper6.com, zeta.com, disqus.com, betweendigital.com, thebrave.io, spotx.tv, spotxchange.com, lunamedia.io, smartyads.com, visiblemeasures.com, krushmedia.com, consumable.com, axonix.com, supply.colossusssp.com, telaria.com, sabio.us, mediafuse.com

How Much Does Slideshare.net Make? {💰}

Display Ads {🎯}

$63,100 per month
Estimations show Slideshare.net's display ad online revenue falls between $42,042 and $115,616 per month.

Keywords {🔍}

data, sri, ambati, hoai, machine, pdf, intelligence, presentation, slides, pptx, software, enterprise, matt, dowle, ajay, ohri, wso, big, starter, support, joys, clean, learning, downloaded, analytics, introduction, level, packages, models, tools, download, lars, albertsson, service, domino, model, agile, multiple, asked, stored, questions, messy, experiences, cleaning, munging, issues, scale, ram, variables, day,

Topics {✒️}

org/web/packages/tidyr/vignettes/tidy-data /finally-tracking-cran-packages-downloads/ https sri ambati keynote machine learning models machine learning platforms sri ambati intra-day auctions select ai machine intelligence big data platform rstudio-cran-logs/ http cran mirror http pptx enginsayin1 data entry errors /cran-download-statistics pitch presentation ai ceo/founder multiple variables data effective companies open source presentation slides slides software read top downloaded packages linux certificate hadley wickham https packages-rstats/ http r-packages-worldmap discussed packages shows /metacran/cranlogs http common issues encountered experiences related content similar serial key [latest] frontline workforce cryotoscmmssoftware 探討 sched_ext 及機器學習 single observational unit 6tb $150k-$200k messy data ai agents highest downloaded package submit search 5tb ram $60k matt dowle matt dowle 1 enterprise clean data variables download logs downloaded packages

Questions {❓}

• 5TB RAM $60k ( 96 * 16GB ) with 6TB $150k-$200k?
• Com/watch?
• ● So- does the R community need to focus on packages that have the highest questions to resolve their issues rather than the ones with the most downloads?

Schema {🗺️}

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      description:This document contains the slides from a presentation by Matt Dowle on working with messy data. It discusses Dowle's experiences over his career cleaning and munging data from various financial institutions to prepare it for machine learning models. It highlights some of the common issues encountered, such as data entry errors, id changes, missing values. It also showcases tools that can be used to help with pre-processing large datasets directly on the command line. Finally, it discusses how the size and scale of data has increased significantly over time, to the point where datasets can now easily exceed RAM sizes on standard machines.
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Social Networks {👍}(1)

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External Links {🔗}(5)

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• Income figures for https://support.scribd.com/hc/en/articles/360038016931-Privacy-Rights-Request-Form

Analytics and Tracking {📊}

• Google Analytics
• Google Analytics 4
• Google Tag Manager

Libraries {📚}

• Clipboard.js
• Moment.js
• Video.js
• WOW
• Zoom.js

Emails and Hosting {✉️}

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• smtp.google.com

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CDN Services {📦}

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