Discovering & Mining The Everyday – Richard Ziade & Tim Meaney

Possibly related…

  1. IA Summit 09 - Keynote

    Michael Wesch opened the IA Summit this year with an inspired keynote that provides a fresh and ambitious direction for all designers. He points out that our “audiences” aren’t audiences at all, but rather creators, and our job is not to lecture but to enable. With this new approach comes not only design challenges but the joy of reconnecting people to each other, which he illustrated with a series of extraordinary video clips.

    —Huffduffed by plindberg

  2. Semantic Web Gang: Interfaces to the Semantic Web

    With so much effort being devoted to the back-office manipulation and storage of semantic data, it is all too easy to forget the opportunities - and challenges - posed in inviting mainstream users to ‘browse the graph’ of semantic data. With expert contributions from MIT’s David Karger and the DBpedia team’s Christian Becker, the Gang sets about ensuring that the Interface is not forgotten.

    —Huffduffed by Clampants

  3. Teaching Data Science with Vik Paruchuri | Software Engineering Daily

    Podcast: Play in new window

    | Download

    There is a need for more data scientists to make sense of the vast amounts of data we produce and store.

    Dataquest is an in-browser platform for learning data science that is tackling this problem.

    Vik Paruchuri is the founder of Dataquest. He was previously a machine learning engineer at EdX and before that a U.S. diplomat.


    What is data science?

    How does data science compare to software engineering?

    How does someone new to data science go about starting off at Kaggle?

    In machine learning, there is unsupervised learning and supervised learning. Could you contrast these two?

    What are the biggest world problems that will be solved with data science?



    How to actually learn data science


    Comments comments

    —Huffduffed by bonono

  4. Data Science Unicorns and Silver-Bullet AI

    SXSW 2019 Schedule | Mythology around data science and AI is building quickly, but buzzwords have led to buzzkill when it comes to actual implementation. This panel of experienced data scientists will discuss the who, what, and how of data science. Forewarning: we won’t call AI a "silver bullet", unless your problem is a werewolf. (Most problems aren’t werewolves.)

    So, are data scientists really unicorns? Is a PhD in data science and 20 years of experience necessary to be useful? What about visualization, TensorF…

    —Huffduffed by danimad

  5. Podcast: Foretelling 2014 Trends in Big Data, Hadoop, Data Science & More | The Big Data Hub

    What’s in store for big data, analytics and data science in 2014? Big data evangelist James Kobielus walks us through what he sees shaping up in those areas, plus cognitive computing, machine learning, Hadoop, NoSQL and more.

    Listen here or read the blog post that spurred this podcast.

    For more information about the IBM big data platform and products, visit

    For more podcasts, blogs, videos, infographics and other resources, visit

    —Huffduffed by mfontenele

  6. An alternate perspective on data-driven decision making - O’Reilly Radar

    In this week’s Radar Podcast episode, O’Reilly’s Roger Magoulas chatted with Tricia Wang, a global tech ethnographer and co-founder of PL Data, about how qualitative and quantitative data need…

    —Huffduffed by agileone

  7. 18: CRAZY Data Science — Rollins, Input/Output

    Data Science on WikipediaData Science Name Controversy by RevolutionsAardvark Search Engine on WikipediaMagic 8 Ball (sort of)Feature Selection on WikipediaData Science vs. Statistics by Hadley WickhamData Lake (top Google hit!)Data Exhaust (‘ware the ads, they are horrible!) on techopediaBeer and Diapers (please don’t confuse these two…)Police Bitcoin Ransom (don’t click the PDF, dude…)Selfie Stick Bans

    —Huffduffed by davidbrush