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| Title & Speakers | Event |
|---|---|
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A guide to building tomorrows AI Solutions | BRK132
2024-11-26 · 07:14
Henk Boelman
,
David Smith
,
David Smith
,
Henk Boelman
@ Microsoft
,
Daniel Laskewitz
,
Daniel Laskewitz
The past few days, you have heard about many new developments in the AI space, from new models and capabilities to autonomous agents. In this session, we will recap the concepts, tools, and frameworks covered this week that you'll need to build your AI solutions for tomorrow. 𝗦𝗽𝗲𝗮𝗸𝗲𝗿𝘀: * Henk Boelman * Daniel Laskewitz * David Smith 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻: This is one of many sessions from the Microsoft Ignite 2024 event. View even more sessions on-demand and learn about Microsoft Ignite at https://ignite.microsoft.com BRK132 | English (US) | AI MSIgnite |
Microsoft Ignite 2023 |
|
Daniel Smith: Big Data and Small Data Everywhere, All at Once
2023-12-04 · 13:42
Daniel Smith: Big Data and Small Data Everywhere, All at Once: Porting Large-scale Data Solutions to Run in the Browser Join Daniel Smith as he shares the fascinating journey of porting a large-scale data profiling solution to run seamlessly in web browsers, making it accessible for both technical and non-technical users. 🌐🖥️ Learn how to achieve this transformation, maintainability, and overcome key challenges in this insightful session. 🚀🔍 #DataSolutions #WebBrowser #innovation ✨ H I G H L I G H T S ✨ 🙌 A huge shoutout to all the incredible participants who made Big Data Conference Europe 2023 in Vilnius, Lithuania, from November 21-24, an absolute triumph! 🎉 Your attendance and active participation were instrumental in making this event so special. 🌍 Don't forget to check out the session recordings from the conference to relive the valuable insights and knowledge shared! 📽️ Once again, THANK YOU for playing a pivotal role in the success of Big Data Conference Europe 2023. 🚀 See you next year for another unforgettable conference! 📅 #BigDataConference #SeeYouNextYear |
DATA MINER Big Data Europe Conference 2020 |
|
85: Landing a Job as an Immigrant w/ Daniel Botero
2023-11-22 · 18:00
Daniel Botero
– guest
,
Avery Smith
– Data Career Coach
Join Avery Smith with Daniel Botero, an expert in helping international STEM students navigate the job market in the US, as they discuss the challenges and strategies for landing a job as an immigrant in the data career field. Tune in to gain valuable insights and tips for success in this competitive job market. Don't miss out on this enlightening conversation, available now on the Data Career Podcast! Connect with Daniel Botero: 🤝 Connect on Linkedin ▶️ Subscribe to Youtube Channel 🎒 Learn About Opny 🤝 Ace your data analyst interview with the interview simulator 📩 Get my weekly email with helpful data career tips 📊 Come to my next free “How to Land Your First Data Job” training 🏫 Check out my 10-week data analytics bootcamp Timestamps: (05:20) - The job market is tough (20:13) - Ask for advice, not referral (35:58) - Hiring is like investing, be the best investment Connect with Avery: 📺 Subscribe on YouTube 🎙Listen to My Podcast 👔 Connect with me on LinkedIn 🎵 TikTok Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open! To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong. 👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa |
Data Career Podcast: Helping You Land a Data Analyst Job FAST |
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Data Scientists at Work
2014-12-15
Sebastian Gutierrez
– author
Data Scientists at Work is a collection of interviews with sixteen of the world's most influential and innovative data scientists from across the spectrum of this hot new profession. "Data scientist is the sexiest job in the 21st century," according to the Harvard Business Review. By 2018, the United States will experience a shortage of 190,000 skilled data scientists, according to a McKinsey report. Through incisive in-depth interviews, this book mines the what, how, and why of the practice of data science from the stories, ideas, shop talk, and forecasts of its preeminent practitioners across diverse industries: social network (Yann LeCun, Facebook); professional network (Daniel Tunkelang, LinkedIn); venture capital (Roger Ehrenberg, IA Ventures); enterprise cloud computing and neuroscience (Eric Jonas, formerly Salesforce.com); newspaper and media (Chris Wiggins, The New York Times); streaming television (Caitlin Smallwood, Netflix); music forecast (Victor Hu, Next Big Sound); strategic intelligence (Amy Heineike, Quid); environmental big data (Andre´ Karpis?ts?enkoEach of these data scientists shares how he or she tailors the torrent-taming techniques of big data, data visualization, search, and statistics to specific jobs by dint of ingenuity, imagination, patience, and passion. , Planet OS); geospatial marketing intelligence (Jonathan Lenaghan, PlaceIQ); advertising (Claudia Perlich, Dstillery); fashion e-commerce (Anna Smith, Rent the Runway); specialty retail (Erin Shellman, Nordstrom); email marketing (John Foreman, MailChimp); predictive sales intelligence (Kira Radinsky, SalesPredict); and humanitarian nonprofit (Jake Porway, DataKind). The book features a stimulating foreword by Google's Director of Research, Peter Norvig. Data Scientists at Work parts the curtain on the interviewees’ earliest data projects, how they became data scientists, their discoveries and surprises in working with data, their thoughts on the past, present, and future of the profession, their experiences of team collaboration within their organizations, and the insights they have gained as they get their hands dirty refining mountains of raw data into objects of commercial, scientific, and educational value for their organizations and clients. |
O'Reilly Data Science Books
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