Data Analytics | Chuck Muizers

Interview success, funny memes about data, and stakeholder management: Jon Krohn speaks with Luke Barousse, a full-time YouTuber who produces content to help aspiring data scientists. First, Jon and his guest go underwater to find out how data science can help you while working on a submarine before they emerge onto Luke’s YouTube channel. There, he discloses all the helpful hacks for data science beginners—with a generous helping of humor! As founder of MacroFit, a data-driven company that helps with meal planning, Luke is no stranger to portion sizes…

This episode is brought to you by Iterative (https://iterative.ai), your mission control center for machine learning. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• Where Luke gets his inspiration for making YouTube videos [04:46]
• How Luke got into creating comedy skits [08:21]
• Luke’s favorite Python libraries for web scraping [14:41]
• Incorrect assumptions that aspiring data scientists make [15:54]
• The best time to use Power BI [19:15]
• The biggest mistakes Luke made in his data science career [22:17]
• Luke’s experience as a submariner and how it helped him in his data analyst career [38:13]
• The must-have skills for entry-level data analyst roles [43:46]

Additional materials: www.superdatascience.com/631

The Super Data Science Podcast: Machine Learning | AI | Success

631: Data Analytics Career Orientation

Join us this week on the New Zealand Initiative podcast! Our host, Dr Eric Crampton chats with Professor Rhema Vaithianathan from Auckland University of Technology. Rhema gives us a peek into her pioneering work on data-tools, designed with a human touch to better serve communities.

In this fascinating chat, they delve into Rhema's trailblazing project, the Allegheny Family Screening Tool. This unique decision-aid tool is a global first, introduced in 2016 to support child welfare hotline screening. The duo also touch on the equally impressive Allegheny Housing Assessment tool, another innovative machine-learning tool used since 2020 for prioritising housing within the homelessness system.

Rhema reveals the power of predictive risk modelling in the US child protection system, where her team used administrative data to help improve outcomes for children. Listen to how this model uses past case data to guide operators in their decisions regarding potential abuse cases.

In the US, this approach worked to substantially reduce child hospitalisation. It could be done in New Zealand as well. In fact, the work started here. But New Zealand’s politics and public service has a very difficult time with new approaches. American localism means that one innovative county can try it out, and demonstrate the benefits to others.

All this and more in our enlightening podcast. Stay tuned for Part Two, where the conversation on AI and predictive models in child protection systems, and on encouraging public sector innovation more broadly, continues!

Podcast: Data analytics for social impact (part one)

ข้อมูลของเรา เวลามีขนาดไม่เยอะ ใช้เครื่องมือธรรมดาอย่าง spreadsheet ก็น่าจะเพียงพอแล้ว แต่เวลาข้อมูลมีขนาดใหญ่ มันก็จะไม่ง่ายอย่างนั้น วันนี้คุณไอซ์ วรุฒ กฤษรุ่งเรือง Senior Data Engineer จากบริษัท Data Wow จะมาเล่าให้ฟัง ตั้งแต่ต้นทาง ไปจนถึงปลายทาง Data Pipeline ทำอย่างไร ยากไหม ติดตามได้ในตอนนี้
.
คำถาม
1. Data Pipeline คืออะไร แล้วมันมีประโยชน์อย่างไรครับ
2. อะไรเป็นองค์ประกอบของ Data Pipeline แล้วแต่ละส่วนมันเชื่อมโยงกันอย่างไร
3. อะไรเป็นความท้าทายเวลาเราต้องสร้าง หรือ Maintain Data Pipeline ครับ
4. ระหว่าง Batch Processing กับ Real-Time/ Streaming Data Pipeline เราเลือกใช้อย่างไร
5. คำว่า Data Pipeline คือการเอาข้อมูลมายัดใส่พวกตระกูล BigQuery หรือ RedShift หรือเปล่า
6. มีเคสที่ Data Pipeline มีบทบาทสำคัญกับธุรกิจมาเล่าให้ฟังไหม
7. อะไรเป็น Technology ที่น่าสนใจและต้องจับตามองสำหรับการทำ Data Pipeline
8. ถ้าวันนี้เราจัดการกับข้อมูลจำนวนหนึ่ง แล้วในอนาคตข้อมูลเยอะขึ้นมากๆ เราจะจัดการหรือเตรียมความพร้อมกับ Data Pipeline ได้อย่างไร
9. เราสามารถทดสอบได้ไหม ว่า Data Pipeline เราทำงานได้อย่างถูกต้อง
10. Data Pipeline มีผลกับการทำ Machine Learning หรือ Data Analytics ไหม
11. มีข้อแนะนำให้คนฟัง เวลาที่คิดจะสร้าง Data Pipeline ควรจะต้องคิดถึงเรื่องอะไรบ้างไหม

Data Pipeline ศิลปะของการจัดการข้อมูล กับคุณวรุฒ กฤษรุ่งเรือง | Tech Monday EP.139

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In April 2019, the Harvard Business School Digital Initiative brought together academics and practitioners for an idea exchange around technology in a multidisciplinary world. This Future Assembly flash talk features Ayelet Israeli from Harvard Business School discussing "The added value of data analytics: evidence from online retailers." To learn more, check out digital.hbs.edu.

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Ayelet Israeli on the added value of data analytics | Future Assembly 2019

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Chuck Muizers is a Chief Operating Officer based in The Woodlands, Texas. Chuck has almost thirty years of experience in the National Security sector. This includes fifteen years of experience spent at the executive level in public and privately held companies. Currently, Mr. Muizers is occupied with several business ventures, including the COO of Skylla Engineering, CEO of ND2 Inc, and founding member of ExpFx LLC.

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast.

As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact.

Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy.

We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.

631: Data Analytics Career Orientation

Super Data Science: ML & AI Podcast with Jon Krohn

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