Data Analytics | Chuck Muizers

Peter Bailis joins the podcast to discuss the work of his company that solves complex commercial problems through automated data analysis.

In this episode you will learn:
• Meaning of the name Sisu [3:08]
• What Sisu does [4:45]
• Sisu and the data science stack [17:00]
• Going from academia to startups [22:37]
• What Sisu looks for when hiring [28:57]
• Peter’s favorite tools [32:40]
• Peter’s academic research [45:02]

Additional materials: www.superdatascience.com/527

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

SDS 527: Automating Data Analytics

COVID, inflation, broken supply chains, and not-so-distant war make this a turbulent time for the modern consumer. During times like these, families tend to their nests, which leads to lots of home-improvement projects…which means lots of painting.

Today we explore the case study of a Fortune 500 producer of the paints and stains that coat many households, consumer products, and even mechanical vehicles. While business expands, this company needs to carefully align the records that track hundreds of suppliers, thousands of storefronts, and millions of customers.

Business expansion and complex supply chains make it particularly important—and challenging—for enterprises such as this paint producer, which we’ll call Bright Colors, to accurately describe the entities that make up their business. They need to be governed, validated data to describe entities such as their products, locations, and customers. Master data management, also known as MDM, streamlines operations and assists data governance by reconciling disparate data records into golden records and ideally a single source of truth.

We’re excited to share our conversation with an industry expert that helps Bright Colors and other Fortune 2000 enterprises navigate turbulent times with effective strategies for MDM and data governance.

Dave Wilkinson is chief technology officer with D3Clarity, a global strategy and implementation services firm that seeks to ensure digital certainty, security, and trust. D3Clarity is a partner of Semarchy, whose Intelligent Data Hub software helps enterprises govern and manage master data, reference data, data quality, enrichment, and workflows. Semarchy sponsored this podcast.

Dave Wilkinson: Enterprise Data Governance and MDM Case Study

Cryptocurrency and blockchain take center stage this week as we welcome Chief Economist at Chainalysis, Philip Gradwell, to discuss the data science applications in this exciting field.

This episode is brought to you by Datalore (https://datalore.online/SDS), the collaborative data science platform, by Zencastr (zen.ai/sds), the easiest way to make high-quality podcasts, and by Bunch (superdatascience.com/bunch), the AI driven leadership coach. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• What the role of a chief economist entails [5:50]
• What are blockchains and cryptocurrency? [8:23]
• How analyzing cryptocurrencies differs from established fiat currencies [12:48]
• Philip's work at Chainalysis [26:07]
• Philip's crypto data analytics pipeline [34:48]
• How Philip develops data products for a wide range of users [46:18]
• How the blockchain facilitates innovative computing and machine learning technologies [51:52]
• What Philip looks for in the data scientists he hires [1:04:59]

Additional materials: www.superdatascience.com/621

621: Blockchains and Cryptocurrencies: Analytics and Data Applications

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

631: Data Analytics Career Orientation

In this podcast CIPD's Chief Executive Peter Cheese. Robert Bolton, Partner, Leader of Global HR Transformation Centre of Excellence, KPMG and Hayley Brown, Talent Intelligence Analyst, Europe, Middle East and Africa (EMEA), for AVON Cosmetics, discuss the results of a survey and how HR needs to be able to use analytics to prove its own value to the business.

Podcast 77:  Analytics: How HR can influence strategy through data

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

Dan and Jay discussed the concept of Data Fabric, an automated and AI-driven approach to managing modern data environments.

Analyst Series - Data Fabric: The Next Step in the Evolution of Data Architectures

Generative AI initiatives require new data pipelines that prepare text files for querying by language models. Data engineers, scientists, and other stakeholders collaborate to design and implement these pipelines, which span text sources, tokens, vectors, vector databases, and LMs.
Published at:
https://www.eckerson.com/articles/the-new-data-pipeline-for-generative-ai-where-and-how-it-works

The New Data Pipeline For Generative AI: Where And How It Works - Audio Blog

Kevin Petrie, the Vice President of Research at Eckerson Group, and Dan O’Brien, research analyst, discussed large language models (LLMs), which are neural networks that analyze text to predict the next word or phrase. These models use training data, often from the internet, to understand word relationships and provide accurate answers to natural language questions.

Analyst Series: Should AI Bots Build Your Data Pipelines?

In this episode, host Felipe Flores interviews Alan Lowthorpe, co-founder of Adaptive Data (who advise organisations on how to accelerate the value delivered from data and AI) and James Lecoutre, Director at Talent Insights Group as they delve into the world of data analytics and AI leadership, sharing insights on building successful teams, embracing diversity, fostering a growth mindset and navigating challenges in data analytics and AI.

#245 - Becoming a Successful Data Analytics and AI Leader

In this session of the Super Data Science Podcast, I chat with Ruben Kogel who has done some amazing data analytics in the startup space.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/1

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 001 : Self-serve Analytics, Data Science MBA and Managing a Team of Analysts with Ruben Kogel

In this session of the Super Data Science Podcast, I chat with Nano Science PHD turned Data Scientist Dr Wilson Pok. You will learn about the importance of defining business problems, a scientific approach to thinking about data analytics and more.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/3

SDS 003 : Defining the Data Problem, Academia vs Career and R Modeling Libraries with Dr. Wilson Pok

greg - one

Only You - gin$eng

Dr. Baishali Roy, special section coordinator for the March 2017 issue of The Leading Edge, joins Andrew Geary to discuss data analytics and machine learning in geophysics. Dr. Roy has worked at ConocoPhillips since 2000. She currently serves on the TLE Editorial Board.

Subscribers can read the full articles in the SEG Digital Library at http://library.seg.org and for those currently not subscribed, abstracts are always free.

Visit http://seg.org/podcast for show notes.

Sponsor
The SEG Wiki is home to hundreds of biographies of key geoscientists, geophysical tutorials, and core content from the science of applied geophysics. Visit http://wiki.seg.org to learn how you can grow the world’s first online, geophysics encyclopedia.

Seismic Soundoff

#14: Data analytics and machine learning - Baishali Roy

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.

Transcript: https://hbs.me/3bQ28EG

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

With the growing popularity of machine learning and artificial intelligence, creating a data science program is a key initiative at most companies today. However, it’s not always clear to executives how they can deliver a return on investments in data science. To explain this, we invited an expert who has spent most of his career in the data science trenches and has a clear-minded perspective on how to deliver ROI with data science.   

Alan Jacobson is the chief data and analytics officer (CDAO) of Alteryx, driving key data initiatives and accelerating digital business transformation for the Alteryx global customer base. As CDAO, Jacobson leads the company’s data science practice as a best-in-class example of how a company can get maximum leverage out of its data and the insights it contains, responsible for data management and governance, product and internal data, and use of the Alteryx Platform to drive continued growth.

Prior to joining Alteryx, Alan held a variety of leadership roles at Ford Motor Company across engineering, marketing, sales and new business development; most recently leading a team of data scientists to drive digital transformation across the enterprise. As an Alteryx evangelist at Ford, Alan spent many years leveraging the Alteryx Platform across the company and witnessed first-hand the impact a culture of analytics can have on the bottom line and what it takes to succeed as a data-driven enterprise.

Alan Jacobson: How to Deliver ROI from Analytics and Data Science

In this episode of the Data Show, I speak with Arun Kejariwal of Facebook and Ira Cohen of Anodot (full disclosure: I’m an advisor to Anodot). This conversation stemmed from a recent online panel discussion we did, where we discussed time series data, and, specifically, anomaly detection and forecasting. Both Kejariwal (at Machine Zone, Twitter, and Facebook) and Cohen (at HP and Anodot) have extensive experience building analytic and machine learning solutions at large scale, and both have worked extensively with time-series data. The growing interest in AI and machine learning has not been confined to computer vision, speech technologies, or text. In the enterprise, there is strong interest in using similar automation tools for temporal data and time series.

Machine Learning and Analytics for Time Series Data

This audio blog focuses on data storytelling and how it uses numbers, narrative, and visuals to communicate insights that would otherwise be hard to absorb. 
Originally published at: 
https://www.eckerson.com/articles/data-storytelling-part-i-telling-it-like-it-is-and-was-and-will-be

Data Storytelling, Part I- Telling It Like It Is, And Was, And Will Be by Jake Freivald - Audio Blog

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.