In this episode of Inside Economics, our weekly podcast, Bernard Yaros, Economist at Moody's Analytics, joins Mark Zandi and the Moody's Analytics team to discuss fiscal policy, debt and deficits. We also discuss this weeks data, including inflation, lumber prices, tax refunds and the CNN/Moody's Analytics back-to-normal index.

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

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In this episode, I respond to Marc Lamy's question about where to find data analyst roles and how to get one. The answer as always, DO PROJECTS! I also mention using your network, especially your university alumni and resources. Maybe try a smaller company? And check out some of the listings on Stack Overflow.

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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

This is probably my favorite Ask Avery show of all time, at least of the last few months. We had some folks show up live to ask questions. Ricardo and Quincy asked awesome questions and it was so fun to talk to them face to face. We talked about how to stand out in getting a job in data with Ricardo. And Quincy asked about my opinion on bootcamps. We then took two written questions from Robert and Adnan. They asked about a structured plan for data science as well as how to know if you like data science.

I’m going to be ending the giveaway for the podcast rate and review. Please if you have one second and are on apple podcasts, please give us a review as it really helps the show. I’ll be choosing two random people to get shout outs on my linkedin as well as the podcast.

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Subscribe on YouTube: https://www.youtube.com/channel/UCuyfszBAd3gUt9vAbC1dfqA

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

Discussing AI First Company and AI First mentality with Ash Fontana. He sheds light on how organizations could embrace analytics, data and AI to retain competitive edge.

Bio: Ash Fontana became one of the most recognized startup investors in the world after launching online investing at AngelList. He then became a Managing Director of Zetta, the first investment fund that focused on AI. The firm was the lead investor in category-defining AI companies such as Kaggle, Domino, Tractable, Lilt and Invenia. He has appeared in Fast Company, Bloomberg, Forbes, CNBC and at the UN. This is his first book.

Ash's Book: The AI-First Company: How to Compete and Win with Artificial Intelligence by Ash Fontana https://amzn.to/33C2OL5

Ash's Recommendations: On Intelligence: How a New Understanding of the Brain Will Lead to the Creation of Truly Intelligent Machines by Jeff Hawkins, Sandra Blakeslee https://amzn.to/3vZhksk Neurophilosophy: Toward a Unified Science of the Mind-Brain by Patricia S. Churchland https://amzn.to/2RiKrYO

Discussion Timeline: TIMELINE

Some questions we covered: 1. Starter: Give your starter pitch 1 point that this book points to: 2. Vishal briefly introduce guest

Stage 2: Subject Matter Expertise 3. What is the state of startups today? 4. State of AI in mature organization? 5. AI and Enterprise outlook? Cautionary tale or hopeful story 6. Who will win the AI race? 7. Challenges in AI adoption?

Stage 3: Introduction as an author 8. What is an AI-First company? 9. Why write AI-First? 10. Why does every company need to prioritize AI over the next decade? 11. What are the most common mistakes companies make when trying to become AI-First? 12. What’s the difference between “lean-startup” and “lean AI”? 14. How do AI-First companies retain more of the “first mover” advantage than others? 15. AI + Business, will make it more science or art? 16. Can AI be a competitive edge

Stage 4: Rapid Fire with Ben Pring [Say what comes to your mind] 17 a. #MachineLearning 17 b. #Technology 17 c. #Leadership 17 d. #FutureOfWork 17 e. #Culture 17 f. #DigitalTransformation 17 g. #Disruption 17 h. #JobsOfFuture 17 i. #FutureofStartup 17 j. #FutureofOrganization 17 k. #AIFirst

Stage 5: Closing 18. What are 1-3 best practices that you think are the key to success in your journey? 19. Do you have any favorite read? 20. As a closing remark, what would you like to tell our audience?

About TAO.ai[Sponsor]: TAO is building the World's largest and AI-powered Skills Universe and Community powering career development platform empowering some of the World's largest communities/organizations. Learn more at https://TAO.ai

About FutureOfData: FutureOfData takes you on the journey with leaders, experts, academics, authors, and change-makers designing the future of data, analytics, and insights.

About AnalyticsWeek.com FutureOfData is managed by AnalyticsWeek.com, a #FutureOfData Leadership community of Organization architects and leaders.

Sponsorship / Guest Request should be directed to [email protected]

Keywords:

FutureofData #Work2.0 #Work2dot0 #Leadership #Growth #Org2dot0 #Work2 #Org2

Send us a text Want to be featured as a guest on Making Data Simple? Reach out to us at [[email protected]] and tell us why you should be next.

Abstract Hosted by Al Martin, VP, Data and AI Expert Services and Learning at IBM, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts. This week on Making Data Simple, we have Nancy Hensley, Nancy is currently the Chief Marketing and Product Officer for Stats Perform. Nancy was the Chief Digital Officer at IBM.

Show Notes 1:37 – Nancy’s bio 3:10 - Are we talking Money Ball? 5:52 - On Base percentage 7:08 – Analyse examples  10:02 – Do you control the data? 11:24 – Out there statistics 14:12 - Can analytics go to far? 17:35 – Real time analysis 18:45 – Covid and sports 21:15 – Your role in sports betting 22:50 – What’s the most fascinating thing you’ve learned? 25:23 – What’s the future?

Website - Stats Perform Money Ball Stats Perform - Twitter  Bill James – Baseball Abstract  The Analyst     Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Imagine you are a business user, consultant, or developer about to enter an SAP S/4HANA implementation project. You are well-versed with SAP’s product portfolio and you know that the preferred reporting option in S/4HANA is embedded analytics. But what exactly is embedded analytics? And how can it be implemented? And who can do it: a business user, a functional consultant specialized in financial or logistics processes? Or does a business intelligence expert or a programmer need to be involved? Good questions! This book will answer these questions, one by one. It will also take you on the same journey that the implementation team needs to follow for every reporting requirement that pops up: start with assessing a more standard option and only move on to a less standard option if the requirement cannot be fulfilled. In consecutive chapters, analytical apps delivered by SAP, apps created using Smart Business Services, and Analytical Queries developed either using tiles or in adevelopment environment are explained in detail with practical examples. The book also explains which option is preferred in which situation. The book covers topics such as in-memory computing, cloud, UX, OData, agile development, and more.Author Freek Keijzer writes from the perspective of an implementation consultant, focusing on functionality that has proven itself useful in the field. Practical examples are abundant, ranging from “codeless” to “hardcore coding.” What You Will Learn Know the difference between static reporting and interactive querying on real-time data Understand which options are available for analytics in SAP S/4HANA Understand which option to choose in which situation Know how to implement these options Who This Book is For SAP power users, functional consultants, developers

Summary Data lineage is the common thread that ties together all of your data pipelines, workflows, and systems. In order to get a holistic understanding of your data quality, where errors are occurring, or how a report was constructed you need to track the lineage of the data from beginning to end. The complicating factor is that every framework, platform, and product has its own concepts of how to store, represent, and expose that information. In order to eliminate the wasted effort of building custom integrations every time you want to combine lineage information across systems Julien Le Dem introduced the OpenLineage specification. In this episode he explains his motivations for starting the effort, the far-reaching benefits that it can provide to the industry, and how you can start integrating it into your data platform today. This is an excellent conversation about how competing companies can still find mutual benefit in co-operating on open standards.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today. When it comes to serving data for AI and ML projects, do you feel like you have to rebuild the plane while you’re flying it across the ocean? Molecula is an enterprise feature store that operationalizes advanced analytics and AI in a format designed for massive machine-scale projects without having to manage endless one-off information requests. With Molecula, data engineers manage one single feature store that serves the entire organization with millisecond query performance whether in the cloud or at your data center. And since it is implemented as an overlay, Molecula doesn’t disrupt legacy systems. High-growth startups use Molecula’s feature store because of its unprecedented speed, cost savings, and simplified access to all enterprise data. From feature extraction to model training to production, the Molecula feature store provides continuously updated feature access, reuse, and sharing without the need to pre-process data. If you need to deliver unprecedented speed, cost savings, and simplified access to large scale, real-time data, visit dataengineeringpodcast.com/molecula and request a demo. Mention that you’re a Data Engineering Podcast listener, and they’ll send you a free t-shirt. Your host is Tobias Macey and today I’m interviewing Julien Le Dem about Open Lineage, a new standard for structuring metadata to enable interoperability across the ecosystem of data management tools.

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of what the Open Lineage project is and the story behind it? What is the current state of t

What's in a job title? that which we call a senior data scientist by any other job title would model as predictively… This, dear listener, is why the hosts of this podcast crunch data rather than dabble in iambic pentameter. With sincere apologies to William Shakespeare, we sat down with Maryam Jahanshahi to discuss job titles, job descriptions, and the research, experiments, and analysis that she has conducted as a research scientist at Datapeople (formerly TapRecruit), specifically relating to data science and analytics roles. The discussion was intriguing and enlightening! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

The first official interview of the data career podcast! It was right after out launch party for the podcast and it was with the awesome. Thom Ives was our guest. Be sure to follow him on LinkedIn: https://www.linkedin.com/in/thomives/. Total stud. Love the guy. Seriously so nice. We talked about his long career in data and his transition from engineering to data science. 

That is the way to become a data scientist, you do data science before the role is ever assigned you. Don’t forget that.

I do want to also apologize for my tech issues on this episode. I somehow unplugged my nice mic for my audio, and my headphones weren’t working to listen to Thom’s. That being said this audio is NOT the best. I am super sorry.

There's also a giveaway going on for the pod, all you need to do is rate and review on Apple Podcasts. 

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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

Adam Kamins, Director of Regional Economics at Moody's Analytics, joins Mark Zandi and the Moody's Analytics team to discuss the recent inflation data and 2020 Census.

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

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HEY! Want a shout out on the podcast, and on LinkedIn? All you have to do is leave and review of the podcast to be considered. It's easy, quick, and free so why not.

This is LAUNCH PARTY episode! Wahoo! I talk about the goals for the pod, a little bit about me, and reveal the upcoming guests! 

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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

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Subscribe on YouTube: https://www.youtube.com/channel/UCuyfszBAd3gUt9vAbC1dfqA

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

"Turn yourself into a Data Head. You'll become a more valuable employee and make your organization more successful."Thomas H. Davenport, Research Fellow, Author of Competing on Analytics, Big Data @ Work, and The AI Advantage You've heard the hype around data—now get the facts. In Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, award-winning data scientists Alex Gutman and Jordan Goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it. You'll learn how to: Think statistically and understand the role variation plays in your life and decision making Speak intelligently and ask the right questions about the statistics and results you encounter in the workplace Understand what's really going on with machine learning, text analytics, deep learning, and artificial intelligence Avoid common pitfalls when working with and interpreting data Becoming a Data Head is a complete guide for data science in the workplace: covering everything from the personalities you’ll work with to the math behind the algorithms. The authors have spent years in data trenches and sought to create a fun, approachable, and eminently readable book. Anyone can become a Data Head—an active participant in data science, statistics, and machine learning. Whether you're a business professional, engineer, executive, or aspiring data scientist, this book is for you.

Prepare for Microsoft Exam DA-100 and help demonstrate your real-world mastery of Power BI data analysis and visualization. Designed for experienced data analytics professionals ready to advance their status, Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the Microsoft Certified Associate level. Focus on the expertise measured by these objectives: Prepare the data Model the data Visualize the data Analyze the data Deploy and maintain deliverables This Microsoft Exam Ref: Organizes its coverage by exam objectives Features strategic, what-if scenarios to challenge you Assumes you are an experienced business intelligence professional or data analyst, or have a similar role Analyzing Data with Microsoft Power BI About the Exam Exam DA-100 focuses on skills and knowledge needed to acquire, profile, clean, transform, and load data; design and develop data models; create measures with DAX; optimize model performance; create reports and dashboards; enrich reports for usability; enhance reports to expose insights; perform advanced analysis; manage datasets, and create and manage workspaces. About Microsoft Certification Passing this exam earns your Microsoft Certified: Data Analyst Associate certification, demonstrating your ability to help businesses maximize the value of data assets by using Microsoft Power BI. As subject matter experts, Data Analysts design and build scalable data models, clean and transform data, and enable advanced analytic capabilities that provide meaningful business value through easy-to-comprehend data visualizations. See full details at: microsoft.com/learn

Explore the most serious prevalent ethical issues in data science with this insightful new resource The increasing popularity of data science has resulted in numerous well-publicized cases of bias, injustice, and discrimination. The widespread deployment of “Black box” algorithms that are difficult or impossible to understand and explain, even for their developers, is a primary source of these unanticipated harms, making modern techniques and methods for manipulating large data sets seem sinister, even dangerous. When put in the hands of authoritarian governments, these algorithms have enabled suppression of political dissent and persecution of minorities. To prevent these harms, data scientists everywhere must come to understand how the algorithms that they build and deploy may harm certain groups or be unfair. Responsible Data Science delivers a comprehensive, practical treatment of how to implement data science solutions in an even-handed and ethical manner that minimizes the risk of undue harm to vulnerable members of society. Both data science practitioners and managers of analytics teams will learn how to: Improve model transparency, even for black box models Diagnose bias and unfairness within models using multiple metrics Audit projects to ensure fairness and minimize the possibility of unintended harm Perfect for data science practitioners, Responsible Data Science will also earn a spot on the bookshelves of technically inclined managers, software developers, and statisticians.

Using log analytics provides organizations with powerful and necessary capabilities for IT security. By analyzing log data, you can drive critical business outcomes, such as identifying security threats or opportunities to build new products. Log analytics also helps improve business efficiency, application, infrastructure, and uptime. In the second edition of this report, data architects and IT infrastructure leads will learn how to get up to speed on log data, log analytics, and log management. Log data, the list of recorded events from software and hardware, typically includes the IP address, time of event, date of event, and more. You'll explore how proactively planned data storage and delivery extends enterprise IT capabilities critical to security analytics deployments. Explore what log analytics is--and why log data is so vital Learn how log analytics helps organizations achieve better business outcomes Use log analytics to address specific business problems Examine the current state of log analytics, including common issues Make the right storage deployments for log analytics use cases Understand how log analytics will evolve in the future With this in-depth report, you'll be able to identify the points your organization needs to consider to achieve successful business outcomes from your log data.

Adam Ozimek Chief Economist at Upwork, joins Mark Zandi and the Moody's Analytics team to discuss the recent job numbers and productivity. 

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

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In this episode of DataFramed, Adel speaks with Amen Ra Mashariki, principal scientist at Nvidia and the former Chief Analytics Officer of the City of New York on how data science is done in government agencies, and how it's driving smarter cities all around us. 

Throughout the episode, Amen deep-dives into the use-cases he worked on to make the city of New York smarter, how data science allows cities to become more reactive and proactive, the unique challenges of scaling data science in a government setting, the friction between providing value and data privacy and ethics, the state of data literacy in government, and more. 

Links from the interview:

Follow Amen on LinkedInFollow Amen on TwitterThe New York City Business AtlasHurricane Sandy FEMA After-Action ReportData Drills

This IBM® Redbooks® publication introduces the latest member of the IBM Z® platform, the IBM z15™. It includes information about the Z environment and how it helps integrate data and transactions more securely. It also provides insight for faster and more accurate business decisions. The z15 is a state-of-the-art data and transaction system that delivers advanced capabilities, which are vital to any digital transformation. The z15 is designed for enhanced modularity, and occupies an industry-standard footprint. It is offered as a single air-cooled 19-inch frame called the z15 T02, or as a multi-frame (1 to 4 19-inch frames) called the z15 T01. Both z15 models excel at the following tasks:: Using hybrid multicloud integration services Securing and protecting data with encryption everywhere Providing resilience with key to zero downtime Transforming a transactional platform into a data powerhouse Getting more out of the platform with operational analytics Accelerating digital transformation with agile service delivery Revolutionizing business processes Blending open source and IBM Z technologies This book explains how this system uses innovations and traditional Z strengths to satisfy growing demand for cloud, analytics, and open source technologies. With the z15 as the base, applications can run in a trusted, reliable, and secure environment that improves operations and lessens business risk.

The economy is booming, should President Biden get the credit? ...and what about his newly unveiled American Families plan? Mark Zandi and the Moody's Analytics team discuss.    

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

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