Bill Spriggs, Chief Economist of AFL-CIO, joins the podcast and shares his views that we need to be patient on inflation, it's not the result of an overly tight labor market but to temporary forces, and thus does not require aggressive Fed rate hikes. He also dissects President Biden's industrial policy. Full episode transcript here For more on Bill Spriggs, click here Follow Mark Zandi @MarkZandi, Cris deRitis @MiddleWayEcon, and Marisa DiNatale on LinkedIn for additional insight

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.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Sarah and Chris are both at the forefront of bringing the promise of gen AI to our actual work as data people—which is a unique challenge!  Precise truth is critical for business questions in a way that it's not for a consumer search query. Sarah Nagy is the CEO of Seek AI, a startup that aims to use natural language processing to change how professionals work with data. Chris Aberger currently leads Numbers Station AI, a startup focused on data-intensive workflow automation. In this conversation with Tristan and Julia, they dive into what this future might actually look like, and tangibly what we can expect from gen AI in the short/medium term. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.  The Analytics Engineering Podcast is sponsored by dbt Labs.

This is the story of how a warehouse worker pivoted into a senior data engineer in just 18 months while tripling her salary.   In this episode of The Data Career Podcast, Avery Smith sits down with Kedeisha Bryan on how she landed a data job and tripled her income in only 18 months.

🌟 Join the data project club!

“25OFF” to get 25% off (first 50 members).

📊 Come to my next free “How to Land Your First Data Job” training

🏫 Check out my 10-week data analytics bootcamp

Kedeisha’s Links:

Connect on LinkedIn Join Data in Motion Community

Timestamps:

(9:19) - Why you need a sponsor in your life

(11:21) - You need common ground to network genuinely

(27:02) - Tired of sending job applications? Network instead

(30:16) - Know that this is a long game

Connect with Avery:

📺 Subscribe on YouTube: https://www.youtube.com/c/AverySmithDataCareerJumpstart/videos 🎙Listen to My Podcast: https://podcasts.apple.com/us/podcast/data-career-podcast/id1547386535 👔 Connect with me on LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://www.instagram.com/datacareerjumpstart/ 🎵 TikTok: https://www.tiktok.com/@verydata?

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 Wrangling with R guides you through mastering data preparation in the R programming language using tidyverse libraries. You will learn techniques to load, explore, transform, and visualize data effectively, gaining the skills needed for data modeling and insights extraction. What this Book will help me do Understand how to use R and tidyverse libraries to handle data wrangling tasks. Learn methods to work with diverse data types like numbers, strings, and dates. Gain proficiency in building visual representations of data using ggplot2. Build and validate your first predictive model for useful insights. Create an interactive web application with Shiny in R. Author(s) Gustavo Santos is an experienced data scientist specializing in R programming and data visualization. With a background in statistics and several years of professional experience in industry and academia, Gustavo excels at translating complex data analytics concepts into practical skills. His approach to teaching is hands-on and example-driven, aiming to empower readers to excel in real-world applications. Who is it for? If you are a data scientist, data analyst, or even a beginner programmer who wants to enhance their data manipulation and visualization skills, this book is perfect for you. Familiarity with R or a general understanding of programming concepts is suggested but not mandatory. It caters to professionals looking to refine their data wrangling workflow and to students aspiring to break into data-centered fields. By the end, you'll be ready to apply data wrangling and visualization tools in your projects.

Mark, Cris, and Marisa answer questions from their recent U.S. Economic Outlook webinar where they discussed that the economy will struggle in 2023 with halting growth and higher unemployment. Recession is a serious threat, but the Moody's Analytics baseline forecast-the most-likely outlook-holds that the economy will avoid a downturn. Full episode transcript Follow Mark Zandi @MarkZandi, Cris deRitis @MiddleWayEcon, and Marisa DiNatale on LinkedIn for additional insight

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.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In today’s episode, we’re joined by Reha Jhunjhunwala, Product Manager of AI ML Initiatives at eClinical Solutions, a company that helps life sciences organizations around the world accelerate clinical development initiatives with expert data services.

We talk about:

• Reha’s background as a dentist and how she got into tech.
• How machine learning and AI impact the software development process.
• How AI will affect the traditional strengths of software in general.
• What the considerations are around AI in healthcare where regulations are strict.
• Some of the things slowing AI down.

Reha Jhunjhunwala - https://www.linkedin.com/in/rehajhunjhunwala/ eClinical Solutions - https://www.linkedin.com/company/eclinical-solutions/

This episode is brought to you by Qrvey

The tools you need to take action with your data, on a platform built for maximum scalability, security, and cost efficiencies. If you’re ready to reduce complexity and dramatically lower costs, contact us today at qrvey.com.

Qrvey, the modern no-code analytics solution for SaaS companies on AWS.

saas #analytics #AWS #BI

In API Analytics for Product Managers, you will learn how to approach APIs as products to drive revenue and business growth. The book provides actionable insights on researching, strategizing, marketing, and evaluating the performance of APIs in SaaS contexts. What this Book will help me do Learn to develop long-term strategies for managing APIs as a product. Master the concepts of the API lifecycle and API maturity for better management. Understand and apply key metrics to measure activation, retention, and engagement of APIs. Design support models for APIs that ensure scalability and efficiency. Gain techniques for deriving actionable business insights from metrics analysis. Author(s) Deepa Goyal is an experienced product manager who specializes in API lifecycle management and analytics strategies. With years of industry experience, she has developed deep expertise in scaling and optimizing APIs to deliver business value. Her practical and results-oriented writing style makes complex topics accessible for professionals looking to enhance their API strategies. Who is it for? Ideal for product managers, engineers, and executives in SaaS companies looking to maximize the potential of APIs. This book is especially suited for individuals with foundational knowledge of APIs aiming to refine their analytical and strategic skills. Readers will gain actionable insights to track API performance effectively and implement metrics-driven decisions. It's a must-read for those focused on leveraging APIs for business growth.

The most common application for data science is to solve problems within your own organization, and as professionals become more data literate, they rely less and less on others to solve their problems and unlock professional growth and career advancement. But in the world of consulting, data science is used to solve other people’s problems, which adds an additional layer of complexity since consultants aren’t always given all of the tools they need to do the job right. Enter Pratik Agrawal, a Partner at Kearney Analytics leading the automotive and industrial transportation sector. In this episode, we are taking a look at how data science is applied in the consulting industry and what skills are critical to be a successful data science consultant.  As a software engineer and data scientist with over a decade of experience in the consulting world at companies like Boston Consulting Group and IRI, Pratik has a deep understanding of how to navigate the industry and how data science can be leveraged in it, as well as expertise in digital transformation projects and strategy. Throughout the episode, we discuss common problems that consultants encounter, the skills needed to be successful as a consultant, the different approaches to analytics in consulting versus in an organization, how to handle context switching when juggling multiple projects, what makes consulting feel exciting and challenging, and much more.

Colleague, Bernard Yaros joins the podcast to help unpack the January CPI Report (which just happened to be released on Valentine's Day) and discuss their biggest inflation concerns, including Marisa's shockingly high gas bill. Bernard gives a rundown of the U.S. Treasury outlook with regards to the debt limit. Follow Mark Zandi @MarkZandi, Cris deRitis @MiddleWayEcon, and Marisa DiNatale on LinkedIn for additional insight

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.

In this episode, Jason Foster talks to Ben Steele, Head of Data and Analytics at BMS Group, a fast-growing, global insurance and reinsurance broker. They discuss the importance of predicting value and the key factors organisations should focus on to become more agile, better equipped to respond to new challenges and opportunities, and ultimately deliver more value to their stakeholders.

Stephen shared his incredible journey from tech support to a successful data analyst in only 3 months! Learn the key skills & strategies that helped him make the transition. 📊 Come to my next free “How to Land Your First Data Job” training

🏫 Check out my 10-week data analytics bootcamp

Timestamps:

(10:16) Your personalized project is critical to landing a data job (11:23) You're missing out if you don't use the filter feature (14:17) Get your mentor and fix your resume! (17:29) It's hard to help you if you don't have a portfolio (23:33) Don't be afraid of the job ads requirement (36:15) Luck favors those who are prepared

Stephen’s Links: 

Connect on Linkedin: https://www.linkedin.com/in/stephentran96/

Connect with Avery:

📺 Subscribe on YouTube: https://www.youtube.com/c/AverySmithDataCareerJumpstart/videos🎙Listen to My Podcast: https://podcasts.apple.com/us/podcast/data-career-podcast/id1547386535👔 Connect with me on LinkedIn: https://www.linkedin.com/in/averyjsmith/📸 Instagram: https://www.instagram.com/datacareerjumpstart/🎵 TikTok: https://www.tiktok.com/@verydata? 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

On today’s episode, we’re joined by Vlad Eidelman. Vlad is CTO and Chief Scientist at FiscalNote — a leading technology provider of global policy and market intelligence uniquely combining AI technology, actionable data, and expert and peer insights to give customers mission-critical insights.

We talk about:

• Vlad’s story and what FiscalNote does.
• How AI changes software.
• The importance of adding extra value to software.
• What to do with user data?
• How Vlad makes internal decisions at FiscalNote.
• The impact of remote work.
• The importance of building the right data analytics stack to acquire data.

Vlad Eidelman - https://www.linkedin.com/in/veidelman/ FiscalNote - https://www.linkedin.com/company/fiscalnote/

This episode is brought to you by Qrvey

The tools you need to take action with your data, on a platform built for maximum scalability, security, and cost efficiencies. If you’re ready to reduce complexity and dramatically lower costs, contact us today at qrvey.com.

Qrvey, the modern no-code analytics solution for SaaS companies on AWS.

saas #analytics #AWS #BI

One of the toughest parts of any data project is experimentation, not just because you need to choose the right testing method to confirm the project’s effectiveness, but because you also need to make sure you are testing the right hypothesis and measuring the right KPIs to ensure you receive accurate results. One of the most effective methods for data experimentation is A/B testing, and Anjali Mehra, Senior Director of Product Analytics, Data Science, Experimentation, and Instrumentation at DocuSign, is no stranger to how A/B testing can impact multiple parts of any organization. Throughout her career, she has also worked in marketing analytics and customer analytics at companies like Shutterfly, Wayfair, and Constant Contact. Throughout the episode, we discuss DocuSign’s analytics goals, how A/B testing works, how to gamify data experimentation, how A/B testing helps with new initiative validation, examples of A/B testing with data projects, how organizations can get started with data experimentation, and much more.

With many recent advances in data science, we have many more tools and techniques available for data analysts to extract information from data sets. This book will assist data analysts to move up from simple tools such as Excel for descriptive analytics to answer more sophisticated questions using machine learning. Most of the exercises use R and Python, but rather than focus on coding algorithms, the book employs interactive interfaces to these tools to perform the analysis. Using the CRISP-DM data mining standard, the early chapters cover conducting the preparatory steps in data mining: translating business information needs into framed analytical questions and data preparation. The Jamovi and the JASP interfaces are used with R and the Orange3 data mining interface with Python. Where appropriate, Voyant and other open-source programs are used for text analytics. The techniques covered in this book range from basic descriptive statistics, such as summarization and tabulation, to more sophisticated predictive techniques, such as linear and logistic regression, clustering, classification, and text analytics. Includes companion files with case study files, solution spreadsheets, data sets and charts, etc. from the book. Features: Covers basic descriptive statistics, such as summarization and tabulation, to more sophisticated predictive techniques, such as linear and logistic regression, clustering, classification, and text analytics Uses R, Python, Jamovi and JASP interfaces, and the Orange3 data mining interface Includes companion files with the case study files from the book, solution spreadsheets, data sets, etc.

Chris Herbert, Managing Director at Harvard Joint Center for Housing Studies, joins the podcast to discuss the state of the housing market, from the current housing recession to the outlook for homeownership. Go Eagles! Full Episode Transcript For more on Chris Herbert, click here Follow Mark Zandi @MarkZandi, Cris deRitis @MiddleWayEcon, and Marisa DiNatale on LinkedIn for additional insight  

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.

Auren Hoffman currently serves as the CEO and Chief Historian at SafeGraph, a data-as-a-service company he founded, which provides primarily location data.  In this conversation with Tristan and Julia, Auren shares how truly few companies are making use of 3rd-party datasets today, how opening up more datasets to public research could help us solve big problems, and a fun fact about Abraham Lincoln's (!) work in the industry.  For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.  The Analytics Engineering Podcast is sponsored by dbt Labs.

You just learned SQL or Python, or Tableau. But you don’t know how to build your data science project? In this episode, Avery shares a 3-step guide to building your first data science project.

🌟 Join the data project club!

“25OFF” to get 25% off (first 50 members).

📊 Come to my next free “How to Land Your First Data Job” training

🏫 Check out my 10-week data analytics bootcamp

Timestamps:

(1:28) - Art is theft, and so is the data science project

(4:02) - Find ideas on Towards Data Science Medium

(5:32) - Read a few articles to get inspiration

(6:05) - Avery’s strategy is doing 30 projects in 30 days

(9:08) - How academia finds inspiration to write

(11:01) - Take Avery’s project, replicate and do it

Mentioned Links:

Building 30 Data Science Projects in 30 days: https://youtu.be/kKmA9ihIg20

30 Data Science Projects Resources: https://www.datacareerjumpstart.com/30projectsresourcesignup

I Used Data Science to UNCOVER McDonald’s Healthiest Meal: https://youtu.be/3bbFc1225-4

Connect with Avery:

📺 Subscribe on YouTube: https://www.youtube.com/c/AverySmithDataCareerJumpstart/videos 🎙Listen to My Podcast: https://podcasts.apple.com/us/podcast/data-career-podcast/id1547386535 👔 Connect with me on LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://www.instagram.com/datacareerjumpstart/ 🎵 TikTok: [https://www.tiktok.com/@verydata?]

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

In today’s episode, we’re joined by Gleb Polyakov. Gleb is the CEO and Co-Founder of Nylas, a platform that allows developers to automate manual, repetitive everyday tasks with little to no code.

We talk about:

• How Nylas works, the benefits it provides and who it targets.
• The definition of first-party data and why it’s important.
• The growth of the API economy.
• The new roles of sales and marketing when selling to developers.
• The trend of using education as a sales technique.

Gleb Polyakov - https://www.linkedin.com/in/gpolyakov Nylas - https://www.linkedin.com/company/nylas/

This episode is brought to you by Qrvey

The tools you need to take action with your data, on a platform built for maximum scalability, security and cost efficiencies. If you’re ready to reduce complexity and dramatically lower costs, contact us today at qrvey.com.

Qrvey, the modern no-code analytics solution for SaaS companies on AWS.

saas #analytics #AWS #BI

As the digital landscape evolves, privacy concerns and regulations are becoming increasingly important for advertisers. With the decline of third-party cookies and the rise of individual data usage consent, measuring advertising attention is more crucial than ever. One of the biggest challenges for advertisers in a cookie-less world is being able to accurately measure the effectiveness of their campaigns. Without cookies, it's harder to track user behaviour and understand how their ads are performing. However, measuring advertising attention through alternative methods such as viewability, brand lift studies, and surveys can be helpful, but they provide vague and delayed signals about advertising effectiveness. How can advertisers measure the attention and effectiveness of their advertising in real-time? To answer this question, I recently spoke to John Hawkins, Chief Scientist at Playground XYZ. Playground XYZ provides a machine learning-based platform for measuring and maximising attention on digital ads. The company’s Attention Intelligence Platform is a unique technology that uses over 40 different signals to track user attention as it happens. In this episode of Leaders of Analytics, we discuss: How Playground’s attention measurement platform works in practiceThe importance of attention time in a world without cookies, where privacy and consent are increasingly of mandated importanceDealing with the complexities of multi-layered machine learning pipelines and convincing stakeholders of their valueHow data science professionals can foster the right non-data science skills that will make them true unicorns, and much more.John on LinkedIn: https://www.linkedin.com/in/hawkinsjohnc/ John's book, Getting Data Science Done.

Send us a text Datatopics is a podcast presented by Kevin Missoorten to talk about the fuzzy and misunderstood concepts in the world of data, analytics, and AI and get to the bottom of things.

In this episode Kevin is joined by Ruben Lasuy - a fellow consultant in the space of GDPR, data governance and data strategy - to explore the so called "Collaborative Data Ecosystems", a datatopic surfing the Solid-protocol wave. But are Solid and its Solid Pods really the trigger for this new concept or is there more at play? 

Datatopics is brought to you by Dataroots Music: The Gentlemen - DivKidThe thumbnail is generated by Midjourney