Machine Learning models can add value and insight to many projects, but they can be challenging to put into production due to problems like lack of reproducibility, difficulty maintaining integrations, and sneaky data quality issues. Kedro, a framework for creating reproducible, maintainable, and modular data science code, and Great Expectations, a framework for data validations, are two great open-source Python tools that can address some of these problems. Both integrate seamlessly with Airflow for flexible and powerful ML pipeline orchestration. In this talk we’ll discuss how you can leverage existing Airflow provider packages to integrate these tools to create sustainable, production-ready ML models.
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In this episode of DataFramed, Adel speaks with Elad Cohen, VP of Data Science and Research at Riskified on how data science is being used to combat fraud in eCommerce.Throughout the episode, Elad talks about his background, the plethora of data science use-cases in eCommerce, how Riskified builds state-of-the-art fraud detection models, common pitfalls data teams face, his best practices gaining organizational buy-in for data projects, how data scientists should focus on value, whether they should have engineering skills, and more.
Relevant links from the interview:
Connect with Elad on LinkedInRegister for our upcoming webinarsHow Riskified chooses what to research
Flow Immersive is the next generation of data visualization and storytelling. Sign up to be on the waitlist: https://flowimmersive.com/signup
Follow Michael DiBenigno on TikTok: https://www.tiktok.com/@the.data.guy?lang=en
Want to break into data science? Check out my new course coming out later this summer: Data Career Jumpstart - https://www.datacareerjumpstart.com
Want to leave a question for the Ask Avery Show?
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Watch The Ask Avery Show Live Tuesday’s at 8PM: https://www.datacareerjumpstart.com/AskAvery
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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
We talked about:
Santiago’s background “Transitioning to ML” vs “Adding ML as a skill” Getting over the fear of math for software developers Learning by explaining Seven lessons I learned about starting a career in machine learning Lesson 1 – Take the first step Lesson 2 – Learning is a marathon, not a sprint Lesson 3 – If you want to go quickly, go alone. If you want to go far, go together. Lesson 4 – Do something with the knowledge you gain Lesson 5 – ML is not just math. Math is not scary. Lesson 6 – Your ability to analyze a problem is the most important skill. Coding is secondary. Lesson 7 – You don’t need to know every detail Tools and frameworks needed to transition to machine learning Problem-based learning vs Top-down learning Learning resources Santiago’s favorite books Santiago’s course on transitioning to machine learning Improving coding skills Building solutions without machine learning Becoming a better engineer What is the difference between machine learning and data science? Getting into machine learning - Reiteration Getting past the math
Links:
Santiago's Twitter: https://twitter.com/svpino Santiago's course: https://gumroad.com/svpino#kBjbC Pinned tweet with a roadmap: https://twitter.com/svpino/status/1400798154732212230
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
by
Avery Smith
Here are some of the topics we covered in this episode… warning, we covered quite a bit.
Data books recommendations, an update on my complete data science bootcamp, and what background is best for data science (spoiler: all backgrounds are welcome), is data getting oversaturated, and how to get a job with no experience.
With that being said, we are at 742 downloads. I’m really excited to hit 1,000. If you could just pause for 30 seconds and think of anyone you know that could benefit from this podcast, and share it with them, I would love you forever.
Want to break into data science? Check out my new course coming out later this summer: Data Career Jumpstart - https://www.datacareerjumpstart.com
Want to leave a question for the Ask Avery Show?
Written Mailbag: https://forms.gle/78zD544drpDAcTRV9
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Want to be on The Ask Avery Show? Sign up for a spot here:
https://calendly.com/datacareer/ask-avery?month=2021-05
Watch The Ask Avery Show Live Tuesday’s at 8PM: https://www.datacareerjumpstart.com/AskAvery
Add The Ask Avery Show to your calendar: https://calendar.google.com/calendar/ical/c_u2rk36mj5mgqg5g42glm9a741c%40group.calendar.google.com/public/basic.ics
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
In this Ask Avery Show, we talk about all things data science careers. We talk about Data Career Jumpstart which is coming out soon, you can sign up for updates at DataCareerJumpstart.com.
We tackle Automation vs Data Science, unpaid internships, how to prepare for data interviews, how to start a data science project, how to make a data project portfolio, and more!
Want to break into data science? Check out my new course coming out later this summer: Data Career Jumpstart - https://www.datacareerjumpstart.com
Want to leave a question for the Ask Avery Show?
Written Mailbag: https://forms.gle/78zD544drpDAcTRV9
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Want to be on The Ask Avery Show? Sign up for a spot here:
https://calendly.com/datacareer/ask-avery?month=2021-05
Watch The Ask Avery Show Live Tuesday’s at 8PM: https://www.datacareerjumpstart.com/AskAvery
Add The Ask Avery Show to your calendar: https://calendar.google.com/calendar/ical/c_u2rk36mj5mgqg5g42glm9a741c%40group.calendar.google.com/public/basic.ics
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
Check out "Telling Your Data Story" by Scott Taylor | https://amzn.to/3qaNakb
Want to attend the Master Data Marathon 3.0 hosted by Scott Taylor? Use code MDM50 for 50% off right now! https://thinklinkers.com/events/master_data_marathon_2021
Super exciting episode today. I got to interview Scott Taylor - The Data Whisperer. What a cool guy. We talked about data governance and data management. What are they? What does that even mean? We talked about Scott’s decades of experience in the data industry and how his use of branding has helped him build his career.
I’m always impressed with Scott’s branding. Let’s start with his name; the data whisperer. Great slogan, catchy, gives you an idea of what he’s about. Then there’s his truth hat, and we talk about that in the episode, and then he has puppets.
Want to break into data science? Check out my new course coming out later this summer: Data Career Jumpstart - https://www.datacareerjumpstart.com
Want to leave a question for the Ask Avery Show?
Written Mailbag: https://forms.gle/78zD544drpDAcTRV9
Audio Mailbag: https://anchor.fm/datacareerpodcast/message
Want to be on The Ask Avery Show? Sign up for a spot here:
https://calendly.com/datacareer/ask-avery?month=2021-05
Watch The Ask Avery Show Live Tuesday’s at 8PM: https://www.datacareerjumpstart.com/AskAvery
Add The Ask Avery Show to your calendar: https://calendar.google.com/calendar/ical/c_u2rk36mj5mgqg5g42glm9a741c%40group.calendar.google.com/public/basic.ics
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
Harness the full power of the behavioral data in your company by learning tools specifically designed for behavioral data analysis. Common data science algorithms and predictive analytics tools treat customer behavioral data, such as clicks on a website or purchases in a supermarket, the same as any other data. Instead, this practical guide introduces powerful methods specifically tailored for behavioral data analysis. Advanced experimental design helps you get the most out of your A/B tests, while causal diagrams allow you to tease out the causes of behaviors even when you can't run experiments. Written in an accessible style for data scientists, business analysts, and behavioral scientists, thispractical book provides complete examples and exercises in R and Python to help you gain more insight from your data--immediately. Understand the specifics of behavioral data Explore the differences between measurement and prediction Learn how to clean and prepare behavioral data Design and analyze experiments to drive optimal business decisions Use behavioral data to understand and measure cause and effect Segment customers in a transparent and insightful way
In this episode, I share my quick experience leaving for 17 days to go do service in the Dominican Republic.
Want to break into data science? Check out my new course coming out later this summer: Data Career Jumpstart - https://www.datacareerjumpstart.com
Want to leave a question for the Ask Avery Show?
Written Mailbag: https://forms.gle/78zD544drpDAcTRV9
Audio Mailbag: https://anchor.fm/datacareerpodcast/message
Want to be on The Ask Avery Show? Sign up for a spot here:
https://calendly.com/datacareer/ask-avery?month=2021-05
Watch The Ask Avery Show Live Tuesday’s at 8PM: https://www.datacareerjumpstart.com/AskAvery
Add The Ask Avery Show to your calendar: https://calendar.google.com/calendar/ical/c_u2rk36mj5mgqg5g42glm9a741c%40group.calendar.google.com/public/basic.ics
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
On this episode, we chat with Andrew Thompson, Editor and Founder of Components, a media and culture publication focused on data journalism. Originally from California, Andrew received a degree in Political Science and Government from Temple University before moving into journalism. Becoming increasingly interested in data and data science, Andrew eventually became the Data Editor and Audience Development Manager at design software startup Ceros and then the Editorial Director of video streaming search engine Flixed. After a couple of years in New York City, he moved back home to Philly, taking Components from a side project to his full-time endeavor. Since 2018, Components has been covered and/or cited in Mashable, Vice, and more than 70 academic papers, and we were lucky enough to feature some of his research on our blog in an article he wrote called “What Spotify Follower Ratio Tells Us About Artist Growth and Fan Engagement.” Read "What Spotify Follower Ratio Tells Us About Artist Growth and Fan Engagement" here. If you want more free insights, follow our podcast, our blog, and our socials. If you're an artist with a free Chartmetric account, sign up for the artist plan, made exclusively for you, here. If you're new to Chartmetric, follow the URL above after creating a free account here.
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, IBM Expert Services Delivery, 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 Lillian Pierson. Lillian is CEO of Data Mania and she supports data professionals to becoming world data leaders and entrepreneurs. Lillian started Data Mania in 2012, Lillian has had 1.2 million people take her courses or read her book.
Show Notes 3:37 – How did you make your transition to Data Mania? 8:20 – What is your Brand now? 9:32 – If I contact Lillian @ Data Mania what will I walk away with? 14:01 – Is it methodology or career coach or is it both? 16:35 – Where did you get your experience to put people in these buckets? 19:08 – Do you provide one on one services? 22:48 – Is your team worldwide? 24:36 – What differentiates your book from another? 26:48 – Who is Data Mania targeting? 28:32 – Where do I start? Email - [email protected] Data-mania Data Superhero Quiz Data Science For Dummies Lillian Pierson - LinkedIn
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.
In this episode of DataFramed, Adel speaks with Sergey Fogelson, Vice President of Data Science and Modeling at Viacom on how data science has evolved over the past decade, and the remaining large-scale challenges facing data teams today.
Throughout the episode, Sergey deep-dives into his background, the various projects he’s been involved with throughout his career, the most exciting advances he’s seen in the data science space, the largest challenges facing data teams today, best practices democratizing data, the importance of learning SQL, and more.
Relevant links from the interview:
Connect with Sergey on LinkedInCheck out Sergey’s course on DataCampLearn more about AirflowLearn more about PySparkLearn more about SQL
More resources from DataCamp
Upskill your team with DataCampOur Guide on Open Source Software in Data ScienceYour Organization’s Guide to Data Maturity
E se tivessemos que começar tudo de novo? Quais erros evitaríamos cometer no começo da carreira? Ainda faríamos faculdade? Quais skills consideramos as mais importantes? Paulo Vasconcellos, Allan Sene e Gabriel Lages compartilham o que fariam se tivessem que começar a carreira tudo de novo. Vem que esse episódio está muito legal!
Acesse nosso post no Medium: https://medium.com/data-hackers/como-come%C3%A7ar%C3%ADamos-em-data-science-hoje-data-hackers-podcast-39-fb5394c8f98b
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.
Want to leave a question for the Ask Avery Show?
Written Mailbag: https://forms.gle/78zD544drpDAcTRV9
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Want to be on The Ask Avery Show? Sign up for a spot here:
https://calendly.com/datacareer/ask-avery?month=2021-05
Watch The Ask Avery Show Live Tuesday’s at 8PM: https://www.datacareerjumpstart.com/AskAvery
Add The Ask Avery Show to your calendar: https://calendar.google.com/calendar/ical/c_u2rk36mj5mgqg5g42glm9a741c%40group.calendar.google.com/public/basic.ics
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
by
Val Kroll
,
Julie Hoyer
,
Tim Wilson
(Analytics Power Hour - Columbus (OH)
,
Maryam Jahanshahi
(TapRecruit)
,
Moe Kiss
(Canva)
,
Michael Helbling
(Search Discovery)
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.
Want to leave a question for the Ask Avery Show?
Written Mailbag: https://forms.gle/78zD544drpDAcTRV9
Audio Mailbag: https://anchor.fm/datacareerjumpstart/message
Want to be on The Ask Avery Show? Sign up for a spot here:
https://calendly.com/datacareer/ask-avery?month=2021-05
Watch The Ask Avery Show Live Tuesday’s at 8PM: https://www.datacareerjumpstart.com/AskAvery
Add The Ask Avery Show to your calendar: https://calendar.google.com/calendar/ical/c_u2rk36mj5mgqg5g42glm9a741c%40group.calendar.google.com/public/basic.ics
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
In this episode of DataFramed, Adel speaks with Dan Becker, CEO of decision.ai and founder of Kaggle Learn on the intersection of decision sciences and AI, and best practices when aligning machine learning to business value.
Throughout the episode, Dan deep-dives into his background, how he reached the top of a Kaggle competition, the difference between machine learning in a Kaggle competition and the real world, the role of empathy when aligning machine learning to business value, the importance of decisions sciences when maximizing the value of machine learning in production, and more.
Links:
Follow Dan on TwitterFollow Dan on LinkedInWhat 70% of data science learners do wrongCheck out Dan’s course on DataCampdecision.aiDan’s climate dashboard
Want to leave a question for the Ask Avery Show?
Written Mailbag: https://forms.gle/78zD544drpDAcTRV9
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Want to be on The Ask Avery Show? Sign up for a spot here: https://calendly.com/datacareer/ask-avery-1
Watch The Ask Avery Show Live Tuesday’s at 8PM: https://www.datacareerjumpstart.com/AskAvery
Add The Ask Avery Show to your calendar: https://calendar.google.com/calendar/ical/c_u2rk36mj5mgqg5g42glm9a741c%40group.calendar.google.com/public/basic.ics
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
by
Jordan Goldmeier
(Booz Allen Hamilton; The Perduco Group; EY; Excel TV; Wake Forest University; Anarchy Data)
,
Alex J. Gutman
"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.
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.