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Jeremy Forman joins us to open up about the hurdles– and successes that come with building data products for pharmaceutical companies. Although he’s new to Pfizer, Jeremy has years of experience leading data teams at organizations like Seagen and the Bill and Melinda Gates Foundation. He currently serves in a more specialized role in Pfizer’s R&D department, building AI and analytical data products for scientists and researchers. .

Jeremy gave us a good luck at his team makeup, and in particular, how his data product analysts and UX designers work with pharmaceutical scientists and domain experts to build data-driven solutions..  We talked a good deal about how and when UX design plays a role in Pfizer’s data products, including a GenAI-based application they recently launched internally.  

Highlights/ Skip to:

(1:26) Jeremy's background in analytics and transition into working for Pfizer (2:42) Building an effective AI analytics and data team for pharma R&D (5:20) How Pfizer finds data products managers (8:03) Jeremy's philosophy behind building data products and how he adapts it to Pfizer (12:32) The moment Jeremy heard a Pfizer end-user use product management research language and why it mattered (13:55) How Jeremy's technical team members work with UX designers (18:00) The challenges that come with producing data products in the medical field (23:02) How to justify spending the budget on UX design for data products (24:59) The results we've seen having UX design work on AI / GenAI products (25:53) What Jeremy learned at the  Bill & Melinda Gates Foundation with regards to UX and its impact on him now (28:22) Managing the "rough dance" between data science and UX (33:22) Breaking down Jeremy's GenAI application demo from CDIOQ (36:02) What would Jeremy prioritize right now if his team got additional funding (38:48) Advice Jeremy would have given himself 10 years ago (40:46) Where you can find more from Jeremy

Quotes from Today’s Episode

“We have stream-aligned squads focused on specific areas such as regulatory, safety and quality, or oncology research. That’s so we can create functional career pathing and limit context switching and fragmentation. They can become experts in their particular area and build a culture within that small team. It’s difficult to build good [pharma] data products. You need to understand the domain you’re supporting. You can’t take somebody with a financial background and put them in an Omics situation. It just doesn’t work. And we have a lot of the scars, and the failures to prove that.” - Jeremy Forman (4:12) “You have to have the product mindset to deliver the value and the promise of AI data analytics. I think small, independent, autonomous, empowered squads with a product leader is the only way that you can iterate fast enough with [pharma data products].” - Jeremy Forman (8:46) “The biggest challenge is when we say data products. It means a lot of different things to a lot of different people, and it’s difficult to articulate what a data product is. Is it a view in a database? Is it a table? Is it a query? We’re all talking about it in different terms, and nobody’s actually delivering data products.” - Jeremy Forman (10:53) “I think when we’re talking about [data products] there’s some type of data asset that has value to an end-user, versus a report or an algorithm. I think it’s even hard for UX people to really understand how to think about an actual data product. I think it’s hard for people to conceptualize, how do we do design around that? It’s one of the areas I think I’ve seen the biggest challenges, and I think some of the areas we’ve learned the most. If you build a data product, it’s not accurate, and people are getting results that are incomplete… people will abandon it quickly.” - Jeremy Forman (15:56) “ I think that UX design and AI development or data science work is a magical partnership, but they often don’t know how to work with each other. That’s been a challenge, but I think investing in that has been critical to us. Even though we’ve had struggles… I think we’ve also done a good job of understanding the [user] experience and impact that we want to have. The prototype we shared [at CDIOQ] is driven by user experience and trying to get information in the hands of the research organization to understand some portfolio types of decisions that have been made in the past. And it’s been really successful.” - Jeremy Forman (24:59) “If you’re having technology conversations with your business users, and you’re focused only the technology output, you’re just building reports. [After adopting If we’re having technology conversations with our business users and only focused on the technology output, we’re just building reports. [After we adopted  a human-centered design approach], it was talking [with end-users] about outcomes, value, and adoption. Having that resource transformed the conversation, and I felt like our quality went up. I felt like our output went down, but our impact went up. [End-users] loved the tools, and that wasn’t what was happening before… I credit a lot of that to the human-centered design team.” - Jeremy Forman (26:39) “When you’re thinking about automation through machine learning or building algorithms for [clinical trial analysis], it becomes a harder dance between data scientists and human-centered design. I think there’s a lack of appreciation and understanding of what UX can do. Human-centered design is an empathy-driven understanding of users’ experience, their work, their workflow, and the challenges they have. I don’t think there’s an appreciation of that skill set.” - Jeremy Forman (29:20) “Are people excited about it? Is there value? Are we hearing positive things? Do they want us to continue? That’s really how I’ve been judging success. Is it saving people time, and do they want to continue to use it? They want to continue to invest in it. They want to take their time as end-users, to help with testing, helping to refine it. Those are the indicators. We’re not generating revenue, so what does the adoption look like? Are people excited about it? Are they telling friends? Do they want more? When I hear that the ten people [who were initial users] are happy and that they think it should be rolled out to the whole broader audience, I think that’s a good sign.” - Jeremy Forman (35:19)

Links Referenced LinkedIn: https://www.linkedin.com/in/jeremy-forman-6b982710/

We’re improving DataFramed, and we need your help! We want to hear what you have to say about the show, and how we can make it more enjoyable for you—find out more here. Data is no longer just for coders. With the rise of low-code tools, more people across organizations can access data insights without needing programming skills. But how can companies leverage these tools effectively? And what steps should they take to integrate them into existing workflows while upskilling their teams?  Michael Berthold is CEO and co-founder at KNIME, an open source data analytics company. He has more than 25 years of experience in data science, working in academia, most recently as a full professor at Konstanz University (Germany) and previously at University of California (Berkeley) and Carnegie Mellon, and in industry at Intel’s Neural Network Group, Utopy, and Tripos. Michael has published extensively on data analytics, machine learning, and artificial intelligence. In the episode, Adel and Michael explore low-code data science, the adoption of low-code data tools, the evolution of data science workflows, upskilling, low-code and code collaboration, data literacy, integration with AI and GenAI tools, the future of low-code data tools and much more.  Links Mentioned in the Show: KNIMEConnect with MichaelCode Along: Low-Code Data Science and Analytics with KNIMECourse: Introduction to KNIMERelated Episode: No-Code LLMs In Practice with Birago Jones & Karthik Dinakar, CEO & CTO at Pienso New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Help us become the #1 Data Podcast by leaving a rating & review! We are 67 reviews away! For anyone aiming to break into data analysis, Avery’s roadmap is the ultimate guide. With practical advice and clear steps, this episode sets you up for success in just 100 days. 💌 Join 30k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com//interviewsimulator ⌚ TIMESTAMPS 01:06 AI Avatars Talk About the Plan 02:04 Learning About Data Roles 03:55 Getting Good at Excel 04:55 Visualizing Data with Tableau 05:54 Learning SQL Basics 06:58 Starting Job Prep Early 07:15 Applying for Jobs Smartly 09:27 Capstone Project: Showing Off Your Skills 11:22 Last Tips and Encouragement 🔗 CONNECT WITH AVERY 🎥 YouTube Channel 🤝 LinkedIn 📸 Instagram 🎵 TikTok 💻 Website 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

Help us become the #1 Data Podcast by leaving a rating & review! We are 67 reviews away! Travel back to 1854 London and see how data visualization saved lives. John Snow’s use of data analytics to fight cholera is a groundbreaking story that still inspires analysts today. 💌 Join 30k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com//interviewsimulator ⌚ TIMESTAMPS 00:24 The Cholera Outbreak in London 01:04 John Snow's Revolutionary Hypothesis 02:58 Lessons for Modern Data Analysts 🔗 CONNECT WITH AVERY 🔗 CONNECT WITH AVERY 🎥 YouTube Channel 🤝 LinkedIn 📸 Instagram 🎵 TikTok 💻 Website 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

Intelligent Data Analytics for Bioinformatics and Biomedical Systems

The book analyzes the combination of intelligent data analytics with the intricacies of biological data that has become a crucial factor for innovation and growth in the fast-changing field of bioinformatics and biomedical systems. Intelligent Data Analytics for Bioinformatics and Biomedical Systems delves into the transformative nature of data analytics for bioinformatics and biomedical research. It offers a thorough examination of advanced techniques, methodologies, and applications that utilize intelligence to improve results in the healthcare sector. With the exponential growth of data in these domains, the book explores how computational intelligence and advanced analytic techniques can be harnessed to extract insights, drive informed decisions, and unlock hidden patterns from vast datasets. From genomic analysis to disease diagnostics and personalized medicine, the book aims to showcase intelligent approaches that enable researchers, clinicians, and data scientists to unravel complex biological processes and make significant strides in understanding human health and diseases. This book is divided into three sections, each focusing on computational intelligence and data sets in biomedical systems. The first section discusses the fundamental concepts of computational intelligence and big data in the context of bioinformatics. This section emphasizes data mining, pattern recognition, and knowledge discovery for bioinformatics applications. The second part talks about computational intelligence and big data in biomedical systems. Based on how these advanced techniques are utilized in the system, this section discusses how personalized medicine and precision healthcare enable treatment based on individual data and genetic profiles. The last section investigates the challenges and future directions of computational intelligence and big data in bioinformatics and biomedical systems. This section concludes with discussions on the potential impact of computational intelligence on addressing global healthcare challenges. Audience Intelligent Data Analytics for Bioinformatics and Biomedical Systems is primarily targeted to professionals and researchers in bioinformatics, genetics, molecular biology, biomedical engineering, and healthcare. The book will also suit academicians, students, and professionals working in pharmaceuticals and interpreting biomedical data.

You might think you know Maven Analytics and its Founder, Chris Dutton. But do you know the whole story? During this show, Chris will talk about how and why Maven got started, how it's evolved over the years, and where things are heading in the future. You'll leave this show with a deeper understanding of one of your favorite EdTech companies. What You'll Learn: Why Chris started Maven Analytics, and what the early days were like How the company has evolved over time What's coming up next for the company and for our learners   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   About our guest: Chris Dutton is an EdTech entrepreneur and best-selling Data Analytics instructor. As Founder and Chief Product Officer at Maven Analytics, his work has been featured by USA Today, Business Insider, Entrepreneur and the New York Times, reaching more than 1,000,000 students around the world. Follow Chris on LinkedIn

Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

Help us become the #1 Data Podcast by leaving a rating & review! We are 67 reviews away! Big changes are happening in the data world, and it’s not just about AI! It’s a mix of challenges and new chances in the data field. Let’s dig into what’s happening and why now’s the time to rethink your next career move. 💌 Join 30k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com//interviewsimulator 🔗 LIVE DATA TECHNOLOGIES: https://www.livedatatechnologies.com/ ⌚ TIMESTAMPS 01:10 - Data-Driven Insights on the Job Market 02:18 - The Rise of Data Engineering 03:49 - AI's Impact on Data Roles 04:44 - Data Analyst Jobs Are Still Growing 06:27 - Job Hopping in Data Roles 🔗 CONNECT WITH AVERY 🎥 YouTube Channel 🤝 LinkedIn 📸 Instagram 🎵 TikTok 💻 Website 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

Today's analytics and data science job market seems to be as competitive as it's ever been. So it's more important than ever to know what employers are looking for and have a solid plan of attack in your job search. In this episode, Luke Barousse and Kelly Adams will walk us through their insights from the job market, talk about exactly what employers are looking for, and lay out an actionable plan for you to start building skills that will help you in your career. You'll leave this show with a deeper understanding of the job market, and a concrete roadmap you can use to take your data skills and career to the next level.   What You'll Learn: Insights from a deep analysis of the data science and analytics job market The skills employers are looking for, and why they matter A roadmap for building key data science and data analytics skills   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   About our guests: Luke Barousse is a data analyst, YouTuber, and engineer who helps data nerds be more productive. Follow Luke on LinkedIn Subscribe to Luke's YouTube Channel Luke's Python, SQL, and ChatGPT Courses

Kelly Adams is a data analyst, course creator, and writer. Kelly's Website Follow Kelly on LinkedIn Datanerd.Tech   Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

Help us become the #1 Data Podcast by leaving a rating & review! We are 67 reviews away! Elijah Butler shares his journey from data analyst to senior roles. We talk about the key skills and strategies that can help you advance while staying true to your career goals. 💌 Join 30k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com//interviewsimulator ⌚ TIMESTAMPS 00:54 Differences Between Data Analyst and Senior Data Analyst 02:31 Elijah's Journey to Senior Data Analyst 09:16 The Importance of Soft Skills and Continuous Learning 12:42 Networking and Internal Promotions 16:47 Advice for Aspiring Senior Analysts 🔗 CONNECT WITH ELIJAH BUTLER: 🤝 LinkedIn: https://www.linkedin.com/in/elijahbutler 🎥 YouTube Channel: https://www.youtube.com/@ImElijahButler_ 🎵 TikTok: https://www.tiktok.com/@imelijahbutler 🔗 CONNECT WITH AVERY: 🔗 CONNECT WITH AVERY 🎥 YouTube Channel 🤝 LinkedIn 📸 Instagram 🎵 TikTok 💻 Website 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

Coalesce 2024: Making data rewarding at Bilt (Rewards)

Dive into the technical evolution of Bilt’s data infrastructure as they moved from fragmented, slow, and costly analytics to a streamlined, scalable, and holistic solution with dbt Cloud. In this session, the Bilt team will share how they implemented data modeling practices, established a robust CI/CD pipeline, and leveraged dbt’s Semantic Layer to enable a more efficient and trusted analytics environment. Attendees will gain a deep understanding of Bilt’s approach to data including: cost optimization, enhancing data accessibility and reliability, and most importantly, supporting scale and growth.

Speakers: Ben Kramer Director, Data & Analytics Bilt Rewards

James Dorado VP, Data Analytics Bilt Rewards

Nick Heron Senior Manager, Data Analytics Bilt Rewards

Read the blog to learn about the latest dbt Cloud features announced at Coalesce, designed to help organizations embrace analytics best practices at scale https://www.getdbt.com/blog/coalesce-2024-product-announcements

Coalesce 2024: Transitioning from dbt Core to dbt Cloud: A user story

Join us as we share our journey of migrating from dbt Core to dbt Cloud. We'll discuss why we made this shift – focusing on security, ownership, and standardization. Starting with separate team-based projects on dbt Core, we moved towards a unified structure, and eventually embraced dbt Cloud. Now, all teams follow a common structure and standardized requirements, ensuring better security and collaboration.

In our session, we'll explore how we improved our data analytics processes by migrating from dbt Core to dbt Cloud. Initially, each team had its way of working on dbt Core, leading to security risks and inconsistent practices. To address this, we transitioned to a more unified approach on dbt Core. This year we migrated dbt Cloud, which allowed us to centralize our data analytics workflows, enhancing security and promoting collaboration.

For scheduling we manage our own Airflow instance using AWS EKS. We use Datahub as data catalog.

Key points: Enhanced Security: dbt Cloud provided robust security features, helping us safeguard our data pipelines. Ownership and Collaboration: With dbt Cloud, teams took ownership of their projects while collaborating more effectively. Standardization: We enforced standardized requirements across all projects, ensuring consistency and efficiency, using dbt-project-evaluator.

Speakers: Alejandro Ivanez Platform Engineer DPG Media

Mathias Lavaert Principal Platform Engineer DPG Media

Read the blog to learn about the latest dbt Cloud features announced at Coalesce, designed to help organizations embrace analytics best practices at scale https://www.getdbt.com/blog/coalesce-2024-product-announcements

Coalesce 2024: From 0-35 dbt contributors: How the NBA scaled analytics engineering

Learn best practices, challenges, and outcomes the NBA experienced as they scaled from zero to 35 monthly active contributors to their dbt Cloud project over the course of one year. Keelan will review the state of analytics before dbt, and how widespread adoption has resulted in improved uptime, time to value, and organizational trust in data. He'll share specifics of the challenges faced, and how they were able to implement best practices and new organizational norms at various stages of the journey to combat these workflow and cultural hurdles. He'll also share the frameworks he and his team built and standardized on as they democratized data analytics to dozens of practitioners, and how they capitalized on small pockets of "ah-ha!" moments to translate it into lasting behavioral change.

Speaker: Keelan Smithers Data Product Manager, Analytics Engineering NBA

Read the blog to learn about the latest dbt Cloud features announced at Coalesce, designed to help organizations embrace analytics best practices at scale https://www.getdbt.com/blog/coalesce-2024-product-announcements

Coalesce 2024: How to leverage dbt for embedded domain knowledge across product engineering teams

In today's data-driven world, harnessing the power of data is no longer an option but a necessity for businesses to thrive. For product engineering teams in particular, timely access to accurate and contextual data is crucial for making informed decisions and monitoring success. In this conversation, Aakriti Kaul and Scott Henry, Data Scientists at Cisco, dive into Duo Security’s data modernization journey, bolstered by dbt Cloud and embedded context in data, aimed at empowering product teams with data access and insights to drive innovation.

At the end of this session we hope to leave attendees with the following takeaways: • Understand how an Embedded Data science model creates value across Product, Engineering and Data teams • Learn practical strategies for implementing dbt within product development workflows to accelerate decision making and drive innovation, in partnership with Analytics Engineering teams • Gain insights from real-world case studies of Duo’s Product Data teams that have successfully leveraged dbt to provide access to data and insights for product teams • Gain insights from our organizational experience using dbt to provide product teams with self-service access to contextual datasets

The presentation is designed for data scientists, analytics engineers and other professionals involved in product development who are interested in leveraging data to drive decision making and embedding context within their data workflows. Whether you're new to dbt or looking to optimize your existing data analytics workflows, this session will provide valuable insights and practical strategies for harnessing the power of dbt in partnership with product engineering teams.

Speakers: Aakriti Kaul Data Scientist Duo Security @ Cisco

Scott Henry Data Scientist Duo Security @ Cisco

Read the blog to learn about the latest dbt Cloud features announced at Coalesce, designed to help organizations embrace analytics best practices at scale https://www.getdbt.com/blog/coalesce-2024-product-announcements

Coalesce 2024 Keynote: Innovating with dbt
video
by Roxi Dahlke (dbt Labs) , Yannick Misteli (Roche) , Tobias Humpert (Siemens AG) , Tristan Handy (dbt Labs) , Amy Chen (Fishtown Analytics) , Greg McKeon (dbt Labs) , James Dorado (Bilt Rewards)

dbt Labs co-founder and CEO, Tristan Handy, unveils his vision for the analytics development lifecycle, highlighting how our mission to make data and AI more accessible and trustworthy is fueling innovation. Hear from data leaders who have unlocked incredible business value with dbt Cloud at scale, and get an exclusive look at the groundbreaking product features that are launching soon. And remember, what happens in Vegas could change the future of analytics and AI.

Read the blog to learn more about the product announcements: https://www.getdbt.com/blog/coalesce-2024-product-announcements

Speakers: Tristan Handy Founder & CEO dbt Labs

Amy Chen Product Manager dbt Labs

Greg McKeon Staff Product Manager dbt Labs

Roxi Dahlke Product Manager dbt Labs

James Dorado VP, Data Analytics Bilt Rewards

Tobias Humpert Siemens Data Cloud Product Owner Siemens AG

Yannick Misteli Head of Engineering Roche

Help us become the #1 Data Podcast by leaving a rating & review! We are 67 reviews away! Finding quality datasets doesn’t have to be hard. In this episode, we highlight seven must-know sources where you can easily grab free data for your next project. These resources are sure to inspire your work. Get FREE access to 1M+ Datasets here: https://datacareerjumpstart.com/datasets 💌 Join 30k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com//interviewsimulator ⌚ TIMESTAMPS 00:10 Kaggle 00:52 Data.World 01:23 Reddit's r/datasets 02:35 Awesome Datasets on GitHub 03:51 Google Dataset Search 04:57 Mendeley 05:42 UC Machine Learning Repository 🔗 CONNECT WITH AVERY 🎥 YouTube Channel 🤝 LinkedIn 📸 Instagram 🎵 TikTok 💻 Website 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

The Data Product Management In Action podcast, brought to you by Soda and executive producer Scott Hirleman, is a platform for data product management practitioners to share insights and experiences.  In Season 01, Episode 20, Nick is back and this time he is chatting with Ganesh Prasad. They dive into Ganesh's background as a data product manager and his journey from data science to product management. The discussion leads into the differences between internal and external products, the importance of user interviews and discovery, and the challenges and advantages of working in big tech and financial industries. Follow along as Ganesh shares some valuable tips and explains the importance of having a product mindset. About our host Nick Zervoudis: Nick is Head of Product at CKDelta, an AI software business within the CK Hutchison Holdings group. Nick oversees a portfolio of data products and works with sister companies to uncover new opportunities to innovate using data, analytics, and machine learning. Nick's career has revolved around data and advanced analytics from day one, having worked as an analyst, consultant, product manager, and instructor for startups, SMEs, and enterprises including PepsiCo, Sainsbury's, Lloyds Banking Group, IKEA, Capgemini Invent, BrainStation, QuantSpark, and Hg Capital. Nick is also the co-host of London's Data Product Management meetup, and speaks and writes regularly about data and AI product management. Connect with Nick on LinkedIn.   About our guest Ganesh Prasad: Ganesh is a Senior Product Lead in the Data Analytics division at Salesforce, bringing over 5 years of experience in data product management from both Salesforce and Mastercard. He has a proven track record of successfully launching and scaling products that meet customer needs. Ganesh has successfully managed and developed analytics, ML, and AI products across various domains, including marketing analytics, fraud detection, revenue forecasting, and platform optimization. Transitioning from a data scientist to a product manager, Ganesh is passionate about the intersection of data and product development. He leads the PM Community of Practice for the Data Analytics division at Salesforce and dedicates his spare time to mentoring others in the field. Connect with Ganesh on LinkedIn.  All views and opinions expressed are those of the individuals and do not necessarily reflect their employers or anyone else.  Join the conversation on LinkedIn.  Apply to be a guest or nominate someone that you know.  Do you love what you're listening to? Please rate and review the podcast, and share it with fellow practitioners you know. Your support helps us reach more listeners and continue providing valuable insights!   

Help us become the #1 Data Podcast by leaving a rating & review! We are 67 reviews away! Many people feel unqualified for a data analyst role, but there are ways to fight imposter syndrome. Learn how to boost your confidence with practical steps 💌 Join 30k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com//interviewsimulator ⌚ TIMESTAMPS 01:30 Step 1: Build Projects to Boost Confidence 03:38 Step 2: Ask 'What's the Worst That Can Happen?' 06:13 Step 3: Accept You Can’t Learn Everything 07:24 Step 4: Fake It Till You Make It 09:28 Bonus Tip: Use Affirmations to Fight Imposter Syndrome 🎞️ Positive Affirmations for Aspiring Data Analysts [Listen Daily] https://youtu.be/vsuZfsYNO30?si=DctCusBQ6OaIlg9s 🔗 CONNECT WITH AVERY 🎥 YouTube Channel 🤝 LinkedIn 📸 Instagram 🎵 TikTok 💻 Website 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

Help us become the #1 Data Podcast by leaving a rating & review! We are 67 reviews away! Find out which tools are most in demand, which are easiest to learn, and the best order to learn them. Learn about the Data Learning Ladder and how to quickly get started in the data industry. 💌 Join 30k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com//interviewsimulator ⌚ TIMESTAMPS 02:23 The Big Six Data Skills 05:55 The Data Learning Ladder 🔗 CONNECT WITH AVERY 🎥 YouTube Channel 🤝 LinkedIn 📸 Instagram 🎵 TikTok 💻 Website 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!

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Data Engineering for Machine Learning Pipelines: From Python Libraries to ML Pipelines and Cloud Platforms

This book covers modern data engineering functions and important Python libraries, to help you develop state-of-the-art ML pipelines and integration code. The book begins by explaining data analytics and transformation, delving into the Pandas library, its capabilities, and nuances. It then explores emerging libraries such as Polars and CuDF, providing insights into GPU-based computing and cutting-edge data manipulation techniques. The text discusses the importance of data validation in engineering processes, introducing tools such as Great Expectations and Pandera to ensure data quality and reliability. The book delves into API design and development, with a specific focus on leveraging the power of FastAPI. It covers authentication, authorization, and real-world applications, enabling you to construct efficient and secure APIs using FastAPI. Also explored is concurrency in data engineering, examining Dask's capabilities from basic setup to crafting advanced machine learning pipelines. The book includes development and delivery of data engineering pipelines using leading cloud platforms such as AWS, Google Cloud, and Microsoft Azure. The concluding chapters concentrate on real-time and streaming data engineering pipelines, emphasizing Apache Kafka and workflow orchestration in data engineering. Workflow tools such as Airflow and Prefect are introduced to seamlessly manage and automate complex data workflows. What sets this book apart is its blend of theoretical knowledge and practical application, a structured path from basic to advanced concepts, and insights into using state-of-the-art tools. With this book, you gain access to cutting-edge techniques and insights that are reshaping the industry. This book is not just an educational tool. It is a career catalyst, and an investment in your future as a data engineering expert, poised to meet the challenges of today's data-driven world. What You Will Learn Elevate your data wrangling jobs by utilizing the power of both CPU and GPU computing, and learn to process data using Pandas 2.0, Polars, and CuDF at unprecedented speeds Design data validation pipelines, construct efficient data service APIs, develop real-time streaming pipelines and master the art of workflow orchestration to streamline your engineering projects Leverage concurrent programming to develop machine learning pipelines and get hands-on experience in development and deployment of machine learning pipelines across AWS, GCP, and Azure Who This Book Is For Data analysts, data engineers, data scientists, machine learning engineers, and MLOps specialists

Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib

Learn how to leverage the scientific computing and data analysis capabilities of Python, its standard library, and popular open-source numerical Python packages like NumPy, SymPy, SciPy, matplotlib, and more. This book demonstrates how to work with mathematical modeling and solve problems with numerical, symbolic, and visualization techniques. It explores applications in science, engineering, data analytics, and more. Numerical Python, Third Edition, presents many case study examples of applications in fundamental scientific computing disciplines, as well as in data science and statistics. This fully revised edition, updated for each library's latest version, demonstrates Python's power for rapid development and exploratory computing due to its simple and high-level syntax and many powerful libraries and tools for computation and data analysis. After reading this book, readers will be familiar with many computing techniques, including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling, and machine learning. What You'll Learn Work with vectors and matrices using NumPy Review Symbolic computing with SymPy Plot and visualize data with Matplotlib Perform data analysis tasks with Pandas and SciPy Understand statistical modeling and machine learning with statsmodels and scikit-learn Optimize Python code using Numba and Cython Who This Book Is For Developers who want to understand how to use Python and its ecosystem of libraries for scientific computing and data analysis.