This project implements a 2024 paper introducing innovative methods for visualization and explainability in graph tasks like node classification and link predication. Performance will be evaluated against baselines on existing and novel datasets, with a focus on explainability in graph tasks.

This project creates an AI healthcare companion with three key features: a Symptom Checker for disease predictions, a Drug Interaction Advisor for explaining medication risks, and a Disease News Finder for summarizing recent research. Using LLMs, it provides user-friendly insights to improve symptom understanding, medication safety, and disease awareness.

Help us become the #1 Data Podcast by leaving a rating & review! We are 67 reviews away! Will AI replace data analysts? Let’s clear up the confusion and talk about what’s next. 💌 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 00:47 The Reality of AI in the Data World 01:47 AI as a Tool, Not a Replacement 02:54 Adapting to AI 06:26 Practical Tips for Using AI 09:05 Conclusion and Career Advice 🔗 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

Amazon is using generative AI across all of its businesses to improve productivity and efficiency and to enhance a broad range of customer experiences -- including shopping and selling on Amazon, watching Prime Video, and interacting with Alexa. Amazon CEO Andy Jassy returned to the re:Invent mainstage to explain Amazon’s internal generative AI strategy and how AWS is helping its customers get the most out of the transformative technology.

Learn more: Customer stories: https://go.aws/genaicustomers AWS events: https://go.aws/events

Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.ly/316g9t4

ABOUT AWS Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts. AWS is the world’s most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.

generativeAI #AmazonBedrock #AmazonQ #AWSTrainium #AWSInferentia #AI #ML #Amazon #retail #Media #AWS

This morning, a great article came across my feed that gave me PTSD, asking if Iceberg is the Hadoop of the Modern Data Stack?

In this rant, I bring the discussion back to a central question you should ask with any hot technology - do you need it at all? Do you need a tool built for the top 1% of companies at a sufficient data scale? Or is a spreadsheet good enough?

Link: https://blog.det.life/apache-iceberg-the-hadoop-of-the-modern-data-stack-c83f63a4ebb9

❤️ If you like my podcasts, please like and rate it on your favorite podcast platform.

🤓 My works:

📕Fundamentals of Data Engineering: https://www.oreilly.com/library/view/fundamentals-of-data/9781098108298/

🎥 Deeplearning.ai Data Engineering Certificate: https://www.coursera.org/professional-certificates/data-engineering

🔥Practical Data Modeling: https://practicaldatamodeling.substack.com/

🤓 My SubStack: https://joereis.substack.com/

Health insurance denials are a big topic in America right now. Holden Karau joins me to talk about using AI to appeal health claim insurance denials.

Fight Health Insurance: https://fighthealthinsurance.com/

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. The modern leader faces unprecedented challenges, from managing a multi-generational workforce to integrating AI into daily operations. How can leaders cultivate a human-centric approach that fosters trust and innovation? What role does vulnerability play in effective leadership, and how can it coexist with the need for bold decision-making? As professionals strive to lead with authenticity, what strategies can help leaders raise the tide for all boats? Dana Maor is the global co-head for the McKinsey People & Organizational Performance Practice and is a member of its Knowledge Council. As a senior partner, she works with leaders globally to transform their organizations and themselves and serves as co-dean of multiple McKinsey leadership programs. In the episode, Adel and Dana explore the complexities of modern leadership, the importance of human-centric leadership, balancing empathy with performance, navigating imposter syndrome, and the evolving role of leaders in the age of AI. Links Mentioned in the Show: The Journey to Leadership by Dana MaorMcKinsey & Company - Organizational Health IndexSkill Track: Artificial Intelligence (AI) LeadershipRelated Episode: How Data can Enable Effective Leadership with Dr. Constance Dierickx, The Decision DoctorRewatch sessions from RADAR: Forward Edition 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

Summary The core task of data engineering is managing the flows of data through an organization. In order to ensure those flows are executing on schedule and without error is the role of the data orchestrator. Which orchestration engine you choose impacts the ways that you architect the rest of your data platform. In this episode Hugo Lu shares his thoughts as the founder of an orchestration company on how to think about data orchestration and data platform design as we navigate the current era of data engineering.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementIt’s 2024, why are we still doing data migrations by hand? Teams spend months—sometimes years—manually converting queries and validating data, burning resources and crushing morale. Datafold's AI-powered Migration Agent brings migrations into the modern era. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today to learn how Datafold can automate your migration and ensure source to target parity. As a listener of the Data Engineering Podcast you clearly care about data and how it affects your organization and the world. For even more perspective on the ways that data impacts everything around us don't miss Data Citizens® Dialogues, the forward-thinking podcast brought to you by Collibra. You'll get further insights from industry leaders, innovators, and executives in the world's largest companies on the topics that are top of mind for everyone. In every episode of Data Citizens® Dialogues, industry leaders unpack data’s impact on the world, from big picture questions like AI governance and data sharing to more nuanced questions like, how do we balance offense and defense in data management? In particular I appreciate the ability to hear about the challenges that enterprise scale businesses are tackling in this fast-moving field. The Data Citizens Dialogues podcast is bringing the data conversation to you, so start listening now! Follow Data Citizens Dialogues on Apple, Spotify, YouTube, or wherever you get your podcasts.Your host is Tobias Macey and today I'm interviewing Hugo Lu about the data platform and orchestration ecosystem and how to navigate the available optionsInterview IntroductionHow did you get involved in building data platforms?Can you describe what an orchestrator is in the context of data platforms?There are many other contexts in which orchestration is necessary. What are some examples of how orchestrators have adapted (or failed to adapt) to the times?What are the core features that are necessary for an orchestrator to have when dealing with data-oriented workflows?Beyond the bare necessities, what are some of the other features and design considerations that go into building a first-class dat platform or orchestration system?There have been several generations of orchestration engines over the past several years. How would you characterize the different coarse groupings of orchestration engines across those generational boundaries?How do the characteristics of a data orchestrator influence the overarching architecture of an organization's data platform/data operations?What about the reverse?How have the cycles of ML and AI workflow requirements impacted the design requirements for data orchestrators?What are the most interesting, innovative, or unexpected ways that you have seen data orchestrators used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on data orchestration?When is an orchestrator the wrong choice?What are your predictions and/or hopes for the future of data orchestration?Contact Info MediumLinkedInParting Question From your perspective, what is the biggest thing data teams are missing in the technology today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links OrchestraPrevious Episode: Overview Of The State Of Data OrchestrationCronArgoCDDAGKubernetesData MeshAirflowSSIS == SQL Server Integration ServicesPentahoKettleDataVoloNiFiPodcast EpisodeDagstergRPCCoalescePodcast EpisodedbtDataHubPalantirThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Episode Summary: In this episode, Mukundan simplifies the concept of Dynamic Topic Modeling (DTM) for listeners and discusses its transformative impact on businesses. DTM is a machine learning method used to track the evolution of themes in text data over time. It helps companies to make smarter decisions by staying in tune with customer needs and market trends. Key Topics Covered: Introduction to Dynamic Topic ModelingWhat it is and why it matters for businesses.Real-world examples like customer reviews and social media trends.How Dynamic Topic Modeling WorksOver time, analyze text data (e.g., reviews, surveys, reports).Groups words into topics such as price, quality, or features.Applications of Dynamic Topic ModelingAdjusting marketing strategies to customer priorities.Enhancing product features based on evolving feedback.Predicting and responding to trends like sustainability in physical products.Tracking employee feedback to refine HR strategies and reduce churn.Step-by-Step Guide to Implementing DTMCollecting text data (e.g., reviews, surveys).Using tools like Python or pre-built software for analysis.Generating clear visuals and actionable insights.Benefits for BusinessesUnderstanding customer and employee feedback more effectively.Staying ahead of competitors.Saving time while making informed, data-driven decisions.Call to ActionEncourage listeners to explore DTM to gain a competitive edge.Mukundan invites questions and collaboration via email: mukundansankar.substack.com.Memorable Quotes: "Dynamic Topic Modeling helps businesses turn text data into actionable business strategies.""With DTM, you can stay ahead of competitors by understanding what customers truly care about over time.""It's not just about making decisions but smarter decisions driven by data."Real-Life Examples: Amazon Reviews: How DTM categorizes feedback into price, durability, and other topics.Marketing Adjustments: Shifting focus to features customers prioritize.Trend Analysis: Tracking the rise of sustainability in customer demands.Employee Insights: Using DTM to predict trends in employee satisfaction and churn.Resources Mentioned: Dynamic Topic Modeling Tools: Python and other software solutions for beginners and professionals.Email for Guidance: mukundansankar.substack.com

Global industry is getting a boost from firming final demand. We are bullish for manufacturing in the coming months—to be supported by next week’s expected December reports on US retail sales and G-4 flash PMI readings. Central banks are still easing but the drivers have turned more domestic. Next week’s projected 25bp Fed cut would bring the 2024 easing cycle to 100bp, despite material upside surprises this year to both growth and core inflation forecasts. (The Weekender will return January 3; Happy holidays to all!)

Speakers:

Bruce Kasman

Joseph Lupton

This podcast was recorded on 13 December 2024.

This communication is provided for information purposes only. Institutional clients please visit www.jpmm.com/research/disclosures for important disclosures. © 2024 JPMorgan Chase & Co. All rights reserved. This material or any portion hereof may not be reprinted, sold or redistributed without the written consent of J.P. Morgan. It is strictly prohibited to use or share without prior written consent from J.P. Morgan any research material received from J.P. Morgan or an authorized third-party (“J.P. Morgan Data”) in any third-party artificial intelligence (“AI”) systems or models when such J.P. Morgan Data is accessible by a third-party. It is permissible to use J.P. Morgan Data for internal business purposes only in an AI system or model that protects the confidentiality of J.P. Morgan Data so as to prevent any and all access to or use of such J.P. Morgan Data by any third-party.

In this podcast episode, we talked with Isabella Bicalho about Career advice, learning, and featuring women in ML and AI.

About the Speaker:

Isabella is a Machine Learning Engineer and Data Scientist with three years of hands-on AI development experience. She draws upon her early computational research expertise to develop ML solutions. While contributing to open-source projects, she runs a newsletter dedicated to showcasing women's accomplishments in data science.

During this event, the guest discussed her transition into machine learning, her freelance work in AI, and the growing AI scene in France. She shared insights on freelancing versus full-time work, the value of open-source contributions, and developing both technical and soft skills. The conversation also covered career advice, mentorship, and her Substack series on women in data science, emphasizing leadership, motivation, and career opportunities in tech.

0:00 Introduction 1:23 Background of Isabella Bicalho 2:02 Transition to machine learning 4:03 Study and work experience 5:00 Living in France and language learning 6:03 Internship experience 8:45 Focus areas of Inria 9:37 AI development in France 10:37 Current freelance work 11:03 Freelancing in machine learning 13:31 Moving from research to freelancing 14:03 Freelance vs. full-time data science 17:00 Finding first freelance client 18:00 Involvement in open-source projects 20:17 Passion for open-source and teamwork 23:52 Starting new projects 25:03 Community project experience 26:02 Teaching and learning 29:04 Contributing to open-source projects 32:05 Open-source tools vs. projects 33:32 Importance of community-driven projects 34:03 Learning resources 36:07 Green space segmentation project 39:02 Developing technical and soft skills 40:31 Gaining insights from industry experts 41:15 Understanding data science roles 41:31 Project challenges and team dynamics 42:05 Turnover in open-source projects 43:05 Managing expectations in open-source work 44:50 Mentorship in projects 46:17 Role of AI tools in learning 47:59 Overcoming learning challenges 48:52 Discussion on substack 49:01 Interview series on women in data 50:15 Insights from women in data science 51:20 Impactful stories from substack 53:01 Leadership challenges in projects 54:19 Career advice and opportunities 56:07 Motivating others to step out of comfort zone 57:06 Contacting for substack story sharing 58:00 Closing remarks and connections

🔗 CONNECT WITH ISABELLA BICALHO Github: github https://github.com/bellabf LinkedIn:   / isabella-frazeto  

🔗 CONNECT WITH DataTalksClub Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html Datalike Substack - https://datalike.substack.com/ LinkedIn:   / datatalks-club  

Karthikeyan Rajendran, Director of Go to Market in the NVAIE Group at Nvidia, joins us on this episode of the Data Unchained podcast to talk about data and NVIDIA's data workflows. We also discuss how AI is playing a part in edge computing, how we can take full advantage of our GPUs, and what the difference in GPUs is and what it means.

podcast #ai #datainnovation #dataprivacy #highperformancecomputing #dataprotection #technology #datasecurity #supercomputing #data

Cyberpunk by jiglr | https://soundcloud.com/jiglrmusic Music promoted by https://www.free-stock-music.com Creative Commons Attribution 3.0 Unported License https://creativecommons.org/licenses/by/3.0/deed.en_US Hosted on Acast. See acast.com/privacy for more information.

In this episode, Bryce and I catch up and chat about the AI generated ADSP Episode 211. Link to Episode 212 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)Twitter ADSP: The PodcastConor HoekstraBryce Adelstein LelbachShow Notes Date Generated: 2024-12-10 Date Released: 2024-12-13 Gantt ChartADSP Episode 211: Power, Politics and Misconduct in C++ ✨ADSP Episode 211 GitHub DiscussionWhisper AIAudacityFinal SolutionBlog Post: On "Safe" C++ - HELL IN A REFCELLReddit CommentsNotebookLMIntro Song Info Miss You by Sarah Jansen https://soundcloud.com/sarahjansenmusic Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/l-miss-you Music promoted by Audio Library https://youtu.be/iYYxnasvfx8

Em uma conversa incrível com os especialistas da Bain & Company, uma das maiores consultorias estratégicas do mundo, exploramos o impacto da inteligência artificial generativa nos negócios e o futuro dessa tecnologia. Falamos sobre como a Gen AI tem transformado a forma como as empresas trabalham, abordando desde as estratégias até a implantação de projetos reais que estão remodelando omercados e entregando resultados tangíveis.

Neste episódio do Data Hackers — a maior comunidade de AI e Data Science do Brasil-, conheçam conheçam: Felipe Fiamozzini (Sócio na Bain & Company); Lara Marinelli (Lead Machine Learning Engineer na Bain); e Carlos Azevedo (Sócio associado na Bain). Juntos, eles compartilham práticas recomendadas, stacks utilizadas e as tendências emergentes que prometem moldar o futuro da Gen AI nos próximos anos.

Lembrando que você pode encontrar todos os podcasts da comunidade Data Hackers no Spotify, iTunes, Google Podcast, Castbox e muitas outras plataformas.

Falamos no episódio:

Carlos Azevedo — Sócio associado na Bain Lara Marinelli — Lead Machine Learning Engineer na Bain Felipe Fiamozzini — Sócio na Bain & Company

Nossa Bancada Data Hackers:

Paulo Vasconcellos — Co-founder da Data Hackers e Principal Data Scientist na Hotmart. Monique Femme — Head of Community Management na Data Hackers

Referências:

Preencha a pesquisa State of Data Brazil: https://www.stateofdata.com.br/podcast

🌟 Session Overview 🌟

Session Name: Automating Web Workflows with LLMs Speaker: Jiri Moravcik Session Description: This talk will delve into Apify's approach to automation and its workflow with Large Language Models (LLMs), highlighting the seamless integration and strategic use of AI in data extraction from the web. Participants will gain insight into how Apify serves clients like Intercom and Rocket Money by employing cutting-edge techniques to scrape and structure online data. The presentation will showcase specific case studies involving ChatGPT, illustrating the methodologies and tools utilized to transform raw data into valuable insights for clients.

🚀 About Big Data and RPA 2024 🚀

Unlock the future of innovation and automation at Big Data & RPA Conference Europe 2024! 🌟 This unique event brings together the brightest minds in big data, machine learning, AI, and robotic process automation to explore cutting-edge solutions and trends shaping the tech landscape. Perfect for data engineers, analysts, RPA developers, and business leaders, the conference offers dual insights into the power of data-driven strategies and intelligent automation. 🚀 Gain practical knowledge on topics like hyperautomation, AI integration, advanced analytics, and workflow optimization while networking with global experts. Don’t miss this exclusive opportunity to expand your expertise and revolutionize your processes—all from the comfort of your home! 📊🤖✨

📅 Yearly Conferences: Curious about the evolution of QA? Check out our archive of past Big Data & RPA sessions. Watch the strategies and technologies evolve in our videos! 🚀 🔗 Find Other Years' Videos: 2023 Big Data Conference Europe https://www.youtube.com/playlist?list=PLqYhGsQ9iSEpb_oyAsg67PhpbrkCC59_g 2022 Big Data Conference Europe Online https://www.youtube.com/playlist?list=PLqYhGsQ9iSEryAOjmvdiaXTfjCg5j3HhT 2021 Big Data Conference Europe Online https://www.youtube.com/playlist?list=PLqYhGsQ9iSEqHwbQoWEXEJALFLKVDRXiP

💡 Stay Connected & Updated 💡

Don’t miss out on any updates or upcoming event information from Big Data & RPA Conference Europe. Follow us on our social media channels and visit our website to stay in the loop!

🌐 Website: https://bigdataconference.eu/, https://rpaconference.eu/ 👤 Facebook: https://www.facebook.com/bigdataconf, https://www.facebook.com/rpaeurope/ 🐦 Twitter: @BigDataConfEU, @europe_rpa 🔗 LinkedIn: https://www.linkedin.com/company/73234449/admin/dashboard/, https://www.linkedin.com/company/75464753/admin/dashboard/ 🎥 YouTube: http://www.youtube.com/@DATAMINERLT

🌟 Session Overview 🌟

Session Name: Can AI Face Your (Potential) Customers? Lessons Learned with Multilingual Enterprises Speaker: Alius Petraska Session Description: Vytenis will share their experience on when AI can directly interact with customers or when human intervention is still necessary. Their solution helps sales and customer service (CS) agents be more effective on calls. This provides a unique perspective on understanding when AI excels and when it falls short, still requiring clients to call or send emails.

🚀 About Big Data and RPA 2024 🚀

Unlock the future of innovation and automation at Big Data & RPA Conference Europe 2024! 🌟 This unique event brings together the brightest minds in big data, machine learning, AI, and robotic process automation to explore cutting-edge solutions and trends shaping the tech landscape. Perfect for data engineers, analysts, RPA developers, and business leaders, the conference offers dual insights into the power of data-driven strategies and intelligent automation. 🚀 Gain practical knowledge on topics like hyperautomation, AI integration, advanced analytics, and workflow optimization while networking with global experts. Don’t miss this exclusive opportunity to expand your expertise and revolutionize your processes—all from the comfort of your home! 📊🤖✨

📅 Yearly Conferences: Curious about the evolution of QA? Check out our archive of past Big Data & RPA sessions. Watch the strategies and technologies evolve in our videos! 🚀 🔗 Find Other Years' Videos: 2023 Big Data Conference Europe https://www.youtube.com/playlist?list=PLqYhGsQ9iSEpb_oyAsg67PhpbrkCC59_g 2022 Big Data Conference Europe Online https://www.youtube.com/playlist?list=PLqYhGsQ9iSEryAOjmvdiaXTfjCg5j3HhT 2021 Big Data Conference Europe Online https://www.youtube.com/playlist?list=PLqYhGsQ9iSEqHwbQoWEXEJALFLKVDRXiP

💡 Stay Connected & Updated 💡

Don’t miss out on any updates or upcoming event information from Big Data & RPA Conference Europe. Follow us on our social media channels and visit our website to stay in the loop!

🌐 Website: https://bigdataconference.eu/, https://rpaconference.eu/ 👤 Facebook: https://www.facebook.com/bigdataconf, https://www.facebook.com/rpaeurope/ 🐦 Twitter: @BigDataConfEU, @europe_rpa 🔗 LinkedIn: https://www.linkedin.com/company/73234449/admin/dashboard/, https://www.linkedin.com/company/75464753/admin/dashboard/ 🎥 YouTube: http://www.youtube.com/@DATAMINERLT

Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. This week, we’re bringing you a special episode straight from RootsConf, our annual internal knowledge-sharing extravaganza! Hosts Murilo and Bart sit down with Tim and Ben, data strategy experts, for a lively chat about the state of generative AI as it transitions from a buzzword to a business tool. Highlights from this episode: Generative AI adoption: Are companies finally moving beyond pilot purgatory?The environmental cost of AI: Can emerging techniques reduce its heavy energy footprint?Bridging the knowledge gap: What’s missing for widespread AI adoption in organizations?Future trends: How generative AI might reshape personalization and business processes in 2025.Plus, we dive into the Gartner Hype Cycle and its relevance in understanding AI’s journey from innovation to disillusionment and beyond. Get ready to dive deep into AI’s evolving role and its impact on industries, sustainability, and society. Hit play and join the discussion!

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. Imagine spending millions on data tools only to find you can’t trust the answers they provide. What if different teams define key metrics in different ways? Without a clear, unified approach, chaos reigns, and confidence erodes. What role do data governance and semantic layers play in helping you trust the AI tools you build and the insights you get from your data? Sarah Levy is a seasoned executive with extensive experience in data science, artificial intelligence, and technology leadership. Currently serving as Co-Founder and CEO of Euno since January 2023, Sarah has previously held significant positions, including VP of Data Science and Data Analytics for Real Estate at Pagaya and CTO at Sight Diagnostics, where innovative advancements in blood testing were achieved. With a strong foundation in research and development from roles at Sight Diagnostics and Natural Intelligence, as well as a robust background in cyber security gained from tenure at the IDF, Sarah has consistently driven impactful decision-making and technological advancements throughout their career. Academic credentials include a Master's degree in Condensed Matter Physics from the Weizmann Institute of Science and a Bachelor's degree in Mathematics and Physics from The Hebrew University of Jerusalem. In the episode, Richie and Sarah explore the challenges of data governance, the role of semantic layers in ensuring data trust, the emergence of analytics engineers, the integration of AI in data processes, and much more. Links Mentioned in the Show: EunoConnect with SarahCourse: Responsible AI Data ManagementRelated Episode: How Data Leaders Can Make Data Governance a Priority with Saurabh Gupta, Chief Strategy & Revenue Officer at The Modern Data CompanyRewatch sessions from RADAR: Forward Edition 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

Communicate the data that is powering our changing world with this essential text The advent of machine learning and neural networks in recent years, along with other technologies under the broader umbrella of ‘artificial intelligence,’ has produced an explosion in Data Science research and applications. Data Visualization, which combines the technical knowledge of how to work with data and the visual and communication skills required to present it, is an integral part of this subject. The expansion of Data Science is already leading to greater demand for new approaches to Data Visualization, a process that promises only to grow. Data Visualization in R and Python offers a thorough overview of the key dimensions of this subject. Beginning with the fundamentals of data visualization with Python and R, two key environments for data science, the book proceeds to lay out a range of tools for data visualization and their applications in web dashboards, data science environments, graphics, maps, and more. With an eye towards remarkable recent progress in open-source systems and tools, this book offers a cutting-edge introduction to this rapidly growing area of research and technological development. Data Visualization in R and Python readers will also find: Coverage suitable for anyone with a foundational knowledge of R and Python Detailed treatment of tools including the Ggplot2, Seaborn, and Altair libraries, Plotly/Dash, Shiny, and others Case studies accompanying each chapter, with full explanations for data operations and logic for each, based on Open Data from many different sources and of different formats Data Visualization in R and Python is ideal for any student or professional looking to understand the working principles of this key field.