The integration of speech AI into everyday business operations is reshaping how we communicate and process information. With applications ranging from customer service to quality control, understanding the nuances of speech AI is crucial for professionals. How do you tackle the complexities of different languages and accents? What are the best practices for implementing speech AI in your organization? Explore the transformative power of speech AI and learn how to overcome the challenges it presents in your professional landscape. Alon Peleg serves as the Chief Operating Officer (COO) at aiOla, a position he assumed in May 2024. With over two decades of leadership experience at renowned companies like Wix, Cisco, and Intel, he is widely recognized in the tech industry for his expertise, dynamic leadership, and unwavering dedication. At aiOla, Alon plays a key role in driving innovation and strategic growth, contributing to the company’s mission of developing cutting-edge solutions in the tech space. His appointment is regarded as a pivotal step in aiOla’s expansion and continued success. Gill Hetz is the VP of AI at aiOla where he leverages his expertise in data integration and modeling. Gill was previously active in the oil and gas industry since 2009, holding roles in engineering, research, and data science. From 2018 to 2021, Gill held key positions at QRI, including Project Manager and SaaS Product Manager. In the episode, Richie, Alon, and Gill explore the intricacies of speech AI, its components like ASR, NLU, and TTS, real-world applications in industries such as retail and pharmaceuticals, challenges like accents and background noise, and the future of voice interfaces in technology, and much more. Links Mentioned in the Show: aiOlaConnect with Alon and GillCourse: Spoken Language Processing in PythonRelated Episode: Building Multi-Modal AI Applications with Russ d'Sa, CEO & Co-founder of LiveKitSign up to attend RADAR: Skills 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

Send us a text Innovating on Wall Street: Kristen McGarry on Data, AI, and Technical Sales 🎧 Tune in for an insider’s look at the technical strategies shaping the future of finance. Kristen McGarry, Principal Account Technical Lead for IBM’s Financial Services Market, returns to Making Data Simple to dive deeper into the intersection of technology and Wall Street. Based in NYC, Kristen works with the world’s largest financial institutions to drive innovation, accelerate time to value, and implement cutting-edge solutions across software, hardware, and services. In this episode, we break down the realities of technical sales, the evolving role of data science in finance, and what Wall Street is getting right (or wrong) about AI. Kristen also shares key insights on the challenges of working with financial giants and predictions for the future of tech in banking. ⏱ Episode Highlights: 📍 02:57 – An Intro to Kristen McGarry 📍 04:36 – Why IBM? 📍 09:25 – The Attraction of Data Science 📍 11:51 – A Day in the Life of an Account Technical Leader 📍 13:30 – Technical Sales versus Sales 📍 15:05 – Continuing to Innovate 📍 19:09 – Dealing with Wall Street 📍 20:17 – The Methodology 📍 22:23 – The How of Technical Sales 📍 23:05 – Continuous Learning 📍 28:03 – Management System 📍 30:34 – Wall Street Learnings 📍 32:20 – Biggest Challenge 📍 33:08 – The Data Challenge 📍 34:22 – Best Data Science Use Cases in Finance 📍 36:14 – What Do Clients Miss on AI? 📍 38:09 – Predictions LinkedIn: https://www.linkedin.com/in/kristen-mcgarry/ Website: https://www.ibm.com/

MakingDataSimple #DataScience #AIinFinance #TechSales #WallStreet #IBM #Innovation #FinancialServices #Leadership #ContinuousLearning #AI

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.

Are you ready to grow your skills in AI and data science? A great place to start is learning to build and use APIs in real-world data and AI projects. API skills have become essential for AI and data science success, because they are used in a variety of ways in these fields. With this practical book, data scientists and software developers will gain hands-on experience developing and using APIs with the Python programming language and popular frameworks like FastAPI and StreamLit. As you complete the chapters in the book, you'll be creating portfolio projects that teach you how to: Design APIs that data scientists and AIs love Develop APIs using Python and FastAPI Deploy APIs using multiple cloud providers Create data science projects such as visualizations and models using APIs as a data source Access APIs using generative AI and LLMs

How can businesses align AI with their values? How does decentralized data impact industries from healthcare to autonomous driving? In this episode of Data Unchained we sit down with Michael Hemenway, a Data Science Consultant, to discuss AI ethics, governance, and the future of responsible technology adoption.

AI #EthicalAI #DataScience #MachineLearning #ArtificialIntelligence #DataUnchained #TechPodcast #CIO #CTO #DecentralizedData #Governance #DeepLearning #BusinessStrategy #FutureTech

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If you want to build a strong career in data, this show is for you. We welcomed the new face of Mavens of Data, Kristen Kehrer, who shared her best advice for data professionals and those aspiring toward a data career. You'll leave the show with some actionable tips and some of the best career advice directly from one of our favorite data pros of all time. What You'll Learn: What you should focus on if you're trying to land your first job How to succeed once you are in that initial role How to think about building a successful career long-term   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   About our guest: Kristen Kehrer has been providing innovative & practical statistical modeling solutions in the utilities, healthcare, and eCommerce sectors since 2010. Alongside her professional accomplishments, she achieved recognition as a LinkedIn Top Voice in Data Science & Analytics in 2018. Kristen is also the founder of Data Moves Me, LLC, and has previously served as a faculty member and subject matter expert at the Emeritus Institute of Management and UC Berkeley Ext.

 Kristen lights up on stage and has spoken at conferences like ODSC, DataScienceGO, BI+Analytics Conference, Boye Conference, and Big Data LDN, etc.

She holds a Master of Science degree in Applied Statistics from Worcester Polytechnic Institute and a Bachelor of Science degree in Mathematics.

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

As businesses collect more data than ever, the question arises: is bigger always better? Companies are beginning to question whether massive datasets and complex infrastructures are truly delivering results or just adding unnecessary costs. How can you align your data strategy with your actual needs? Could focusing on smaller, more manageable datasets improve efficiency and save resources while still delivering valuable insights? Dr. Madelaine Daianu is the Head of Data & AI at Credit Karma, Inc. Before joining the company in June 2023, she served as Head of Data and Pricing at Belong Home, Inc. Earlier in her career, Daianu has held numerous senior roles in data science and machine learning at The RealReal, Facebook, and Intuit. Daianu earned a Bachelor of Applied Science in Bioengineering and Mathematics from the University of Illinois at Chicago and a Ph.D. in Bioengineering and Biomedical Engineering from the University of California, Los Angeles. In the episode, Richie and Madelaine explore generative AI applications at Credit Karma, the importance of data infrastructure, the role of explainability in fintech, strategies for scaling AI processes, and much more. Links Mentioned in the Show: Credit KarmaConnect with MaddieSkill Track: AI Business FundamentalsRelated Episode: Effective Product Management for AI with Marily Nika, Gen AI Product Lead at Google AssistantSign up to attend RADAR: Skills 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

With AI agents and GPU acceleration at the forefront, data science is entering a new era of efficiency and innovation. How are AI copilots transforming the way data scientists code and solve problems? Are they a reliable partner or a source of new complexities? On the other hand, the move to GPU-accelerated data science tools is revolutionizing model training and experimentation. What does this mean for the future of data science workflows? Explore these cutting-edge developments and their impact on the industry. Jean-Francois got a PhD in machine learning in the previous millennium. Given the AI winter at the time, he worked for a while on mathematical optimization software as dev manager for CPLEX in a startup. He came back to Machine Learning when IBM acquired the startup. Since then he discovered Kaggle and became one of the best Kagglers in the world. He joined NVIDIA 5 years ago and leads the NVIDIA Kaggle Grandmaster team there. Chris Deotte is a senior data scientist at NVIDIA. Chris has a Ph.D. in computational science and mathematics with a thesis on optimizing parallel processing. Chris is a Kaggle 4x grandmaster. In the episode, Richie, Jean-Francois, and Chris explore the transformative role of AI agents in data science, the impact of GPU acceleration on workflows, the evolution of competitive data science techniques, the importance of model evaluation and communication skills, and the future of data science roles in an AI-driven world, and much more. Links Mentioned in the Show: NVIDIANVIDIA RapidsFew shot learningConnect with Jean-Francois on Linkedin and Kaggle and check out Chris on KaggleCourse: Winning a Kaggle Competition in PythonRelated Episode: Becoming a Kaggle GrandmasterSign up to attend RADAR: Skills 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

One of the most pressing challenges in our increasingly data-driven world is the Data Deluge—the overwhelming flood of information that we generate and record every single day. With us are three experts from the University of Virginia’s School of Data Science. Phil Bourne, a professor of biomedical engineering and the founding dean of the school of data science, is joined by Terence Johnson and Alex Gates, both assistant professors of data science. Together, they have been exploring innovative methods to make sense of the vast oceans of data we’re all swimming in.

This episode unpacks the challenges of the data deluge—what it means for businesses, researchers, and society at large—and explore the strategies we can use to navigate it. How do we make sense of so much information? How do we ensure the ethical use of this data? And what opportunities does this overwhelming flood of data open up for the future?

In this podcast episode, we talked with Alexander Guschin about launching a career off Kaggle.

About the Speaker: Alexander Guschin is a Machine Learning Engineer with 10+ years of experience, a Kaggle Grandmaster ranked 5th globally, and a teacher to 100K+ students. He leads DS and SE teams and contributes to open-source ML tools. 0:00 Starting with Machine Learning: Challenges and Early Steps 13:05 Community and Learning Through Kaggle Sessions 17:10 Broadening Skills Through Kaggle Participation 18:54 Early Competitions and Lessons Learned 21:10 Transitioning to Simpler Solutions Over Time
23:51 Benefits of Kaggle for Starting a Career in Machine Learning
29:08 Teamwork vs. Solo Participation in Competitions
31:14 Schoolchildren in AI Competitions 42:33 Transition to Industry and MLOps 50:13 Encouraging teamwork in student projects 50:48 Designing competitive machine learning tasks 52:22 Leaderboard types for tracking performance 53:44 Managing small-scale university classes 54:17 Experience with Coursera and online teaching 59:40 Convincing managers about Kaggle's value 61:38 Secrets of Kaggle competition success 63:11 Generative AI's impact on competitive ML 65:13 Evolution of automated ML solutions 66:22 Reflecting on competitive data science experience

🔗 CONNECT WITH ALEXANDER GUSCHINLinkedin - https://www.linkedin.com/in/1aguschin/Website - https://www.aguschin.com/

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If you're looking to transition into a career in Data Science or Data Analytics, this one is for you! In this episode with Data Science Infinity Founder Andrew Jones, you'll learn what hiring managers are looking for, and hear some great advice for what you should and shouldn't be doing if you want to break into data. You'll leave with a solid roadmap of steps to take, and practical tips and actionable advice to help you launch your own data career. What You'll Learn: What hiring managers are looking for in candidates How you can build an amazing portfolio to showcase your skills Where you should focus your learning to maximize your career results   This session was part of our OPEN CAMPUS week in October, which included 6 days of live expert sessions.   Register for free to be part of the next live session: https://bit.ly/3XB3A8b     Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

In this episode, I chat with Daliana Liu of The Data Scientist Show! She talks about her career journey, including her tenure at Amazon, and offers practical advice on making data science impactful in business. Tune in to discover what truly makes a great data scientist and check out Daliana's Data Science Career Accelerator course, designed to help data scientists advance their careers: https://maven.com/dalianaliu/ds-career 💌 Join 10k+ 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 00:00 - Introduction 13:55 - Focusing on non-technical skills 18:07 - The importance of communication skills 23:11 - How to have positive visibility in your company 28:25 - Data Science & ML Career Accelerators 🔗 CONNECT WITH DALIANA 🎥 YouTube Channel: https://www.youtube.com/@UCa0RTSXWyZdh7IciV9r-3ow 🤝 LinkedIn: https://www.linkedin.com/in/dalianaliu/ 📸 Instagram: https://www.instagram.com/dalianaliu/ Website: https://www.dalianaliu.blog/ 🔗 CONNECT WITH AVERY 🎥 YouTube Channel: https://www.youtube.com/@averysmith 🤝 LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://instagram.com/datacareerjumpstart 🎵 TikTok: https://www.tiktok.com/@verydata 💻 Website: https://www.datacareerjumpstart.com/ 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

Join the millions of people already using Microsoft Access and become a database power-user in no time! In the newly revised edition of Microsoft Access For Dummies, professional database developer and Access extraordinaire Laurie Ulrich-Fuller walks you through the ins-and-outs of one of the world's most popular database platforms. This is the perfect beginner's guide to Microsoft Access, showing you how to create databases, extract data, create reports, and more. The author demonstrates a ton of tips, tricks, and best practices you can use immediately to create, maintain, and improve your databases. You'll also find: Updates outlining edge browser controls in forms Step-by-step guides explaining how to import, export, and edit data Easy-to-follow query-writing tutorials to help you find the exact data you're looking for when you need it Whether you're a database novice or a data science whiz, Microsoft Access For Dummies has the info you need to supercharge your database skills. It's the perfect, how-to guide to get you up-to-speed on everything you need to know to get started with Microsoft's world-famous database app.

Complete eight data science projects that lock in important real-world skills—along with a practical process you can use to learn any new technique quickly and efficiently. Data analysts need to be problem solvers—and The Well-Grounded Data Analyst will teach you how to solve the most common problems you'll face in industry. You'll explore eight scenarios that your class or bootcamp won’t have covered, so you can accomplish what your boss is asking for. In The Well-Grounded Data Analyst you'll learn: High-value skills to tackle specific analytical problems Deconstructing problems for faster, practical solutions Data modeling, PDF data extraction, and categorical data manipulation Handling vague metrics, deciphering inherited projects, and defining customer records The Well-Grounded Data Analyst is for junior and early-career data analysts looking to supplement their foundational data skills with real-world problem solving. As you explore each project, you'll also master a proven process for quickly learning new skills developed by author and Half Stack Data Science podcast host David Asboth. You'll learn how to determine a minimum viable answer for your stakeholders, identify and obtain the data you need to deliver, and reliably present and iterate on your findings. The book can be read cover-to-cover or opened to the chapter most relevant to your current challenges. About the Technology Real world data analysis is messy. Success requires tackling challenges like unreliable data sources, ambiguous requests, and incompatible formats—often with limited guidance. This book goes beyond the clean, structured examples you see in classrooms and bootcamps, offering a step-by-step framework you can use to confidently solve any data analysis problem like a pro. About the Book The Well-Grounded Data Analyst introduces you to eight scenarios that every data analyst is bound to face. You’ll practice author David Asboth’s results-oriented approach as you model data by identifying customer records, navigate poorly-defined metrics, extract data from PDFs, and much more! It also teaches you how to take over incomplete projects and create rapid prototypes with real data. Along the way, you’ll build an impressive portfolio of projects you can showcase at your next interview. What's Inside Deconstructing problems Handling vague metrics Data modeling Categorical data manipulation About the Reader For early-career data scientists. About the Author David Asboth is a data generalist educator, and software architect. He co-hosts the Half Stack Data Science podcast. Quotes Well reasoned and well written, with approaches to solve many sorts of data analysis problems. - Naomi Ceder, Fellow of the Python Software Foundation An excellent resource for any aspiring data scientist! - Andrew R. Freed, IBM David’s clear and repeatable framework will give you confidence to tackle open-ended stakeholder requests and reach an answer much faster! - Shaun McGirr, DevOn Software Services A book version of shadowing a senior data analyst while they explain handling frequent data problems at work, including all the ugly gotchas. - Randy Au, Google

Mergulhamos no universo dos AI Agents e discutimos por que eles são considerados a próxima revolução em Data & AI. Nossos convidados exploram desde os conceitos básicos até aplicações reais, incluindo como empresas estão criando agentes de forma autônoma e o papel do Langflow — uma plataforma de AI Agents, fundada por um brasileiro, que já é destaque no cenário internacional — nesse ecossistema.

Neste episódio do Data Hackers — a maior comunidade de AI e Data Science do Brasil - cconheçam Mikaeri Ohana - Head de Dados e IA na CI&T & Content Creator at Explica Mi , e o Gabriel Almeida - Founder & CTO @ Langflow.

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

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

Gabriel Lages — Co-founder da Data Hackers e Data & Analytics Sr. Director na Hotmart.

Referências:

Participe do Evento do ifood: https://lu.ma/si2mn42p Blog Data Hackers - Langflow: Conheça uma plataforma de AI Agents fundada por um Brasileiro que já é destaque no cenário internacional: https://www.datahackers.news/p/langflow-conheca-uma-plataforma-de-ai-agents-fundada-por-um-brasileiro Langflow: https://www.langflow.org/pt/ Site da DataStax:
 Blog Data Hackers - CrewAI : https://www.datahackers.news/p/crew-ai-a-startup-brasileira-que-esta-dominando-o-mercado-de-ai-agents

When you know the cause of an event, you can affect its outcome. This accessible introduction to causal inference shows you how to determine causality and estimate effects using statistics and machine learning. A/B tests or randomized controlled trials are expensive and often unfeasible in a business environment. Causal Inference for Data Science reveals the techniques and methodologies you can use to identify causes from data, even when no experiment or test has been performed. In Causal Inference for Data Science you will learn how to: Model reality using causal graphs Estimate causal effects using statistical and machine learning techniques Determine when to use A/B tests, causal inference, and machine learning Explain and assess objectives, assumptions, risks, and limitations Determine if you have enough variables for your analysis It’s possible to predict events without knowing what causes them. Understanding causality allows you both to make data-driven predictions and also intervene to affect the outcomes. Causal Inference for Data Science shows you how to build data science tools that can identify the root cause of trends and events. You’ll learn how to interpret historical data, understand customer behaviors, and empower management to apply optimal decisions. About the Technology Why did you get a particular result? What would have lead to a different outcome? These are the essential questions of causal inference. This powerful methodology improves your decisions by connecting cause and effect—even when you can’t run experiments, A/B tests, or expensive controlled trials. About the Book Causal Inference for Data Science introduces techniques to apply causal reasoning to ordinary business scenarios. And with this clearly-written, practical guide, you won’t need advanced statistics or high-level math to put causal inference into practice! By applying a simple approach based on Directed Acyclic Graphs (DAGs), you’ll learn to assess advertising performance, pick productive health treatments, deliver effective product pricing, and more. What's Inside When to use A/B tests, causal inference, and ML Assess objectives, assumptions, risks, and limitations Apply causal inference to real business data About the Reader For data scientists, ML engineers, and statisticians. About the Author Aleix Ruiz de Villa Robert is a data scientist with a PhD in mathematical analysis from the Universitat Autònoma de Barcelona. Quotes With intuitive explanations, application-focused insights, and real-world examples, this book offers immense practical value. - Philipp Bach, Maintainer of the DoubleML libraries for Python and R An essential guide for navigating the complexities of real-world data analysis. - Adi Shavit, SWAPP A must-read! Demystifies causal inference with a blend of theory and practice. - Karan Gupta, SunPower Corporation Causal relationships can mask and distort results. This book provides a set of tools to extract insights correctly. - Peter V. Henstock, Harvard Extension

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. Dive into conversations that should flow as smoothly as your morning coffee (but don’t), where industry insights meet laid-back banter. Whether you’re a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let’s get into the heart of data, unplugged style! This week, we dive into: The creative future with AI: is generative AI helping or hurting creators? Environmental concerns of AI: the hidden costs of AI’s growing capabilities—how much energy do these models actually consume, and is it worth it?AI copyright controversies: Mark Zuckerberg’s LLaMA model faces criticism for using copyrighted materials like content from the notorious LibGen database.Trump vs. AI regulation: The former president repeals Biden’s AI executive order, creating a Wild West approach to AI development in the U.S. How will this impact innovation and global competition?Search reimagined with Perplexity AI: A new era of search blending conversational AI and personalized data unification. Could this be the future of information retrieval?Apple Intelligence on pause: Apple's AI-generated news alerts face a bumpy road. For more laughs, check out the dedicated subreddit AppleIntelligenceFail.Rhai scripting for Rust: Empowering Rust developers with an intuitive embedded scripting language to make extensibility a breeze.Poisoned text for scrapers: Exploring creative ways to protect web content from unauthorized scraping by AI systems.The rise of the AI Data Engineer: Is this a new role in data science, or are we just rebranding existing skills?

The Data Product Management In Action podcast, brought to you by executive producer Scott Hirleman, is a platform for data product management practitioners to share insights and experiences. In the 25th celebration minisode of Data Product Management in Action, hosts Frannie Helforoush and Nadiem von Heydebrand reflect on the progress of data product management in 2024. They highlight the growing clarity and recognition of the field, the rise of AI product management, and the importance of thoughtful integration without succumbing to overhype. The episode revisits key 2024 discussions on building data platforms, decision support products, and data mesh implementation. Looking forward to 2025, they foresee increased interest and adoption, emphasizing the field's potential for driving organizational value. Frannie and Nadiem express excitement for future episodes and community contributions. About our Host Nadiem von Heydebrand: Nadiem is CEO and Co-Founder at Mindfuel. In 2019, he combined his passion for data science with product management and is a thought leader for data product management today, aiming to prove true value contribution from data. Working as an expert in the data industry for over a decade now, he has seen hundreds of data science initiatives, built scaled data teams and enabled global organizations like Volkswagen, Munich Re, Allianz, Red Bull, Vorwerk to become data-driven. With Mindfuel “Delight”, a Data Product Management SaaS solution combined with professional services, he brought in experience from hands-on challenges like scaling out data platforms and architecture, implementing data mesh concepts or transforming AI performance into business performance to delight consumers all over the globe. Connect with Nadiem on LinkedIn

About our Host Frannie Helforoush: From coding to crafting customer-centric products, my journey began as a software engineer and evolved into a strategic product manager. With an innate curiosity for problem-solving, I fuse my expertise in data and product management to create impactful solutions as a data product manager now. With a background in both software engineering and product management, I seamlessly bridge the gap between the data and product worlds. I thrive on making data accessible and actionable for driving product innovation and  ensuring that product thinking is applied to every aspect of data management. Connect with Frannie 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!  

Statistical quantitative methods are vital for financial valuation models and benchmarking machine learning models in finance. This book explores the theoretical foundations of statistical models, from ordinary least squares (OLS) to the generalized method of moments (GMM) used in econometrics. It enriches your understanding through practical examples drawn from applied finance, demonstrating the real-world applications of these concepts. Additionally, the book delves into non-linear methods and Bayesian approaches, which are becoming increasingly popular among practitioners thanks to advancements in computational resources. By mastering these topics, you will be equipped to build foundational models crucial for applied data science, a skill highly sought after by software engineering and asset management firms. The book also offers valuable insights into quantitative portfolio management, showcasing how traditional data science tools can be enhanced with machine learning models. These enhancements are illustrated through real-world examples from finance and econometrics, accompanied by Python code. This practical approach ensures that you can apply what you learn, gaining proficiency in the statsmodels library and becoming adept at designing, implementing, and calibrating your models. By understanding and applying these statistical models, you enhance your data science skills and effectively tackle financial challenges. What You Will Learn Understand the fundamentals of linear regression and its applications in financial data analysis and prediction Apply generalized linear models for handling various types of data distributions and enhancing model flexibility Gain insights into regime switching models to capture different market conditions and improve financial forecasting Benchmark machine learning models against traditional statistical methods to ensure robustness and reliability in financial applications Who This Book Is For Data scientists, machine learning engineers, finance professionals, and software engineers

Many of us look forward to our Spotify wrapped at the end of the year. It's fun to see your whole year in music reflected back to you in the form of auras and moods and, of course, ranked lists, all powered by the cold, hard data of our listening habits. But there's so much more data available to visualize as art. That's what our guest, Pete Cybriwsky does. Pete is an entrepreneur and an award winning artist building at the intersection of data and art. In this podcast, you'll hear him in conversation with Lane Rasberry wikimedian in residence at the UVA School of data science. 

If you want to learn more about Pete's work, check out his new app Day By Data.

To turn your spotify data into art, visit ngenart.com.

By introducing a range of AI-enhanced products that amplify creativity and interactivity across our platforms, Buzzfeed has been able to connect with the largest global audience of young people online to cement its role as the defining digital media company of the AI era. Notably, some of Buzzfeed's most successful tools and content experiences thrive on the power of small, focused datasets. Still wondering how Shrek fits into the picture? You'll have to watch!

Video from: https://smalldatasf.com/

📓 Resources Big Data is Dead: https://motherduck.com/blog/big-data-... Small Data Manifesto: https://motherduck.com/blog/small-dat... Why Small Data?: https://benn.substack.com/p/is-excel-... Small Data SF: https://www.smalldatasf.com/

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Discover how BuzzFeed's Data team, led by Gilad Cohen, harnesses AI for creative purposes, leveraging large language models (LLMs) and generative image capabilities to enhance content creation. This video explores how machine learning teams build tools to create new interactive media experiences, focusing on augmenting creative workflows rather than replacing jobs, allowing readers to participate more deeply in the content they consume.

We dive into the core data science problem of understanding what a piece of content is about, a crucial step for improving content recommendation systems. Learn why traditional methods fall short and how the team is constantly seeking smaller, faster, and more performant models. This exploration covers the evolution from earlier architectures like DistilBERT to modern, more efficient approaches for better content representation, clustering, and user personalization.

A key technique explored is the use of text embeddings, which are dense, low-dimensional vector representations of data. This video provides an accessible explanation of embeddings as a form of compressed knowledge, showing how BuzzFeed creates a unique vector for each article. This allows for simple vector math to find semantically similar content, forming a foundational infrastructure for powerful ranking and recommender systems.

Explore how BuzzFeed leverages generative image capabilities to create new interactive formats. The journey began with Midjourney experiments and evolved to building custom tools by fine-tuning a Stable Diffusion XL model using LORA (Low-Rank Approximation). This advanced technique provides greater control over image output, enabling the rapid creation of viral AI generators that respond to trending topics and allow for massive user engagement.

Finally, see a practical application of machine learning for content optimization. BuzzFeed uses its vast historical dataset from Bayesian A/B testing to train a model that predicts headline performance. By generating multiple headline candidates with an LLM like Claude and running them through this predictive model, they can identify the winning headline. This showcases how to use unique, in-house data to build powerful tools that improve click-through rates and drive engagement, pointing to a significant transformation in how media is created and consumed.