Send us a text Money Ball is back! Nancy Hensley, Chief Marketing Officer for Stats Perform, gives us the latest on data analytics in sports. If you like sports don't listen unless you have time to be entertained. Show Notes 04:09 What does Money Ball look like now?07:30 Mrs Chicago's personal update08:40 Fan website: The Analyst11:16 Stats Perform for the rest of us17:25 Sports tech competitors18:34 Monetizing data. $115M for NFL data! What?27:44 Broadcaster and PressboxLinkedin: https://www.linkedin.com/in/nancyhensley/ Website: https://statsperform.com/ 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. 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.

MakingDataSimple #MoneyBall #DataAnalytics #StatsPerform #SportsTech #MakingData Simple

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.

Está no ar, o Data Hackers News !! Os assuntos mais quentes da semana, com as principais notícias da área de Dados, IA e Tecnologia, que você também encontra na nossa Newsletter semanal, agora no Podcast do Data Hackers !!

Aperte o play e ouça agora, o Data Hackers News dessa semana !

Para saber tudo sobre o que está acontecendo na área de dados, se inscreva na Newsletter semanal:

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.datahackers.news/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Conheça nossos comentaristas do Data Hackers News:

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Monique Femme⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Paulo Vasconcellos

⁠Matérias/assuntos comentados:

Após Efeito DeepSeek, as ações da Nvidia desabam;

OpenAI anuncia Operator: uma IA que usa o browser para fazer tarefas.

Demais canais do Data Hackers:

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Site⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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Remco Broekmans and I chat about data modeling and the business, Data Vault, and using AI to accelerate data modeling.

Michael Hanson, senior US and Canadian economist, and Gabriel Lozano, Head of Mexico Economics, discuss their latest Research Note on the Trump administration’s threatened 25% tariffs on Canada and Mexico. They explore the motivations, risks for retaliation, potential economic implications, and consequences for USMCA.

This podcast was recorded on January 28, 2025.

This communication is provided for information purposes only.  Institutional clients can view the related report at https://www.jpmm.com/research/content/GPS-4894757-0 for more information; please visit www.jpmm.com/research/disclosures for important disclosures.

© 2025 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.

Want to leapfrog 99% of your peers in the data analyst field (within the next THREE TO SIX months)? Tune in to discover the strategies you need! Listen to this NEXT: The ONLY Framework To Become A Data Analyst in 2025 (SPN Method) https://youtu.be/XUxWQgh3soo 💌 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 00:06 - Become laser-focused on your 'why' 03:34 - Focus on what matters to land the job 05:37 - Quit on being quiet, share your work! 07:51 - Networking 11:44 - Maximizing Your Time 🔗 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

As multimodal AI continues to grow, professionals are exploring new skills to harness its potential. From understanding real-time APIs to navigating new application architectures, the landscape is shifting. How can developers stay ahead in this evolving field? What opportunities do AI agents present for automating tasks and enhancing productivity? And how can businesses ensure they're ready for the future of AI-driven interactions? Russ D'Sa is the CEO & Co-founder at Livekit. Russ is building the transport layer for AI computing. He founded Livekit, the company that powers voice chat for OpenAI and Character.ai. Previously, he was a Product Manager at Medium and an engineer at Twitter. He's also a serial entrepreneur, having previously founded mobile search platform Evie Labs. In the episode, Richie and Russ explore the evolution of voice AI, the challenges of building voice applications, the rise of video AI, the implications of deep fakes, the potential of AI-generated worlds, the future of AI in customer service and education, and much more. Links Mentioned in the Show: LiveKitChatGPT VoiceCourse: Developing LLM Applications with LangChainRelated Episode: Creating High Quality AI Applications with Theresa Parker & Sudhi Balan, Rocket SoftwareRewatch 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

Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. Fully understanding how machine learning algorithms function is essential for any serious ML engineer. In Machine Learning Algorithms in Depth you’ll explore practical implementations of dozens of ML algorithms including: Monte Carlo Stock Price Simulation Image Denoising using Mean-Field Variational Inference EM algorithm for Hidden Markov Models Imbalanced Learning, Active Learning and Ensemble Learning Bayesian Optimization for Hyperparameter Tuning Dirichlet Process K-Means for Clustering Applications Stock Clusters based on Inverse Covariance Estimation Energy Minimization using Simulated Annealing Image Search based on ResNet Convolutional Neural Network Anomaly Detection in Time-Series using Variational Autoencoders Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probabilistic algorithms, you’ll learn the fundamentals of Bayesian inference and deep learning. You’ll also explore the core data structures and algorithmic paradigms for machine learning. Each algorithm is fully explored with both math and practical implementations so you can see how they work and how they’re put into action. About the Technology Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. This book guides you from the core mathematical foundations of the most important ML algorithms to their Python implementations, with a particular focus on probability-based methods. About the Book Machine Learning Algorithms in Depth dissects and explains dozens of algorithms across a variety of applications, including finance, computer vision, and NLP. Each algorithm is mathematically derived, followed by its hands-on Python implementation along with insightful code annotations and informative graphics. You’ll especially appreciate author Vadim Smolyakov’s clear interpretations of Bayesian algorithms for Monte Carlo and Markov models. What's Inside Monte Carlo stock price simulation EM algorithm for hidden Markov models Imbalanced learning, active learning, and ensemble learning Bayesian optimization for hyperparameter tuning Anomaly detection in time-series About the Reader For machine learning practitioners familiar with linear algebra, probability, and basic calculus. About the Author Vadim Smolyakov is a data scientist in the Enterprise & Security DI R&D team at Microsoft. Quotes I love this book! It shows you how to implement common ML algorithms in plain Python with only the essential libraries, so you can see how the computation and math works in practice. - Junpeng Lao, Senior Data Scientist at Google I highly recommend this book. In the era of ChatGPT real knowledge of algorithms is invaluable. - Vatsal Desai, InfoDesk Explains algorithms so well that even a novice can digest it. - Harsh Raval, Zymr

While absent of immediate trade war, the start of the Trump administration brought little clarity to the US policy path ahead, and we do not change our key calls. Against this backdrop, the global economy is humming along with news supporting our call for global industry to perk up in the coming months. Among the many central banks meeting next week, the Fed (hold) and ECB (-25bp) will be a reminder of the stark divergence between these two economies. 

Speakers:

Bruce Kasman

Joseph Lupton

This podcast was recorded on 24 January 2025.

This communication is provided for information purposes only. Institutional clients please visit www.jpmm.com/research/disclosures for important disclosures. © 2025 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.

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?

Leadership is facing unprecedented challenges in today's socio-political and economic climate. As the lines between work and personal life blur, professionals are seeking workplaces that prioritize humanity and purpose. How can leaders create environments that support employee well-being and connection? With AI's growing presence, how do we balance technological integration with maintaining a human-centered approach? Dr. Christie Smith is a renowned leadership expert, visionary thinker, and founder of The Humanity Studio, a pioneering research and advisory institute dedicated to improving the way we live by revolutionizing the way we work. With over 35 years of experience advising Fortune 500 companies and holding global leadership roles at Accenture, Apple, and Deloitte, Dr. Smith has shaped the future of leadership, talent strategy, and organizational culture across industries. Dr. Kelly Monahan is Managing Director of the Upwork Research Institute, leading their future of work research program. Her research has been recognized and published in both applied and academic journals, including MIT Sloan Management Review and the Journal of Strategic Management. In 2018, Kelly released her first book, “How Behavioral Economics Influences Management Decision-Making: A New Paradigm” (Academic Press/Elsevier Publishers). In 2019, Kelly gave her first TedX talk on the future of work. Kelly is frequently quoted in the media on talent decision-making and the future of work. She also has written over a dozen publications and is a sought-after speaker on how to apply new management and talent models in knowledge-based organizations. Kelly holds a B.S. from Rochester Institute of Technology, an M.S. from Roberts Wesleyan College, and a Ph.D. in Organizational Leadership from Regent University. In the episode, Richie, Christie, and Kelly explore leadership transformations driven by crises, the rise of human-centered workplaces, the integration of AI with human intelligence, the evolving skill landscape, the emergence of gray-collar work, and much more. Links Mentioned in the Show: Essential: How Distributed Teams, Generative AI, and Global Shifts Are Creating a New Human-Powered LeadershipThe Humanity StudioUpwork Research InstituteConnect with Christie and KellySkill Track: AI Business FundamentalsRelated Episode: Leadership in the AI Era with Dana Maor, Senior Partner at McKinsey & CompanyRewatch sessions from RADAR: Forward Edition New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business

Chip Huyen joins me to chat about AI Engineering, AI Agents, and much more.

In this episode, host Jason Foster is joined by Lara Menke, Leadership Psychologist & Executive Coach. 

Together they explore leadership development and the application of business psychology to improve workplace dynamics and team performance. 

They also discuss the transformative potential of emotionally intelligent, self-aware, and empathetic leadership in fostering resilient and effective teams. 

*****  

Cynozure is a leading data, analytics and AI company that helps organisations to reach their data potential. It works with clients on data and AI strategy, data management, data architecture and engineering, analytics and AI, data culture and literacy, and data leadership. The company was named one of The Sunday Times' fastest-growing private companies in both 2022 and 2023, and recognised as The Best Place to Work in Data by DataIQ in 2023 and 2024. 

Supported by Our Partners • Sonar —  Trust your developers – verify your AI-generated code. • Vanta —Automate compliance and simplify security with Vanta. — In today's episode of The Pragmatic Engineer, I'm joined by Charity Majors, a well-known observability expert – as well as someone with strong and grounded opinions. Charity is the co-author of "Observability Engineering" and brings extensive experience as an operations and database engineer and an engineering manager. She is the cofounder and CTO of observability scaleup Honeycomb. Our conversation explores the ever-changing world of observability, covering these topics: • What is observability? Charity’s take • What is “Observability 2.0?” • Why Charity is a fan of platform teams • Why DevOps is an overloaded term: and probably no longer relevant • What is cardinality? And why does it impact the cost of observability so much? • How OpenTelemetry solves for vendor lock-in  • Why Honeycomb wrote its own database • Why having good observability should be a prerequisite to adding AI code or using AI agents • And more! — Timestamps (00:00) Intro  (04:20) Charity’s inspiration for writing Observability Engineering (08:20) An overview of Scuba at Facebook (09:16) A software engineer’s definition of observability  (13:15) Observability basics (15:10) The three pillars model (17:09) Observability 2.0 and the shift to unified storage (22:50) Who owns observability and the advantage of platform teams  (25:05) Why DevOps is becoming unnecessary  (27:01) The difficulty of observability  (29:01) Why observability is so expensive  (30:49) An explanation of cardinality and its impact on cost (34:26) How to manage cost with tools that use structured data  (38:35) The common worry of vendor lock-in (40:01) An explanation of OpenTelemetry (43:45) What developers get wrong about observability  (45:40) A case for using SLOs and how they help you avoid micromanagement  (48:25) Why Honeycomb had to write their database  (51:56) Companies who have thrived despite ignoring conventional wisdom (53:35) Observability and AI  (59:20) Vendors vs. open source (1:00:45) What metrics are good for  (1:02:31) RUM (Real User Monitoring)  (1:03:40) The challenges of mobile observability  (1:05:51) When to implement observability at your startup  (1:07:49) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • How Uber Built its Observability Platform https://newsletter.pragmaticengineer.com/p/how-uber-built-its-observability-platform  • Building an Observability Startup https://newsletter.pragmaticengineer.com/p/chronosphere  • How to debug large distributed systems https://newsletter.pragmaticengineer.com/p/antithesis  • Shipping to production https://newsletter.pragmaticengineer.com/p/shipping-to-production  — See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠ — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

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!  

Está no ar, o Data Hackers News !! Os assuntos mais quentes da semana, com as principais notícias da área de Dados, IA e Tecnologia, que você também encontra na nossa Newsletter semanal, agora no Podcast do Data Hackers !!

Aperte o play e ouça agora, o Data Hackers News dessa semana !

Para saber tudo sobre o que está acontecendo na área de dados, se inscreva na Newsletter semanal:

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.datahackers.news/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Conheça nossos comentaristas do Data Hackers News:

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Monique Femme⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Paulo Vasconcellos

⁠Matérias/assuntos comentados:

Perplexity AI quer comprar o TikTok;

Google anuncia Gemini para Gmail e Google Docs gratuitamente;

OpenAI anuncia fim de testes do o3 e anuncia o3-mini para as próximas semanas.

Citado no Episódio: As Tendências para Dados e AI em 2025 - Data Hackers Podcast #100

Demais canais do Data Hackers:

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CLEAR AND CONCISE TECHNIQUES FOR USING ANALYTICS TO DELIVER BUSINESS IMPACT AT ANY ORGANIZATION Organizations have more data at their fingertips than ever, and their ability to put that data to productive use should be a key source of sustainable competitive advantage. Yet, business leaders looking to tap into a steady and manageable stream of “actionable insights” often, instead, get blasted with a deluge of dashboards, chart-filled slide decks, and opaque machine learning jargon that leaves them asking, “So what?” Analytics the Right Way is a guide for these leaders. It provides a clear and practical approach to putting analytics to productive use with a three-part framework that brings together the realities of the modern business environment with the deep truths underpinning statistics, computer science, machine learning, and artificial intelligence. The result: a pragmatic and actionable guide for delivering clarity, order, and business impact to an organization’s use of data and analytics. The book uses a combination of real-world examples from the authors’ direct experiences—working inside organizations, as external consultants, and as educators—mixed with vivid hypotheticals and illustrations—little green aliens, petty criminals with an affinity for ice cream, skydiving without parachutes, and more—to empower the reader to put foundational analytical and statistical concepts to effective use in a business context.

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