Featured Issues

Luca Massaron on Kaggle mastery, plus Sairam Sundaresan on scalable ML and GenAI systems.Packt and Go1 Invite You to Shape a New Study on Developer LearningAs AI generates more learning content, it is becoming harder to see where expert input really makes a difference. Packt has recently partnered with Go1 to create a short study looking at how developersactually learntoday, and when structured courses still matter alongside AI tools.If you work with learning or rely on it to build skills, your perspective would be useful. The survey takes under5minutestocomplete,and the results will be sharedin a study published in March.Take the 5-Minute SurveySubscribe|Submit a tip|Advertise with UsWelcome to DataPro #164. As data science and ML roles continue to evolve, standing out increasingly depends on how well you can demonstrate real-world problem solving, not just model knowledge.So, if you’ve ever wondered whether Kaggle is “just competitions,” or why so many strong data scientists and ML engineers still credit it for major career breakthroughs, this issue is for you.In this edition, Luca Massaron, co-author of The Kaggle Book, 2nd Edition, breaks down what Kaggle truly offers beyond leaderboards. He explains how notebooks, datasets, and competition workflows help build a visible record of problem solving, experimentation, and technical judgment. More importantly, he shows how this experience translates into real-world skills and interview-ready stories using the STAR framework.To support your learning, our authors have also created a free reference cheatsheet that maps all the libraries covered in the book, giving you a clear learning path as you work through the resources. You can download it here:Kaggle Book Cheatsheet.💡 Workshop Spotlight: Machine Learning and Generative AI System Design WorkshopJoin Sairam Sundaresan, AI Engineering Leader, for a hands-on system design workshop on February 28, focused on building machine learning and generative AI systems that actually scale. In this live session, you’ll move beyond model demos and learn how experienced architects design end-to-end AI systems by balancing cost, latency, quality, and risk.Through guided exercises and design sprints, you’ll practice making real architectural trade-offs and defining success metrics that go beyond accuracy. Whether you’re an ML engineer, architect, or product leader, this workshop equips you with reusable frameworks to design AI systems that hold up in production and evolve with changing models and regulations.Register Now and Save 35%Use DATAPRO35 at checkout for early access savings and reserve your seat.Cheers,Merlyn ShelleyGrowth Lead, PacktThe Essential Asset: Leveraging Kaggle Experience in a Competitive Professional LandscapeEngaging with the Kaggle platform offers a clear advantage for data science professionals seeking to emerge in complex and challenging job market situations, such as the recent one marked by widespread layoffs and hiring difficulties. While competitive data science does not cover the entire span of enterprise-level processes related to data processing and MLOPs, the knowledge and skills acquired on Kaggle are a significant complement to real-world experience. Kaggle serves as an integrated environment for acquiring, documenting, validating, and showcasing competencies that help candidates stand out from the crowd and avoid becoming obsolete in front of automated machine learning (AutoML) or other off-the-shelf solutions, such as recent tabular AI solutions.The Creation of a Verifiable PortfolioEmployers often view a robust portfolio of projects as a great demonstration of technical knowledge and hands-on experience compared to academic credentials alone. Kaggle facilitates the building up of this critical professional asset.First of all, Notebooks are recognized as the most important tool (after rankings) for demonstrating a candidate's abilities, providing tangible evidence of their capacity for clean coding and effective communication. Even if top ranks are not achieved, high-quality Notebooks focused on Exploratory Data Analysis (EDA), tutorials on model architectures, or implementations of cutting-edge research prove crucial abilities, such as extracting visual and non-visual insights from data. Notebooks showcase not justwhat a candidate has done, but also how they approach problems and communicate insights and conclusions, which is critical for working with management, clients, and experts from diverse backgrounds in a business-oriented company environment.On the other hand, Kaggle Datasets provide an excellent means for demonstrating ability with data to be used with Machine Learning (ML) algorithms. By curating, cleaning, and documenting data, professionals can publish and maintain a dataset on Kaggle, thereby demonstrating their understanding of the data's value and potential. The presence of a description, tags, a license, sources, and a frequency of updates are pieces of information used to calculate a usability index, which helps others understand how to use the data. This shows an ability to manage and document data over time. Recently, this opportunity has also been extended to models, allowing the showcase of the necessary competencies in maintenance, fine-tuning, and evaluation of both small and large language models.This Week’s Sponsor: Progress TelerikWebinar: How to Build Faster with AI AgentsLearn how full-stack developers boost productivity by up to 50% using AI agents to automate layout, styling, and component generation with RAG and LLM pipelines.See how orchestration and spec-driven workflows keep quality and consistency in check. Save your seat.Accelerated Skill Acquisition and MarketabilityKaggle participation fosters self-development, exposing data scientists to diverse data types and problems, demanding rapid iteration on model hypotheses, and requiring extensive feature engineering, experience akin to "competition heat." This challenging environment sharpens skills necessary for finding quick and effective solutions to data problems.For job seekers, this translates directly into marketability. Recruiters and human resource departments often monitor Kaggle profiles and rankings when searching for candidates with specific or rare competencies, such as those demonstrated in NLP or computer vision competitions. Consistently good performance in multiple competitions signals a genuine competency and provides verifiable credentials that differentiate an applicant from the crowd.Furthermore, teaming up in competitions teaches individuals to work collaboratively toward a common goal within a limited time frame, and teamwork is a highly valued quality in data science teams. Participating in Kaggle competitions also enhances networking opportunities, facilitating connections that may result in job referrals and opportunities.The Kaggle Book, 2nd Edition isn’t just about winning competitions. For data scientists and ML engineers, it offers a practical way to deepen modeling intuition, experiment with real world datasets, and refine end to end problem solving through notebooks and iterative workflows.As data science roles increasingly demand production awareness, rapid experimentation, and clear communication of results, this book helps you build skills that translate directly into stronger models and better technical decisions. With 30% off the eBook and 20% off the print edition, it’s a timely opportunity to add a structured, hands on reference to your learning stack.Add to CartTranslating Experience into Interview Gold via the STAR ApproachThe experience gained on Kaggle is invaluable during the job interview process. Candidates should leverage their competition efforts to demonstrate problem-solving capabilities using the STAR (Situation, Task, Action, Result) approach. This approach requires structuring competition narratives to emphasize past behavior rather than simply reciting technical capabilities.For example, when detailing a challenging competition:Situation: The candidate must provide a clear context for the problem encountered, detailing the environment and why the situation required attention or action.Task: Clearly explain the objective taken on, such as cleaning messy data, doing explorative analysis (EDA), or continuously improving a benchmark model.Action: Describe the specific steps executed. This can involve explaining the methodologies or media (such as notebooks) used to implement the solution.Result: Articulate the achievement, whether it was improving business value, beating a reference benchmark, or learning from the challenges faced.By utilizing the STAR framework with Kaggle examples, professionals can craft compelling narratives that effectively articulate their problem-solving capabilities and capacity for incremental improvements, thus granting them an edge over other applicants in a very competitive hiring landscape.Wrapping up how Kaggle can give a boost to your careerThe ability to build a robust portfolio, rapidly acquire new skills, and articulate experiences with clarity and confidence are timeless assets in any competitive field. Kaggle provides a unique and effective arena for developing these very competencies. The platform's emphasis on tangible results and peer-reviewed work ensures that the skills showcased are not merely theoretical but demonstrably real. For professionals committed to lifelong learning and staying ahead of the curve, engaging with Kaggle is a direct investment in their career longevity and relevance. By translating this experience into compelling narratives, as outlined through the STAR approach, candidates can effectively communicate their value, demonstrating that they are not just spectators in the data science field but proactive actors in its evolving future.Reading along? Don’t forget to download the free Kaggle Book Cheatsheet a clear reference point for the libraries covered throughout the book.See you next time!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Luca Massaron on turning Kaggle experience into portfolios, skills, and interview wins.Packt and Go1 Invite You to Shape a New Study on Developer LearningAs AI generates more learning content, it is becoming harder to see where expert input really makes a difference. Packt has recently partnered with Go1 to create a short study looking at how developersactually learntoday, and when structured courses still matter alongside AI tools.If you work with learning or rely on it to build skills, your perspective would be useful. The survey takes under5minutestocomplete,and the results will be sharedin a study published in March.Take the 5-Minute SurveySubscribe|Submit a tip|Advertise with Us🧩Welcome to BIPro #123 and happy new year. We’re back with fresh insights, thoughtful perspectives, and practical ideas to help you navigate the year ahead. We hope you’re off to a strong start.For our first issue of the year, we’re joined by Luca Massaron, co author of The Kaggle Book, 2nd Edition and a long standing voice in applied data science. Luca has spent years working at the intersection of real world machine learning, competitive data science, and professional skill building, and brings a grounded view of what actually helps practitioners stay relevant.In this edition, Luca explores why Kaggle has evolved far beyond competitions into a verifiable professional asset. From building portfolios that stand out to recruiters, to accelerating skill development under pressure, to translating competition experience into compelling interview narratives using the STAR framework, this piece is especially timely for anyone navigating a tighter job market or adapting to the rise of AutoML. To help you follow along, our authors have created a free reference cheatsheet that maps all the libraries covered in the book, giving you a clear learning roadmap and an easy point of reference as you work through the material. Free download: Grab the Kaggle Book Cheatsheet.💡 Workshop Spotlight: Machine Learning and Generative AI System Design WorkshopJoin Sairam Sundaresan, AI Engineering Leader, for a hands-on system design workshop on February 28, focused on building machine learning and generative AI systems that actually scale. In this live session, you’ll move beyond model demos and learn how experienced architects design end-to-end AI systems by balancing cost, latency, quality, and risk. Through guided exercises and design sprints, you’ll practice making real architectural trade-offs and defining success metrics that go beyond accuracy. Whether you’re an ML engineer, architect, or product leader, this workshop equips you with reusable frameworks to design AI systems that hold up in production and evolve with changing models and regulations.Register Now and Save With the Early Bird OfferUse FLASH35 for early access savings and reserve your seat.Cheers,Merlyn ShelleyGrowth Lead, PacktThe Essential Asset: Leveraging Kaggle Experience in a Competitive Professional LandscapeEngaging with the Kaggle platform offers a clear advantage for data science professionals seeking to emerge in complex and challenging job market situations, such as the recent one marked by widespread layoffs and hiring difficulties. While competitive data science does not cover the entire span of enterprise-level processes related to data processing and MLOPs, the knowledge and skills acquired on Kaggle are a significant complement to real-world experience. Kaggle serves as an integrated environment for acquiring, documenting, validating, and showcasing competencies that help candidates stand out from the crowd and avoid becoming obsolete in front of automated machine learning (AutoML) or other off-the-shelf solutions, such as recent tabular AI solutions.The Creation of a Verifiable PortfolioEmployers often view a robust portfolio of projects as a great demonstration of technical knowledge and hands-on experience compared to academic credentials alone. Kaggle facilitates the building up of this critical professional asset.First of all, Notebooks are recognized as the most important tool (after rankings) for demonstrating a candidate's abilities, providing tangible evidence of their capacity for clean coding and effective communication. Even if top ranks are not achieved, high-quality Notebooks focused on Exploratory Data Analysis (EDA), tutorials on model architectures, or implementations of cutting-edge research prove crucial abilities, such as extracting visual and non-visual insights from data. Notebooks showcase not justwhat a candidate has done, but also how they approach problems and communicate insights and conclusions, which is critical for working with management, clients, and experts from diverse backgrounds in a business-oriented company environment.On the other hand, Kaggle Datasets provide an excellent means for demonstrating ability with data to be used with Machine Learning (ML) algorithms. By curating, cleaning, and documenting data, professionals can publish and maintain a dataset on Kaggle, thereby demonstrating their understanding of the data's value and potential. The presence of a description, tags, a license, sources, and a frequency of updates are pieces of information used to calculate a usability index, which helps others understand how to use the data. This shows an ability to manage and document data over time. Recently, this opportunity has also been extended to models, allowing the showcase of the necessary competencies in maintenance, fine-tuning, and evaluation of both small and large language models.Accelerated Skill Acquisition and MarketabilityKaggle participation fosters self-development, exposing data scientists to diverse data types and problems, demanding rapid iteration on model hypotheses, and requiring extensive feature engineering, experience akin to "competition heat." This challenging environment sharpens skills necessary for finding quick and effective solutions to data problems.For job seekers, this translates directly into marketability. Recruiters and human resource departments often monitor Kaggle profiles and rankings when searching for candidates with specific or rare competencies, such as those demonstrated in NLP or computer vision competitions. Consistently good performance in multiple competitions signals a genuine competency and provides verifiable credentials that differentiate an applicant from the crowd.Furthermore, teaming up in competitions teaches individuals to work collaboratively toward a common goal within a limited time frame, and teamwork is a highly valued quality in data science teams. Participating in Kaggle competitions also enhances networking opportunities, facilitating connections that may result in job referrals and opportunities.The Kaggle Book, 2nd Edition isn’t just for competitive data scientists. For BI professionals, it offers a practical way to strengthen analytical depth, data intuition, and storytelling through real datasets and notebooks.As BI roles increasingly overlap with ML, experimentation, and AI driven insights, this book helps you build skills that translate directly into stronger analysis and clearer decision making. With 30% off the eBook and 20% off the print edition, it’s a timely way to add a structured, hands on reference to your BI toolkit.Add to CartTranslating Experience into Interview Gold via the STAR ApproachThe experience gained on Kaggle is invaluable during the job interview process. Candidates should leverage their competition efforts to demonstrate problem-solving capabilities using the STAR (Situation, Task, Action, Result) approach. This approach requires structuring competition narratives to emphasize past behavior rather than simply reciting technical capabilities.For example, when detailing a challenging competition:Situation: The candidate must provide a clear context for the problem encountered, detailing the environment and why the situation required attention or action.Task: Clearly explain the objective taken on, such as cleaning messy data, doing explorative analysis (EDA), or continuously improving a benchmark model.Action: Describe the specific steps executed. This can involve explaining the methodologies or media (such as notebooks) used to implement the solution.Result: Articulate the achievement, whether it was improving business value, beating a reference benchmark, or learning from the challenges faced.By utilizing the STAR framework with Kaggle examples, professionals can craft compelling narratives that effectively articulate their problem-solving capabilities and capacity for incremental improvements, thus granting them an edge over other applicants in a very competitive hiring landscape.Wrapping up how Kaggle can give a boost to your careerThe ability to build a robust portfolio, rapidly acquire new skills, and articulate experiences with clarity and confidence are timeless assets in any competitive field. Kaggle provides a unique and effective arena for developing these very competencies. The platform's emphasis on tangible results and peer-reviewed work ensures that the skills showcased are not merely theoretical but demonstrably real. For professionals committed to lifelong learning and staying ahead of the curve, engaging with Kaggle is a direct investment in their career longevity and relevance. By translating this experience into compelling narratives, as outlined through the STAR approach, candidates can effectively communicate their value, demonstrating that they are not just spectators in the data science field but proactive actors in its evolving future.Reading along? Don’t forget to download the free Kaggle Book Cheatsheet a clear reference point for the libraries covered throughout the book.See you next time!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}