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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}}

Mobile development blogs, tutorials and resources inside!Latest Mobile Dev Insights: iOS, Android, Cross-PlatformAdvertise with Us|Sign Up to the NewsletterMobilePro #203: A sneak peek into the past and the futureWebinar: How to Build Faster with AI AgentsLearn how full‑stack developers boost productivity by 50% with AI agents that automate layout, styling, and component generation through RAG and LLM pipelines. See how orchestration and spec‑driven workflows keep you in control of quality and consistency.Save your seat!Hi ,Welcome to the first MobilePro issue of the year. We missed being in touch with you, our readers, but worry not, we’re back for good.The world of mobile development was constantly on the move in 2025, but the real story was the direction the updates were pointed in. Everyday development focussed on adaptability to a scalable structure, being AI-assisted, and maintenance of clean codebases, and the teams that adapted early are already moving differently.Last year we sent out 40 issues to almost 100,000 subscribers. We featured news, tutorials and tips and advice. This year, we want to continue carry on serving you, our reader. And sure, just as the mobile dev landscape keeps changing, we’ll be updating with the times as well.To start with, this edition is a reset and a catch-up in one. We’re bringing you a chance to look back at pieces from past issues that defined the trajectory we were on in 2025. Make note of the trends that we could track only in hindsight before embarking on the news that has taken place in 2026.Let’s get started!The shifts that defined 2025AI became default, skepticism defined mobile workflows: Towards the end of July 2025, the 2025 Stack Overflow Developer Survey showed 84 % of developers—including many in mobile workflows—used or planned to use AI tools, yet only 33 % trusted their outputs. This marked a defining 2025 trend: AI presence is now assumed, but judgment and domain expertise matter more than ever.Swift’s Android support reshapes cross-platform mobile development: In June 2025, the Swift open-source project formalized official Android support by creating an Android Working Group and preview SDK, which enabled mobile developers to build Android apps in Swift without relying on third-party hacks. This signaled a major shift from platform-exclusive languages toward true cross-platform toolchains. Language portability and unified codebases became strategic priorities rather than niche experiments.Apple opened core AI models to third-party developers: In May 2025, Apple shifted its long-held closed ecosystem approach by opening access to its on-device Apple Intelligence foundation models for third-party developers. This enabled apps to integrate native AI features like summarization, generation, and personalized interactions directly within the app layer. Apple’s move signified that AI was becoming a fundamental building block for app innovation going ahead.Native mobile languages, Kotlin, Swift, and Ruby, lose ground: In April 2025, the TIOBE Programming Community index that runs every month showed Kotlin, Swift and Ruby in a swirl of decline. That has largely been the pattern across the whole of 2025. This trend suggests mobile dev teams are increasingly prioritizing unified, cross-platform toolchains (like Flutter and React Native) over platform-exclusive languages.Most popular articles from our past newslettersMission Possible—The Model Context Protocol (Originally sent: October 29, 2025) MCP is the next major shift in how software connects, similar to how REST transformed web development. Instead of treating AI as just a chatbot layer, MCP is positioned as an open standard for giving AI models structured access to real tools and actions, turning natural language into reliable, executable workflows across apps like GitHub, Blender, Playwright, and Google Maps.This article by Christoffer Noring, Modern AI Integration Expert, was derived from his Learn Model Context Protocol with Python book. In 2025, this was especially timely as prompts became the new UI and teams started building more agent-like experiences. MCP addressed the biggest blocker in that transition: integration chaos. By standardizing how apps expose capabilities to AI, MCP promised less glue code, faster interoperability, and a foundation for the agentic future, where assistants not only answer questions but also operate software.Swift Made Simple—Functional Programming Demystified (Originally sent: August 20, 2025)This article by Jon Hoffman, a Swift programming expert and Mastering Swift 6's author, made functional programming in Swift feel practical instead of academic. By focusing on four core ideas including immutability, pure functions, first-class functions, and higher-order functions, iOS developers were provided clear habits to write code that’s easier to reason about, reuse, and test.In 2025, this was especially relevant as Swift apps increasingly dealt with concurrency and complex state, where bugs often come from hidden mutations and side effects. By promoting predictable behavior and safer data flow, the article positioned FP as a reliable way to build cleaner, more maintainable Swift codebases that scale with real-world app complexity.Mastering Android Architecture with MVVM and the Repository Pattern (Originally sent: November 26, 2025)This article was drawn from the third edition of How to Build Android Applications with Kotlin written by four experts, Alex Forrester, Eran Boudjnah, Alexandru Dumbravan, and Jomar Tigcal. It broke down why Android apps need stronger structure as they scale, especially when activities and fragments start absorbing too much user interface (UI), business logic, and data-handling responsibility. It introduced MVVM as a clean way to separate concerns across the View, ViewModel, and Model layers, and showed how Jetpack ViewModel plus Kotlin Flow enables reactive, lifecycle-aware UI updates with less manual wiring.In 2025, as Android development increasingly relied on Jetpack libraries and coroutine-based patterns, this approach reflected what production-grade Android teams needed most: scalable structure, predictable state management, and cleaner boundaries between UI and data.This week’s news cornerReact Native and Expo dethroned by Snap’s Valdi and ByteDance’s Lynx: In a surprise shake-up, Snap’s Valdi and ByteDance’s Lynx climbed ahead of React Native and Expo. However, one thing to note is that they still follow web-style development (TS/JSX/CSS-like patterns). React Native and Expo remain strong and are rapidly improving stability, performance, and release workflows. We see how mobile dev is shifting toward faster “web-like” DX, better performance models, and more competition/innovation in tooling and UI stacks.OpenClaw signals the rise of Local, self-improving AI assistants: OpenClaw is a rapidly growing open-source, local-first AI assistant that lives inside chat apps and can execute real tasks via filesystem/shell access, skills, and integrations. It matters because it signals a shift from building standalone apps to building agent plugins/automations, raising new expectations for self-extending software. It also makes security, permissions, and orchestration skills critical as agents become powerful “personal OS” layers.Android Studio Otter 3 Feature Drop: Android Studio’s Otter 3 Feature Drop is now live and stable. This release delivers major upgrades to AI-powered development, with refinements that genuinely improve day-to-day workflows. Devs can cherish this as this means stronger performance on large projects, fewer random IDE slowdowns, more stable builds, a sharper debugging experience, and better compatibility with the latest Android Gradle Plugin (AGP). You can learn more about this new release and its benefits in this insightful article by Shubham Singh.Android updates are changing, and developers will feel it in 2026: Android is shifting its update model in 2026. It will lean toward a faster, more predictable, and modular rollouts that reduces fragmentation and eases long-term version support. This also brings tighter platform-level security enforcement, meaning older apps may need quicker fixes and cleaner permission handling, especially in enterprise fleets. With deeper on-device AI becoming standard, developers will need stronger QA pipelines, smarter fallbacks, and more modern app architecture.Announcing the Windows Workgroup: Swift has announced the creation of Windows Workgroup. This new workgroup gives Swift’s Windows support real momentum, improving tooling, core libraries (like Foundation and Dispatch), and best practices for Windows API interoperability. With Arc Browser gone as a flagship example, this effort helps rebuild confidence and sets the stage for a clearer roadmap, stronger stability, and future standout Swift-built Windows apps.Tell us how you learn in the age of AIAs 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 Survey!What do you want more of in 2026?Click here and tell us!👋 And that’s a wrap on this first edition of 2026. We hope you liked this short recap of everything that happened in the last year. In the coming weeks, we hope to keep you at the forefront of pathbreaking news and any trends carving new paths. See you around!Cheers,Runcil Rebello,Editor-in-Chief, MobilePro*{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}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @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}}