Hi JavaScript London,

We're thrilled to host the first meet-up of 2025!

We are kicking things off with Trainline!! 😃 🚝

We have some fantastic tech talks lined up, there will be plenty of pizzas 🍕, and of course drinks 🍻, plus plenty of networking with fellow tech enthusiasts. Don't miss out on a night of great conversations and new connections!

Address: 120 Holborn, London EC1N 2TD

Registration - The website is in the process of being updated however we wanted to put the event live... We will email everyone who is attending reminding them to sign-up via the website closer to the event!

Talks: Stephen Cooper - The challenges/approaches of reducing the bundle size of a component library.

Simon McManus & Paul Kiddie - Observability of repository health in the web platform at Trainline

Full agenda: 6:00 PM: Doors Open. Grab a pizza slice, sip a beer, and start chatting with other attendees.

6:30 PM: Welcome remarks by Sam and Richard

6:35 PM: First talk

7:05 PM: Toilet break

7:15 PM: Second talk

8:00 PM: Closing remarks and an optional pub visit!

Directions to Trainline:

The closest train stations: Chancery Lane & Farringdon

Sponsor: Trainline:

We are champions of rail, inspired to build a greener, more sustainable future of travel. Trainline enables millions of travellers to find and book the best value tickets across carriers, fares, and journey options through our highly rated mobile app, website, and B2B partner channels.

Now Europe’s number 1 downloaded rail app, with over 125 million monthly visits and £5.3 billion in annual ticket sales, we collaborate with 270+ rail and coach companies in over 40 countries.

We want to create a world where travel is as simple, seamless, and affordable as it should be.

Today, we're a FTSE 250 company driven by our incredible team of over 1,000 Trainliners from 50+ nationalities, based across London, Paris, Barcelona, Milan, Edinburgh, Berlin, Madrid, and Brussels.

Send us a text It's a New Year! Setting your intentions; identifying what's important, and how to prioritize in business. Additionally, harness the superpower of identifying styles.

This week's episode is a replay of a highly rated episode with Lynne Snead, founder and owner of Talent Evolution Systems.  Lynne is a behavioral analyst, consultant, training specialist, speaker, coach. Lynne has a back ground in Educational Psychology, and has specialized in organizational performance for over 20 years. Lynne is one of the original Franklin Covey co-authors, has a best seller, she created Franklin Covey’s signature Project Development process and programs, worked directly with Stephen R Covey Senior and Stephen MR Covey Junior. Lynne started her own company in 2003. 

I've known Lynne for several years and highly recommend reaching out to her for more information. Show Notes 1:42 – Lynne talks about her career 4:44 – What is your definition of Coach?7:38 – Beginning of the year intentions16:24 – Line of sight36:40 – How do you prioritize in business?44:40 – Style is important50:54 – What is the “cheat” to align different styles?  Favorite book: Speed of Trust Favorite book: Atomic Habits

About Vern Harnish  Contact Lynne Snead Connect with the Team Producer Kate Mayne - LinkedIn. Host Al Martin - LinkedIn and Twitter.  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.

Stephen Roach, Senior Fellow at the China Center of Yale Law School, discusses China’s near and longer-term economic prospects with Mark, Cris and Marisa. Steve explains why he has evolved from a bull to a bear on China’s economy, why the critical relationship between China and the U.S. has gone sideways, and how to get the relationship moving in a positive direction. For more on Stephen Roach, click here For more information on Stephen Roach's book, Accidental Conflict: America, China, and the Clash of False Narratives, click here For the full transcript, click here Follow Mark Zandi @MarkZandi, Cris deRitis @MiddleWayEcon, and Marisa DiNatale on LinkedIn for additional insight.

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

Data lakes and warehouses have become increasingly fragile, costly, and difficult to maintain as data gets bigger and moves faster. Data meshes can help your organization decentralize data, giving ownership back to the engineers who produced it. This book provides a concise yet comprehensive overview of data mesh patterns for streaming and real-time data services. Authors Hubert Dulay and Stephen Mooney examine the vast differences between streaming and batch data meshes. Data engineers, architects, data product owners, and those in DevOps and MLOps roles will learn steps for implementing a streaming data mesh, from defining a data domain to building a good data product. Through the course of the book, you'll create a complete self-service data platform and devise a data governance system that enables your mesh to work seamlessly. With this book, you will: Design a streaming data mesh using Kafka Learn how to identify a domain Build your first data product using self-service tools Apply data governance to the data products you create Learn the differences between synchronous and asynchronous data services Implement self-services that support decentralized data

Data Science Projects with Python offers a hands-on, project-based approach to learning data science using real-world data sets and tools. You will explore data using Python libraries like pandas and Matplotlib, build machine learning models with scikit-learn, and apply advanced techniques like XGBoost and SHAP values. This book equips you to confidently extract insights, evaluate models, and deliver results with clarity. What this Book will help me do Learn to load, clean, and preprocess data using Python and pandas. Build and evaluate predictive models, including logistic regression and random forests. Visualize data effectively using Python libraries like Matplotlib. Master advanced techniques like XGBoost and algorithmic fairness. Communicate data-driven insights to aid decision making in practical scenarios. Author(s) Stephen Klosterman is an experienced data scientist with a strong focus on practical applications of machine learning in business. Combining a rich academic background with hands-on industry experience, he excels at explaining complex concepts in an approachable way. As the author of 'Data Science Projects with Python,' his goal is to provide learners with the skills needed for real-world data science challenges. Who is it for? This book is ideal for beginners in data science and machine learning who have some basic programming knowledge in Python. Aspiring data scientists will benefit from its practical, end-to-end examples. Professionals seeking to expand their skillset in predictive modeling and delivering business insights will find this book invaluable. Some foundation in statistics and programming is recommended.

Summary Data governance is a term that encompasses a wide range of responsibilities, both technical and process oriented. One of the more complex aspects is that of access control to the data assets that an organization is responsible for managing. The team at Immuta has built a platform that aims to tackle that problem in a flexible and maintainable fashion so that data teams can easily integrate authorization, data masking, and privacy enhancing technologies into their data infrastructure. In this episode Steve Touw and Stephen Bailey share what they have built at Immuta, how it is implemented, and how it streamlines the workflow for everyone involved in working with sensitive data. If you are starting down the path of implementing a data governance strategy then this episode will provide a great overview of what is involved.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Feature flagging is a simple concept that enables you to ship faster, test in production, and do easy rollbacks without redeploying code. Teams using feature flags release new software with less risk, and release more often. ConfigCat is a feature flag service that lets you easily add flags to your Python code, and 9 other platforms. By adopting ConfigCat you and your manager can track and toggle your feature flags from their visual dashboard without redeploying any code or configuration, including granular targeting rules. You can roll out new features to a subset or your users for beta testing or canary deployments. With their simple API, clear documentation, and pricing that is independent of your team size you can get your first feature flags added in minutes without breaking the bank. Go to dataengineeringpodcast.com/configcat today to get 35% off any paid plan with code DATAENGINEERING or try out their free forever plan. You invest so much in your data infrastructure – you simply can’t afford to settle for unreliable data. Fortunately, there’s hope: in the same way that New Relic, DataDog, and other Application Performance Management solutions ensure reliable software and keep application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo’s end-to-end Data Observability Platform monitors and alerts for data issues across your data warehouses, data lakes, ETL, and business intelligence. The platform uses machine learning to infer and learn your data, proactively identify data issues, assess its impact through lineage, and notify those who need to know before it impacts the business. By empowering data teams with end-to-end data reliability, Monte Carlo helps organizations save time, increase revenue, and restore trust in their data. Visit dataengineeringpodcast.com/montecarlo today to request a demo and see how Monte Carlo delivers data observability across your data inf

Data Science Projects with Python introduces you to data science and machine learning using Python through practical examples. In this book, you'll learn to analyze, visualize, and model data, applying techniques like logistic regression and random forests. With a case-study method, you'll build confidence implementing insights in real-world scenarios. What this Book will help me do Set up a data science environment with necessary Python libraries such as pandas and scikit-learn. Effectively visualize data insights through Matplotlib and summary statistics. Apply machine learning models including logistic regression and random forests to solve data problems. Identify optimal models through evaluation metrics like k-fold cross-validation. Develop confidence in data preparation and modeling techniques for real-world data challenges. Author(s) Stephen Klosterman is a seasoned data scientist with a keen interest in practical applications of machine learning. He combines a strong academic foundation with real-world experience to craft relatable content. Stephen excels in breaking down complex topics into approachable lessons, helping learners grow their data science expertise step by step. Who is it for? This book is ideal for data analysts, scientists, and business professionals looking to enhance their skills in Python and data science. If you have some experience in Python and a foundational understanding of algebra and statistics, you'll find this book approachable. It offers an excellent gateway to mastering advanced data analysis techniques. Whether you're seeking to explore machine learning or apply data insights, this book supports your growth.

Build powerful data analytics applications with this business intelligence tool and overcome all your business challenges Key Features Master time-saving techniques and make your QlikView development more efficient Perform geographical analysis and sentiment analysis in your QlikView applications Explore advanced QlikView techniques, tips, and tricks to deliver complex business requirements Book Description QlikView is one of the most flexible and powerful business intelligence platforms around, and if you want to transform data into insights, it is one of the best options you have at hand. Use this Learning Path, to explore the many features of QlikView to realize the potential of your data and present it as impactful and engaging visualizations. Each chapter in this Learning Path starts with an understanding of a business requirement and its associated data model and then helps you create insightful analysis and data visualizations around it. You will look at problems that you might encounter while visualizing complex data insights using QlikView, and learn how to troubleshoot these and other not-so-common errors. This Learning Path contains real-world examples from a variety of business domains, such as sales, finance, marketing, and human resources. With all the knowledge that you gain from this Learning Path, you will have all the experience you need to implement your next QlikView project like a pro. This Learning Path includes content from the following Packt products: QlikView for Developers by Miguel Angel Garcia, Barry Harmsen Mastering QlikView by Stephen Redmond Mastering QlikView Data Visualization by Karl Pover What you will learn Deliver common business requirements using advanced techniques Load data from disparate sources to build associative data models Understand when to apply more advanced data visualization Utilize the built-in aggregation functions for complex calculations Build a data architecture that supports scalable QlikView deployments Troubleshoot common data visualization errors in QlikView Protect your QlikView applications and data Who this book is for This Learning Path is designed for developers who want to go beyond their technical knowledge of QlikView and understand how to create analysis and data visualizations that solve real business needs. To grasp the concepts explained in this Learning Path, you should have a basic understanding of the common QlikView functions and some hands-on experience with the tool. Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.

In this podcast, Stephen Wunker spent time discussing the future of organizations via cost innovations and how some enterprises connect a successful pricing strategy with their data strategy. He sheds light on what some successful companies do to stay competitive and keep innovating their cost strategies to find effective customer connections. He shares some challenges that leaders face in adopting a successful cost innovation strategy. The book "Costovation" and this podcast are relevant for anyone seeking to learn about innovative ways to define their cost strategies. It is especially relevant for data science leaders to understand how they could transform sales by connecting cost and innovation.

Timelines: 0:30 Stephen's journey 6:25 Introducing "costovation". 10:10 Cost management in the age of "freemium" and opensource. 12:35 Key points of "Cost-o-vation". 15:40 Resolving issues between cost and innovation. 18:26 Introducing radical ideas of innovation to companies. 21:40 Gauging innovation. 24:20 Role of data in costovation. 26:15 Why adopt cost-ovation? 31:44 Innovation tips and suggestions. 34:45 Example of a company that is practicing cost-o-vation. 37:15 Tenets of good leadership. 39:50 Scalability of cost-o-vation. 43:17 cost-ovation and the customer. 47:47 Stephen's favorite reads. 49:45 Key takeaways.

Stephen's Book: Costovation: Innovation That Gives Your Customers Exactly What They Want--And Nothing More by Stephen Wunker, Jennifer Luo Law amzn.to/2xYyRFs

Stephen's Recommended Read: The Three-Box Solution: A Strategy for Leading Innovation by Vijay Govindarajan amzn.to/2y2Sex6 Made to Stick: Why Some Ideas Survive and Others Die by Chip Heath, Dan Heath amzn.to/2Ct2SRV The Innovator's Solution: Creating and Sustaining Successful Growth by Clayton M. Christensen, Michael E. Raynor amzn.to/2DZ6jRK

Podcast Link: https://futureofdata.org/stephen-wunker-on-future-of-customer-success-through-cost-innovation-and-data/

Stephen's BIO: Stephen Wunker is the founder and managing director of New Markets Advisors, a Boston-based consultancy focused on innovation and growth strategy.

With a long track record of creating successful ventures, Stephen has consulted multinational firms and start-ups across six continents, developing dozens of new growth platforms for clients over the past decade. He also pioneered both mobile commerce and mobile marketing, and he led the team that created one of the world's first smartphones.

In addition to his entrepreneurial and corporate ventures, he was a long-term colleague of the leading innovation authority Harvard Business School Professor Clayton Christensen in establishing his consulting practice, Innosight. His previous experience includes years with the management consultancy Bain & Company, the Rockefeller Brothers Fund, and the Soros Foundations.

Stephen holds an MBA from Harvard Business School, a Master's in Public Administration from Columbia University, and a BA cum laude from Princeton University. Coauthor of “COSTOVATION: Innovation that Gives Your Customers Exactly What They Want—and Nothing More” (HarperCollins Leadership, Aug. 14), his third book, Stephen has contributed to Harvard Business Review, Forbes, and a range of journals, and has appeared on Bloomberg TV, BBC and other broadcasts. He has lived in the United States, United Kingdom, Netherlands, Japan, Ecuador, and Zambia, and is now based in Boston.

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to come on the show and discuss their journey in creating the data-driven future.

Wanna Join? If you or any you know wants to join in, Register your interest by emailing us @ [email protected]

Want to sponsor? Email us @ [email protected]

Keywords: FutureOfData,

DataAnalytics,

Leadership,

Futurist,

Podcast,

BigData,

Strategy

D3.js in Action is a practical tutorial for creating interactive graphics and data-driven applications using D3.js. You'll start with in-depth explanations of D3's out-of-the-box layouts, along with dozens of practical use cases that align with different types of visualizations. Then, you'll explore practical techniques for content creation, animation, and representing dynamic data—including interactive graphics and data streamed live over the web. The final chapters show you how to use D3's rich interaction model as the foundation for a complete web application. In the end, you'll be ready to integrate D3.js into your web development process and transform any site into a more engaging and sophisticated user experience. About the Technology D3.js is a JavaScript library that allows data to be represented graphically on a web page. Because it uses the broadly supported SVG standard, D3 allows you to create scalable graphs for any modern browser. You start with a structure, dataset, or algorithm and programmatically generate static, interactive, or animated images that responsively scale to any screen. About the Book D3.js in Action introduces you to the most powerful web data visualization library available and shows you how to use it to build interactive graphics and data-driven applications. You'll start with dozens of practical use cases that align with different types of charts, networks, and maps using D3's out-of-the-box layouts. Then, you'll explore practical techniques for content design, animation, and representation of dynamic data—including interactive graphics and live streaming data. What's Inside Interacting with vector graphics Expressive data visualization Creating rich mapping applications Prepping your data Complete data-driven web apps in D3 About the Reader Readers need basic HTML, CSS, and JavaScript skills. No experience with D3 or SVG is required. About the Author Elijah Meeks is a senior data visualization engineer at Netflix. His D3.js portfolio includes work at Stanford University and with well-known companies worldwide. Quotes A mandatory introduction to a very complex and powerful library. - Stephen Wakely, Thomson Reuters Quickly gets you coding amazing visualizations. - Ntino Krampis, PhD, City University of New York A remarkable exploration of the world of dataviz possibilities with D3. - Arun Noronha, Directworks Inc. A must-have book. - Arif Shaikh, Sony Pictures Entertainment One of the most comprehensive books about data visualization I have ever read. - Andrea Mostosi, The Fool s.r.l.

Neo4j in Action is a comprehensive guide to Neo4j, aimed at application developers and software architects. Using hands-on examples, you'll learn to model graph domains naturally with Neo4j graph structures. The book explores the full power of native Java APIs for graph data manipulation and querying. About the Technology Much of the data today is highly connected--from social networks to supply chains to software dependency management--and more connections are continually being uncovered. Neo4j is an ideal graph database tool for highly connected data. It is mature, production-ready, and unique in enabling developers to simply and efficiently model and query connected data. About the Book Neo4j in Action is a comprehensive guide to designing, implementing, and querying graph data using Neo4j. Using hands-on examples, you'll learn to model graph domains naturally with Neo4j graph structures. The book explores the full power of native Java APIs for graph data manipulation and querying. It also covers Cypher, Neo4j's graph query language. Along the way, you'll learn how to integrate Neo4j into your domain-driven app using Spring Data Neo4j, as well as how to use Neo4j in standalone server or embedded modes. What's Inside Graph database patterns How to model data in social networks How to use Neo4j in your Java applications How to configure and set up Neo4j About the Reader Knowledge of Java basics is required. No prior experience with graph data or Neo4j is assumed. About the Authors Aleksa Vukotic is an architect specializing in graph data models. Nicki Watt, Dominic Fox, Tareq Abedrabbo, and Jonas Partner work at OpenCredo, a Neo Technology partner, and have been involved in many projects using Neo4j. Quotes A pragmatic programmatic tour through Neo4j’s APIs and query language. - From the Foreword by Jim Webber and Ian Robinson, Neo Technology Excellent coverage of one of the most successful NoSQL products. - Pouria Amirian, PhD, University of Oxford A great resource for rethinking your data storage using graphs in Neo4j. - Stephen Kitt, ERDF

Like most PL/SQL programmers, you've found Oracle PL/SQL Programming to be a reference book you can't do without. Packed with examples and recommendations, it's a book that has helped everyone, from Oracle Forms developers to database administrators, make the most of PL/SQL. But you can't always carry it with you, and even at your desk there are times when you just need a reminder, a quick answer to a problem you're up against. For times like those, PL/SQL experts Stephen Feuerstein, Bill Pribyl, and Chip Dawes have written a new edition of Oracle PL/SQL Language Pocket Reference. Updated for Oracle9i, this little book is easy to take with you and use anywhere for quick problem solving. The second edition of the Oracle PL/SQL Language Pocket Reference boils down the most vital information from Oracle PL/SQL Programming into a convenient quick reference to PL/SQL basics. This compact book will be indispensable for new and seasoned Oracle database developers alike. It summarizes features available in Oracle9i and provides essential information on PL/SQL block structure, fundamental language elements, control statements, and use of procedures, functions, packages, triggers, Oracle objects, external procedures, and methods of calling Java classes from PL/SQL. O'Reilly's Pocket References have become a favorite among developers and database administrators everywhere. By providing a wealth of important details in a concise, well-organized format, these handy books deliver just what you need to complete the task at hand. When you've reached a sticking point in your work and need to get to a solution quickly, the new edition of Oracle PL/SQL Language Pocket Reference is the book you'll want to have.