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The purpose of this group is to bring R and other open source statistical software users in San Diego together for presentations and discussions on topics of mutual interest. If you have any questions please reach out to Derren Barken or Alain Domissy.

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O **NerdZão** é um grupo de estudos gratuito com sede em São Paulo. O foco principal do grupo é promover o networking e disseminar o conhecimento sobre softwares, plataformas, tecnologias e linguagens de programação de forma divertida, quebrando o paradigma de complexidade no aprendizado.**Sobre os

Old group of Build for India initiative. This group is not active now.

A Techie Tech Blog on Master Data Management And Every Buzz Surrounding It

Future/promises refer to constructs used to synchronize program execution. Learning how it works under the hood by implementing it is a great fundamental skill to know. This article is …

I blog about data science and tech.

MapLight offers data series about federal campaign finance and bills in Congress, for research and media organizations.

Machine Learning, Data Science and Deep Learning with Python covers machine learning, Tensorflow, artificial intelligence, and neural networks—all skills that are in demand from the biggest tech employers. Filled with examples using accessible Python code you can experiment with, this complete hands-on data science tutorial teaches you techniques used by real data scientists and prepares you for a move into this hot career path. Distributed by Manning Publications This course was created independently by big data expert Frank Kane and is distributed by Manning through our exclusive liveVideo platform.

See it. Do it. Learn it! Keras in Motion introduces you to the amazing Keras deep learning library through high-quality video-based lessons and built-in exercises, so you can put what you learn into practice. Keras in Motion teaches you to build neural-network models for real-world data problems using Python and Keras. In over two hours of hands-on, practical video lessons, you'll apply Keras to common machine learning scenarios, ranging from regression and classification to implementing Autoencoders and applying transfer learning. In each crystal-clear video module, you'll put your new knowledge into practice, as you teach your network to recognize text and even create an algorithm for a self-driving car!

One billion iPhone and iPad users are waiting for the next amazing app. It's time for you to build it! If you've programmed in another language like Python or JavaScript, it's a breeze to pick up Apple's powerful Swift language. This interactive liveVideo course gives you a crash course in Swift and then guides you step-by-step through building your own iPhone and iPad apps.

Think Like a Data Scientist presents a step-by-step approach to data science, combining analytic, programming, and business perspectives into easy-to-digest techniques and thought processes for solving real world data-centric problems.

Now updated for Swift 5! Swift is more than just a fun language to build iOS applications with. It features a host of powerful tools that, if effectively used, can help you create even better apps with clean, crystal-clear code and awesome features. Swift in Depth is designed to help you unlock these tools and quirks and get developing next-gen apps, web services, and more!

These easy to learn and apply software engineering techniques will radically improve collaboration, scaling, and deployment in your data science projects. In Software Engineering for Data Scientists you’ll learn to improve performance and efficiency by: Using source control Handling exceptions and errors in your code Improving the design of your tools and applications Scaling code to handle large data efficiently Testing model and data processing code before deployment Scheduling a model to run automatically Packaging Python code into reusable libraries Generating automated reports for monitoring a model in production Software Engineering for Data Scientists presents important software engineering principles that will radically improve the performance and efficiency of data science projects. Author and Meta data scientist Andrew Treadway has spent over a decade guiding models and pipelines to production. This practical handbook is full of his sage advice that will change the way you structure your code, monitor model performance, and work effectively with the software engineering teams.

This is the story of two competitors. On one side is an expert programmer and on the other are the AI powerhouse coding assistants, GitHub Copilot and OpenAI ChatGPT.

R is the most powerful tool you can use for statistical analysis. This definitive guide smooths R’s steep learning curve with practical solutions and real-world applications for commercial environments. In R in Action, Third Edition you will learn how to: Set up and install R and RStudio Clean, manage, and analyze data with R Use the ggplot2 package for graphs and visualizations Solve data management problems using R functions Fit and interpret regression models Test hypotheses and estimate confidence Simplify complex multivariate data with principal components and exploratory factor analysis Make predictions using time series forecasting Create dynamic reports and stunning visualizations Techniques for debugging programs and creating packages R in Action, Third Edition makes learning R quick and easy. That’s why thousands of data scientists have chosen this guide to help them master the powerful language. Far from being a dry academic tome, every example you’ll encounter in this book is relevant to scientific and business developers, and helps you solve common data challenges. R expert Rob Kabacoff takes you on a crash course in statistics, from dealing with messy and incomplete data to creating stunning visualizations. This revised and expanded third edition contains fresh coverage of the new tidyverse approach to data analysis and R’s state-of-the-art graphing capabilities with the ggplot2 package.

NEWER EDITION AVAILABLE IN MEAP Practical Data Science with R, Second Edition is now available in the Manning Early Access Program. An eBook of this older edition is included at no additional cost when you buy the revised edition! You may still purchase Practical Data Science with R (First Edition) using the Buy options on this page. Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.

Master this versatile tool and take the next steps in your data science career! Learn how easy Pandas makes it to efficiently sort, analyze and munge almost any type of data.

Guide machine learning projects from design to production with the techniques in this one-of-a-kind project management guide. No ML skills required In Managing Machine Learning Projects you’ll learn essential machine learning project management techniques, including: Understanding an ML project’s requirements Setting up the infrastructure for the project and resourcing a team Working with clients and other stakeholders Dealing with data resources and bringing them into the project for use Handling the lifecycle of models in the project Managing the application of ML algorithms Evaluating the performance of algorithms and models Making decisions about which models to adopt for delivery Taking models through development and testing Integrating models with production systems to create effective applications Steps and behaviors for managing the ethical implications of ML technology Managing Machine Learning Projects is an end-to-end guide for delivering machine learning applications on time and under budget. It lays out tools, approaches, and processes designed to handle the unique challenges of machine learning project management. You’ll follow an in-depth case study through a series of sprints and see how to put each technique into practice. The book’s strong consideration to data privacy, and community impact ensure your projects are ethical, compliant with global legislation, and avoid being exposed to failure from bias and other issues.

Master core data analysis skills using Julia. Interesting hands-on projects guide you through time series data, predictive models, popularity ranking, and more. In Julia for Data Analysis you will learn how to: Read and write data in various formats Work with tabular data, including subsetting, grouping, and transforming Visualize your data Build predictive models Create data processing pipelines Create web services sharing results of data analysis Write readable and efficient Julia programs Julia was designed for the unique needs of data scientists: it's expressive and easy-to-use whilst also delivering super-fast code execution. Julia for Data Analysis shows you how to take full advantage of this amazing language to read, write, transform, analyze, and visualize data—everything you need for an effective data pipeline. It’s written by Bogumił Kamiński, one of the top contributors to Julia, #1 Julia answerer on StackOverflow, and a lead developer of Julia’s core data package DataFrames.jl. Its engaging hands-on projects get you into the action quickly. Plus, you’ll even be able to turn your new Julia skills to general purpose programming!

iOS Development with Swift is a hands-on guide to creating apps for iPhone and iPad using the Swift language. Inside, you'll be guided through every step of the process for building an app, from first idea to App Store. This book fully covers Swift 4, Xcode 9, and iOS 11. This book is perfect alongside Craig's iOS Development with Swift in Motion video course, each offering unique exercises, examples, and more to get you learning!

Introducing Data Science teaches you how to accomplish the fundamental tasks that occupy data scientists. Using the Python language and common Python libraries, you'll experience firsthand the challenges of dealing with data at scale and gain a solid foundation in data science.

A field guide for the unique challenges of data science leadership, filled with transformative insights, personal experiences, and industry examples. In How To Lead in Data Science you will learn: Best practices for leading projects while balancing complex trade-offs Specifying, prioritizing, and planning projects from vague requirements Navigating structural challenges in your organization Working through project failures with positivity and tenacity Growing your team with coaching, mentoring, and advising Crafting technology roadmaps and championing successful projects Driving diversity, inclusion, and belonging within teams Architecting a long-term business strategy and data roadmap as an executive Delivering a data-driven culture and structuring productive data science organizations How to Lead in Data Science is full of techniques for leading data science at every seniority level—from heading up a single project to overseeing a whole company's data strategy. Authors Jike Chong and Yue Cathy Chang share hard-won advice that they've developed building data teams for LinkedIn, Acorns, Yiren Digital, large asset-management firms, Fortune 50 companies, and more. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away. Carefully crafted assessments and interview scenarios encourage introspection, reveal personal blind spots, and highlight development areas.

Hello Swift! is a how-to guide to programming iOS Apps with the Swift language, written from a kid's perspective. This approachable, well-illustrated, step-by-step guide takes you from beginning programming concepts all the way through developing complete apps. (Adults will like it too!)

Graphs are the natural way to understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with practical examples and concrete advice on implementation and deployment. In Graph Algorithms for Data Science you will learn: Labeled-property graph modeling Constructing a graph from structured data such as CSV or SQL NLP techniques to construct a graph from unstructured data Cypher query language syntax to manipulate data and extract insights Social network analysis algorithms like PageRank and community detection How to translate graph structure to a ML model input with node embedding models Using graph features in node classification and link prediction workflows Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It’s filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You’ll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. You don’t need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects.

The beating heart of any product or service business is returning clients. Don't let your hard-won customers vanish, taking their money with them. In Fighting Churn with Data you'll learn powerful data-driven techniques to maximize customer retention and minimize actions that cause them to stop engaging or unsubscribe altogether. This hands-on guide is packed with techniques for converting raw data into measurable metrics, testing hypotheses, and presenting findings that are easily understandable to non-technical decision makers.

Some people like to believe that all data is ready to be used immediately. Not so! Data in the wild is hard to track and harder to understand, and the first job of data scientists to identify and prepare data so it can be used. To find your way through the data jungle successfully, you need the right perspective and guidance. (There's no point hacking at overgrowth with a spoon after all!) Identify and prepare your data well, and you'll be well set to create insight from chaos and discover important analytic patterns - to set your business on the right track. Exploring the Data Jungle: Finding, Preparing, and Using Real-World Data is a collection of three hand-picked chapters introducing you to the often-overlooked art of putting unfamiliar data to good use. Brian Godsey, author of Think Like a Data Scientist, has selected these chapters to help you navigate data in the wild, identify and prepare raw data for analysis, modeling, machine learning, or visualization. As you explore the data jungle you'll discover real-world examples in Python, R, and other languages suitable for data science.

There’s never been a better time to get into data science. But where do you start? Data Science is a broad field, incorporating aspects of statistics, machine learning, and data engineering. It's easy to become overwhelmed, or end up learning about a small section of data science or a single methodology. Exploring Data Science is a collection of five hand-picked chapters introducing you to various areas in data science and explaining which methodologies work best for each. John Mount and Nina Zumel, authors of Practical Data Science with R, selected these chapters to give you the big picture of the many data domains. You’ll learn about time series, neural networks, text analytics, and more. As you explore different modeling practices, you’ll see practical examples of how R, Python, and other languages are used in data science. Along the way, you'll experience a sample of Manning books you may want to add to your library.

Design a modern scalable data platform that takes full advantage of the flexibility of the cloud. Explore a six-layer approach that increases flexibility and reduces costs.

Dask is a native parallel analytics tool designed to integrate seamlessly with the libraries you’re already using, including Pandas, NumPy, and Scikit-Learn. With Dask you can crunch and work with huge datasets, using the tools you already have. And Data Science with Python and Dask is your guide to using Dask for your data projects without changing the way you work!