Dart Cookbook. Over 110 incredibly effective, useful, and hands-on recipes to design Dart web client and server applications

Ivo Balbaert

Dart Essentials. Design and build full-featured web and CLI apps using the powerful Dart language and its libraries and tools

Martin Sikora, Krunal Jhaveri

Dart: Scalable Application Development. Provides a solid foundation of libraries and tools

David Mitchell, Sergey Akopkokhyants, Ivo Balbaert

Designed to create next generation apps, Google’s Dart offers a much more robust framework and also supersedes JavaScript in several aspects. Familiar yet innovative, compact yet scalable, it blows away the accumulated JavaScript legacy limitations. Dart was designed for great tool-ability and developer productivity, allowing you to create better application faster than before. Google chose it for their billion dollar advertising business and you have its power for your projects too.The first module will introduce you the Dart language starting from its conception to its current form, and where it headed is through engaging substantial practical projects. You will be taken through building typical applications and exploring the exciting new technologies of HTML5.The second module will show you how to add internalization support to your web applications and how i18n and i10n access can be embedded into your code to design applications that can be localized easily. You will be shown how to organize client-to-server communication and how different HTML5 features can be used in Dart. Finally, this module will show you how you can store data locally, break the storage limit, and prevent security issues in your web application.The third module is a pragmatic guide that will increase your expertise in writing all kinds of applications, including web apps, scripts, and server-side apps. It provides rich insights on how to extend your Dart programming skills. Altogether, this course provides you the power to create powerful applications with Dart, without worrying about your knowledge leading to you having to make compromises to the end product!This Learning Path has been curated from three Packt products:Dart By Example By Davy MitchellMastering Dart By Sergey AkopkokhyantsDart Cookbook By Ivo Balbaert

Data Analysis Foundations with Python. Master Data Analysis with Python: From Basics to Advanced Techniques

Cuantum Technologies LLC

Embark on a comprehensive journey through data analysis with Python. Begin with an introduction to data analysis and Python, setting a strong foundation before delving into Python programming basics. Learn to set up your data analysis environment, ensuring you have the necessary tools and libraries at your fingertips. As you progress, gain proficiency in NumPy for numerical operations and Pandas for data manipulation, mastering the skills to handle and transform data efficiently.Proceed to data visualization with Matplotlib and Seaborn, where you'll create insightful visualizations to uncover patterns and trends. Understand the core principles of exploratory data analysis (EDA) and data preprocessing, preparing your data for robust analysis. Explore probability theory and hypothesis testing to make data-driven conclusions and get introduced to the fundamentals of machine learning. Delve into supervised and unsupervised learning techniques, laying the groundwork for predictive modeling.To solidify your knowledge, engage with two practical case studies: sales data analysis and social media sentiment analysis. These real-world applications will demonstrate best practices and provide valuable tips for your data analysis projects.

Data Analysis with Python. A Modern Approach

David Taieb

Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects.Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence.

Data Analysis with R. A comprehensive guide to manipulating, analyzing, and visualizing data in R - Second Edition

Tony Fischetti

Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples.Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility.This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst.

Data Democratization with Domo. Bring together every component of your business to make better data-driven decisions using Domo

Jeff Burtenshaw

Domo is a power-packed business intelligence (BI) platform that empowers organizations to track, analyze, and activate data in record time at cloud scale and performance.Data Democratization with Domo begins with an overview of the Domo ecosystem. You’ll learn how to get data into the cloud with Domo data connectors and Workbench; profile datasets; use Magic ETL to transform data; work with in-memory data sculpting tools (Data Views and Beast Modes); create, edit, and link card visualizations; and create card drill paths using Domo Analyzer. Next, you’ll discover options to distribute content with real-time updates using Domo Embed and digital wallboards. As you advance, you’ll understand how to use alerts and webhooks to drive automated actions. You’ll also build and deploy a custom app to the Domo Appstore and find out how to code Python apps, use Jupyter Notebooks, and insert R custom models. Furthermore, you’ll learn how to use Auto ML to automatically evaluate dozens of models for the best fit using SageMaker and produce a predictive model as well as use Python and the Domo Command Line Interface tool to extend Domo. Finally, you’ll learn how to govern and secure the entire Domo platform.By the end of this book, you’ll have gained the skills you need to become a successful Domo master.

Data Engineering with AWS Cookbook. A recipe-based approach to help you tackle data engineering problems with AWS services

Trâm Ngoc Pham, Gonzalo Herreros González, Viquar Khan, Huda Nofal

Performing data engineering with Amazon Web Services (AWS) combines AWS's scalable infrastructure with robust data processing tools, enabling efficient data pipelines and analytics workflows. This comprehensive guide to AWS data engineering will teach you all you need to know about data lake management, pipeline orchestration, and serving layer construction.Through clear explanations and hands-on exercises, you’ll master essential AWS services such as Glue, EMR, Redshift, QuickSight, and Athena. Additionally, you’ll explore various data platform topics such as data governance, data quality, DevOps, CI/CD, planning and performing data migration, and creating Infrastructure as Code. As you progress, you will gain insights into how to enrich your platform and use various AWS cloud services such as AWS EventBridge, AWS DataZone, and AWS SCT and DMS to solve data platform challenges.Each recipe in this book is tailored to a daily challenge that a data engineer team faces while building a cloud platform. By the end of this book, you will be well-versed in AWS data engineering and have gained proficiency in key AWS services and data processing techniques. You will develop the necessary skills to tackle large-scale data challenges with confidence.