The Data Science Workshop. A New, Interactive Approach to Learning Data Science

Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, ...

You already know you want to learn data science, and a smarter way to learn data science is to learn by doing. The Data Science Workshop focuses on building up your practical skills so that you can understand how to develop simple machine learning models in Python or even build an advanced model for detecting potential bank frauds with effective modern data science. You'll learn from real examples that lead to real results.Throughout The Data Science Workshop, you'll take an engaging step-by-step approach to understanding data science. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend training a model using sci-kit learn. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding.Every physical print copy of The Data Science Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your data science book.Fast-paced and direct, The Data Science Workshop is the ideal companion for data science beginners. You'll learn about machine learning algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.

Mastering Python Data Analysis. Become an expert at using Python for advanced statistical analysis of data using real-world examples

Luiz Felipe Martins, Gavin Hackeling, Magnus Vilhelm Persson

Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. Ever imagined how to become an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? Well, look no further, this is the book you want!Through this comprehensive guide, you will explore data and present results and conclusions from statistical analysis in a meaningful way. You’ll be able to quickly and accurately perform the hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making.You’ll start off by learning about the tools available for data analysis in Python and will then explore the statistical models that are used to identify patterns in data. Gradually, you’ll move on to review statistical inference using Python, Pandas, and SciPy. After that, we’ll focus on performing regression using computational tools and you’ll get to understand the problem of identifying clusters in data in an algorithmic way. Finally, we delve into advanced techniques to quantify cause and effect using Bayesian methods and you’ll discover how to use Python’s tools for supervised machine learning.

Kivy - Interactive Applications and Games in Python. Create responsive cross-platform UI/UX applications and games in Python using the open source Kivy library

Roberto Ulloa

Modern Python Cookbook. 133 recipes to develop flawless and expressive programs in Python 3.8 - Second Edition

Steven F. Lott

Python is the preferred choice of developers, engineers, data scientists, and hobbyists everywhere. It is a great language that can power your applications and provide great speed, safety, and scalability. It can be used for simple scripting or sophisticated web applications. By exposing Python as a series of simple recipes, this book gives you insight into specific language features in a particular context. Having a tangible context helps make the language or a given standard library feature easier to understand.This book comes with 133 recipes on the latest version of Python 3.8. The recipes will benefit everyone, from beginners just starting out with Python to experts. You'll not only learn Python programming concepts but also how to build complex applications.The recipes will touch upon all necessary Python concepts related to data structures, object oriented programming, functional programming, and statistical programming. You will get acquainted with the nuances of Python syntax and how to effectively take advantage of it.By the end of this Python book, you will be equipped with knowledge of testing, web services, configuration, and application integration tips and tricks. You will be armed with the knowledge of how to create applications with flexible logging, powerful configuration, command-line options, automated unit tests, and good documentation.

Exploratory Data Analysis with Python Cookbook. Over 50 recipes to analyze, visualize, and extract insights from structured and unstructured data

Ayodele Oluleye

In today's data-centric world, the ability to extract meaningful insights from vast amounts of data has become a valuable skill across industries. Exploratory Data Analysis (EDA) lies at the heart of this process, enabling us to comprehend, visualize, and derive valuable insights from various forms of data.This book is a comprehensive guide to Exploratory Data Analysis using the Python programming language. It provides practical steps needed to effectively explore, analyze, and visualize structured and unstructured data. It offers hands-on guidance and code for concepts such as generating summary statistics, analyzing single and multiple variables, visualizing data, analyzing text data, handling outliers, handling missing values and automating the EDA process. It is suited for data scientists, data analysts, researchers or curious learners looking to gain essential knowledge and practical steps for analyzing vast amounts of data to uncover insights.Python is an open-source general purpose programming language which is used widely for data science and data analysis given its simplicity and versatility. It offers several libraries which can be used to clean, analyze, and visualize data. In this book, we will explore popular Python libraries such as Pandas, Matplotlib, and Seaborn and provide workable code for analyzing data in Python using these libraries.By the end of this book, you will have gained comprehensive knowledge about EDA and mastered the powerful set of EDA techniques and tools required for analyzing both structured and unstructured data to derive valuable insights.

Blender 2.6 Cycles: Materials and Textures Cookbook. With this book you'll be able to explore and master all that the Cycles rendering engine is capable of. From the basics right through to refining, this is a must-read if you're serious about the realism of your materials and textures

Ton Roosendaal, Enrico Valenza

Cycles is Blender's new, powerful rendering engine. Using practical examples, this book will show you how to create a vast array of realistic and stunning materials and texture effects using the Cycles rendering engine.Blender 2.6 Cycles: Materials and Textures Cookbook is a practical journey into the new and exciting Cycles rendering engine for Blender. In this book you will learn how to create a vast array of materials and textures in Cycles, including glass, ice, snow, rock, metal and water. If you want to take your 3D models to the next level, but don't know how, then this cookbook is for you!In this practical cookbook, you will learn how to create stunning materials and textures to really bring your 3D models to life! Diving deep into Cycles you will learn Cycle's node-based material system, how to set-up a 3D scene for rendering, how to create a natural and man-made materials as well as the correct organization and re-use of Cycles materials to save you time and effort.To ensure that your creations look stunning you will learn how illumination works in Cycles, improve the quality of the final render and to avoid the presence of noise and fireflies. Each chapter of Blender 2.6 Cycles: Materials and Textures Cookbook builds on the complexity of the last so that by the end of this book you will know how to create an impressive library of realistic-looking materials and textures.

Fast Data Processing Systems with SMACK Stack. Click here to enter text

Raúl Estrada

SMACK is an open source full stack for big data architecture. It is a combination of Spark, Mesos, Akka, Cassandra, and Kafka. This stack is the newest technique developers have begun to use to tackle critical real-time analytics for big data. This highly practical guide will teach you how to integrate these technologies to create a highly efficient data analysis system for fast data processing.We’ll start off with an introduction to SMACK and show you when to use it. First you’ll get to grips with functional thinking and problem solving using Scala. Next you’ll come to understand the Akka architecture. Then you’ll get to know how to improve the data structure architecture and optimize resources using Apache Spark. Moving forward, you’ll learn how to perform linear scalability in databases with Apache Cassandra. You’ll grasp the high throughput distributed messaging systems using Apache Kafka. We’ll show you how to build a cheap but effective cluster infrastructure with Apache Mesos. Finally, you will deep dive into the different aspect of SMACK using a few case studies. By the end of the book, you will be able to integrate all the components of the SMACK stack and use them together to achieve highly effective and fast data processing.

JavaScript Concurrency. Build better software with concurrent JavaScript programming, and unlock a more efficient and forward thinking approach to web development

Adam Boduch

Concurrent programming may sound abstract and complex, but it helps to deliver a better user experience. With single threaded JavaScript, applications lack dynamism. This means that when JavaScript code is running, nothing else can happen. The DOM can’t update, which means the UI freezes. In a world where users expect speed and responsiveness – in all senses of the word – this is something no developer can afford.Fortunately, JavaScript has evolved to adopt concurrent capabilities – one of the reasons why it is still at the forefront of modern web development. This book helps you dive into concurrent JavaScript, and demonstrates how to apply its core principles and key techniques and tools to a range of complex development challenges. Built around the three core principles of concurrency – parallelism, synchronization, and conservation – you’ll learn everything you need to unlock a more efficient and dynamic JavaScript, to lay the foundations of even better user experiences. Throughout the book you’ll learn how to put these principles into action by using a range of development approaches. Covering everything from JavaScript promises, web workers, generators and functional programming techniques, everything you learn will have a real impact on the performance of your applications. You’ll also learn how to move between client and server, for a more frictionless and fully realized approach to development. With further guidance on concurrent programming with Node.js, JavaScript Concurrency is committed to making you a better web developer.The best developers know that great design is about more than the UI – with concurrency, you can be confident every your project will be expertly designed to guarantee its dynamism and power.