Packt Publishing

The ability to proficiently perform statistical modeling is a fundamental skill for data scientists and essential for businesses reliant on data insights. Building Statistical Models with Python is a comprehensive guide that will empower you to leverage mathematical and statistical principles in data assessment, understanding, and inference generation.

This book not only equips you with skills to navigate the complexities of statistical modeling, but also provides practical guidance for immediate implementation through illustrative examples. Through emphasis on application and code examples, you'll understand the concepts while gaining hands-on experience. With the help of Python and its essential libraries, you'll explore key statistical models, including hypothesis testing, regression, time series analysis, classification, and more.

By the end of this book, you'll gain fluency in statistical modeling while harnessing the full potential of Python's rich ecosystem for data analysis.

Globally, 275,000 new business ventures get launched every single day, and ninety percent of them fail. One of the most fundamental reasons for that is that they don't solve a real market problem that a real market population has, in a way that resonates with that market and sells their solution. Consequently, they struggle to gain traction and attain scale.
In this book, you'll learn what business models are. Additionally, you'll find out what business model innovation is and, ultimately, how to use Design Thinking to identify not just a winning value proposition but also bring that value proposition to the market in a way that resonates with customers. In doing so, you'll be able to unlock maximum value for your business, allowing it to attain maximum scale through growing waves of adopters.
By the end of this book, you'll understand what you need to do to uncover your target markets' 'reason to buy', as well as how to wrap a winning business model around that reason so that your business can gain traction and achieve scale.

This book is a guide for developers and engineers to use Hadoop and Hive for scalable big data applications. It covers reading, writing, and managing large datasets with Hive and provides a concise introduction to Apache Hadoop and Hive, detailing their collaboration to simplify development. Through clear examples, the book explains the logic, code, and configurations needed for building successful distributed applications.
The course starts with an introduction to big data and Apache Hadoop fundamentals. It then covers the Hadoop Distributed Filesystem and how to get started with Hadoop. The journey continues with interfaces to access HDFS files, resource management with Yet Another Resource Negotiator, and MapReduce for data processing. The book also explores Hive architecture, storage types, and the Hive query language.
Mastering these concepts is vital for creating scalable big data solutions. This book ensures a smooth transition from novice to proficient Hadoop and Hive user, providing practical skills and comprehensive knowledge. By the end, readers will be able to set up, configure, and optimize Hadoop, utilize Hive for data management, and effectively solve big data challenges.