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Practical Statistics for Data Scientists: 50 Essential Concepts

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Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.

Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.

With this book, you’ll learn:

  • Why exploratory data analysis is a key preliminary step in data science
  • How random sampling can reduce bias and yield a higher quality dataset, even with big data
  • How the principles of experimental design yield definitive answers to questions
  • How to use regression to estimate outcomes and detect anomalies
  • Key classification techniques for predicting which categories a record belongs to
  • Statistical machine learning methods that “learn” from data
  • Unsupervised learning methods for extracting meaning from unlabeled data
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Docker Cookbook: Over 100 practical and insightful recipes to build distributed applications with Docker , 2nd Edition
Docker Cookbook: Over 100 practical and insightful recipes to build distributed applications with Docker , 2nd Edition

Leverage Docker to deploying software at scale

Key Features

  • Leverage practical examples to manage containers efficiently
  • Integrate with orchestration tools such as Kubernetes for controlled deployments
  • Learn to implement best practices on improving...
Hands-on Kubernetes on Azure: Use Azure Kubernetes Service to automate management, scaling, and deployment of containerized applications, 3rd Edition
Hands-on Kubernetes on Azure: Use Azure Kubernetes Service to automate management, scaling, and deployment of containerized applications, 3rd Edition

Understand the fundamentals of Kubernetes deployment on Azure with a learn-by-doing approach

Key Features

  • Get to grips with the fundamentals of containers and Kubernetes
  • Deploy containerized applications using the Kubernetes platform
  • Learn how you can...
Pointers in C Programming: A Modern Approach to Memory Management, Recursive Data Structures, Strings, and Arrays
Pointers in C Programming: A Modern Approach to Memory Management, Recursive Data Structures, Strings, and Arrays

Gain a better understanding of pointers, from the basics of how pointers function at the machine level, to using them for a variety of common and advanced scenarios. This short contemporary guide book on pointers in C programming provides a resource for professionals and advanced students needing in-depth hands-on coverage of pointer...


Bootstrapping Microservices with Docker, Kubernetes, and Terraform: A project-based guide
Bootstrapping Microservices with Docker, Kubernetes, and Terraform: A project-based guide
Summary
The best way to learn microservices development is to build something! Bootstrapping Microservices with Docker, Kubernetes, and Terraform guides you from zero through to a complete microservices project, including fast prototyping, development, and deployment. You’ll get your feet wet using
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Data Structures and Algorithmic Thinking with Go: Data Structure and Algorithmic Puzzles
Data Structures and Algorithmic Thinking with Go: Data Structure and Algorithmic Puzzles

Peeling Data Structures and Algorithms:

The sample chapter should give you a very good idea of the quality and style of our book. In particular, be sure you are comfortable with the level and with our GoLang coding style. This book focuses on giving solutions for complex problems in data structures and...

Data Science Revealed: With Feature Engineering, Data Visualization, Pipeline Development, and Hyperparameter Tuning
Data Science Revealed: With Feature Engineering, Data Visualization, Pipeline Development, and Hyperparameter Tuning
This book introduces you to the world of data science. It reveals the proper way to do data science. It covers essential statistical and programming techniques to help you understand data science from a broad perspective. Not only that, but it provides a theoretical, technical, and mathematical foundation for problem-solving...
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