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
Blockchain Basics: A Non-Technical Introduction in 25 Steps
In 25 concise steps, you will learn the basics of blockchain technology. No mathematical formulas, program code, or computer science jargon are used. No previous knowledge in computer science, mathematics, programming, or cryptography is required. Terminology is explained through pictures, analogies, and metaphors.
Agile UX Storytelling: Crafting Stories for Better Software Development
Learn how to use stories throughout the agile software development lifecycle. Through lessons and examples, Agile UX Storytelling demonstrates to product owners, customers, scrum masters, software developers, and designers how to craft stories to facilitate communication, identify problems and patterns, refine collaborative...
Practical Data Science Cookbook - Second Edition
Over 85 recipes to help you complete real-world data science projects in R and Python
About This Book
Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data
Get beyond the theory and implement real-world projects in data science using...
Coding Projects in Python
Using fun graphics and easy-to-follow instructions, this straightforward, this visual guide shows young learners how to build their own computer projects using Python, an easy yet powerful free programming language available for download.
Perfect for kids ages 10 and over who are ready to take a second step after Scratch, Coding...
Introduction to Machine Learning with Python: A Guide for Data Scientists
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available...