Data Science do Zero: Primeiras Regras Com o Python

As bibliotecas, estruturas, módulos e kits de ferramentas do data science são ótimas para desempenhá-lo mas, também, são uma ótima forma de mergulhar na disciplina sem ter, de fato, que entender data science. Neste livro, você aprenderá como os algoritmos e as ferramentas mais essenciais de data science funcionam ao implementá-los do zero. Se você tiver aptidão para matemática e alguma habilidade para programação, o autor Joel Grus lhe ajudará a se sentir confortável com matemática e estatística nos fundamentos de data science. Você precisará iniciar como um cientista de dados com habilidades de hackers. Atualmente, a grande massa de dados contém respostas para perguntas que ninguém nunca pensou em perguntar. Este guia fornece o conhecimento para desenterrar tais respostas. Obtenha um curso intensivo em Python; Aprenda o básico de álgebra linear, estatística e probabilidade ― e entenda como e quando eles são usados em data science; Colete, explore, limpe, mude e manipule dados; Vá fundo nos princípios do aprendizado de máquina; Implemente modelos como k-vizinhos mais próximos, Naive Bayes, regressão logística e linear, árvores de decisão, redes neurais e agrupamentos; Explore sistemas recomendados, processamento de linguagem natural, análise de rede, MapReduce e bases de dados. Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.Get a crash course in PythonLearn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data scienceCollect, explore, clean, munge, and manipulate dataDive into the fundamentals of machine learningImplement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clusteringExplore recommender systems, natural language processing, network analysis, MapReduce, and databases "Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. In this book, you'll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today's messy glut of data holds answers to questions no one's even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python. Learn the basics of linear algebra, statistics, and probability--and understand how and when they're used in data science. Collect, explore, clean, munge, and manipulate data. Dive into the fundamentals of machine learning. Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering. Explore recommender systems, natural language processing, network analysis, MapReduce, and databases."--Provided by publisher