Data Science from Scratch

To really learn data science, you should not only master the tools--data science libraries, frameworks, modules, and toolkits--but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these 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 the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today's messy glut of data.

* Get a crash course in Python
* Learn the basics of linear algebra, statistics, and probability--and 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

Pentru a invata cu adevarat stiinta datelor, nu numai ca ar trebui sa stapaniti instrumentele - biblioteci, cadre, module si seturi de stiinte de date - ci sa intelegeti si ideile si principiile care stau la baza lor. Actualizata pentru Python 3.6, aceasta a doua editie a Data Science from Scratch va arata cum functioneaza aceste instrumente si algoritmi implementandu-le de la zero. Daca aveti o aptitudine pentru matematica si unele abilitati de programare, autorul Joel Grus va va ajuta sa va simtiti confortabil cu matematica si statistici la baza stiintei datelor si cu abilitatile de hacking de care aveti nevoie pentru a incepe ca om de stiinta al datelor.