Data Science from Scratch: First Principles with Python
$65.99
669 in stock
Refresh Stock LevelInformation
Shipping
We currently offer free shipping on all orders over $100. Standard media mail shipping is $7.50 plus $1 for each additional book. Electronics are $35 shipping on all items.
Books
We get our books from a national distributor and although we strive to present up to date stock counts, stock constantly fluctuates. We perform a stock check when you add your book to the cart to ensure that it is available for shipping from the distributor. You can also check stock status by clicking the refresh stock link on the product page for the most up to date stock at the distributor. If an item is on backorder, you may place an order and we will update you on the estimated ship date as soon as we can confirm with the distributor.
Return & exchange
If you are not satisfied with your purchase you can return it to us within 14 days for an exchange or refund. More info.
Assistance
Can’t find what you’re looking for? We have access to over 13 million titles, reach out and see if we can help!
Contact us on (575) 322-6867, or email us at business@rabsbooks.com.
| Weight | 1.4 lbs |
|---|---|
| Dimensions | 9.1 × 6.9 × 0.9 in |
Description
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, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering
- Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
O’Reilly Media
Reviews
There are no reviews yet.