Ads · Data science for business book
Ad
related to: data science for business bookBrowse & Discover Thousands of Computers & Internet Book Titles, for Less.
Search results
Sep 17, 2013 · Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect.
1. Data Science from Scratch: First Principles with Python by Joel Grus. Data Science from Scratch is a perfect book for beginners. After the successful first edition of the book, Joel Grus introduced a revised edition that covers the basics of data science using the Python 3 programming language.
- Non-Technical Data Science Books
- General Data Science Books
- Python For Data Science Books
- R For Data Science Books
- Statistics Books
These are books that can help motivate you to start or continue your data science journey. Or they may help you better understand important issues in the data science field. You won’t learn many practical skills from them, but they’re good reads that help show how data and statistics are used in the real world.
1. The Elements of Data Analytic Style
Rating: 3.9/5 (12) This book by Johns Hopkins professor Jeff Leek is a useful guide for anyone involved with data analysis. It covers a lot of the little details you might miss in statistics lessons and textbooks. Since it's a pay-what-you-want book, you cantechnically get this one for free. Of course, we recommend making a contribution if you can.
2. The Art of Data Science
Rating: 4.6/5 (44) This is another pay-what-you-want book. It takes a big-picture view of how to do data science rather than focusing on the technical nitty-gritty of statistical or programming techniques.
3. An Introduction to Data Science
Rating: 4.4/5 (74) This introductory textbook was written by Syracuse professor Jeffrey Stanton. Not surprisingly, it covers a lot of the fundamentals of data science and statistics. It also covers some R programming. Still, some sections are worthwhile reading even for those who are learning Python.
1. Python Data Science Handbook
Rating: 4.6/5 (586) An O’Reilly text by Jake VanderPlas, this book is also available as a series of Jupyter Notebooks on Github. It’s not for total beginners since it assumes some knowledge of Python programming basics. (But don’t worry – we’ve got an interactive Python course you can takefor that).
2. Automate the Boring Stuff with Python
Rating: 4.7/5 (2,494) This total beginner’s Python book isn’t focused on data science specifically. Still, the introductory concepts it teaches are all relevant in data science. Plus, some of the specific skills later in the book (like web scraping and working with Excel files and CSVs) will also be of use to data scientists.
3. A Byte of Python
Rating: 3.9/5 (9) Like Automate the Boring Stuff, this is a well-liked Python-from-scratch ebook. It also teaches the basics of the language to total beginners. It’s not data-science-specific, but most of the concepts it covers are relevant to data scientists. It has also been translated into a wide variety of languages, so it’s easily accessible to learners all over the globe.
1. R Programming for Data Science
Rating: 4.2/5 (20) Roger D. Peng’s text will teach you the basics of R programming from scratch. This is a pay-what-you-want text. Note that for $20 you can get it with all of the mentioned datasets and code files.
2. An Introduction to Data Science
Rating: 4.4/5 (74) This introductory text was already listed above, but we’re listing it again in the R section because it does cover quite a bit of R programming for data science.
3. Advanced R
Rating: 4.8/5 (143) This is precisely what it sounds like: a free online text that covers advanced R topics. It’s written by Hadley Wickham, one of the most influential voices in the R community.
3. Bayesian Methods for Hackers
Rating: 4.3/5 (128) Here’s another free read on Bayesian statistics and programming. The cool thing about this one is that the chapters are in Jupyter Notebook form, so it’s easy to run, edit, and tinker with all of the code you come across.
4. Statistical Inference for Data Science
A rigorous look at statistical inference. This one is for readers who are already somewhat comfortable with basic statistics topics and programming with R.
7. Data Mining and Machine Learning
Rating: 4.7/5 (18) This Cambridge University Press text will take you deepinto the statistics and algorithms used for various types of data analysis.
Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect.
Nov 3, 2022 · That being said, here, we explore 18 of the best data science books in the world today, highlighting the very features, topics, and insights that make each of these institutional data-centric bibles crucial for the success of your career and business.
These data science books are recommended by experts. They cover everything from beginner-level knowledge and algorithmic bias to designing databases and building neural networks.
