Data Science Book | practical statistics for data scientists | python data science handbook

Here are 10 highly regarded data science books along with their respective authors.
00:05
The Elements of Statistical Learning, Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
00:15
Python for Data Analysis by Wes McKinney.
00:18
Data Science for Business What You Need to Know About Data Mining and Data Analytic Thinking by Foster Provost and Tom Fossett.
00:27
Hands on Machine Learning was sick at Learn Keras and Tensorflow concepts, Tools and Techniques to Build Intelligent Systems by Aurelian Jaren.
00:37
Pattern Recognition and Machine Learning by Christopher M Bishop.
00:42
Big Data A Revolution That Will Transform How We Live, work, and Think.
00:46
By Victor Mayer Schonberger and Kenneth Cukier.
00:50
Data Science from Scratch First Principles with Python by Joel Grass.
00:56
Deep Learning by Ian Goodfellow, Yoshua Banjo and Aaron Courville.
01:01
R for data science Import, tidy, transform, visualize, and model Data by Hadley Wickham and Garrett Groland.
01:10
The Data Science Handbook by Field Katie.
01:13
These books cover a range of topics in data science, including statistical learning, data analysis with Python, machine learning, big data, deep learning, and data visualization.
01:26
They are highly recommended resources for individuals interested in expanding their knowledge and skills in the field of data science.