Here is the list of Top 10 Data Science Books. You will love the Books if you are a Data Science Books Lover!
Book Name: Practical Statistics for Data Science
Author: Peter Bruce
Statistical methods are a key part of data science, yet very few data scientists have formal statistical training. Courses and books on basic statistics rarely cover this topic from a data science perspective. The second edition of this practical guide-which now includes examples of Python as well as R, explains ways to apply different statistical methods to data science, telling you how to avoid their misuse, and it gives you important advice on what is important and what is not.
Book Name: Python Data Science Handbook
Author: Jake VanderPlas
For many researchers, Python is a first-class tool primarily for sorting, manipulating, and gaining insights from its libraries. There are numerous resources available for individual pieces of this data science stack, but only through the Python Data Science Handbook, you get them all- IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.
Book Name: R for Data Science
Author: Garret Grolemund
If you want to market yourself to employers and stay current with your data science skills, you should have a good handle on R. R is in the neck with Python Than as the top programming language for the science of statistics. A recent data science community survey found that 52.1% of respondents use R, less than 52.5 who use Python. If you want to sharpen your R skills, R for Data Science is the best book.
Book Name: Advanced R
Author: Hadley Wickman
If you really want to differentiate yourself as an R user and impress employers, Advanced is a great resource. This includes everything from foundation to data structures, object-oriented programming, and from debugging to functional programming and performance codes. With the development of the RCPP package, users can now generate performance codes using R, taking advantage of the speed of C ++.
Book Name: Inflection Point
Author: Scott Stawski
My recommendation is that there is no technical book here but it is definitely necessary if you want to specialize in data science. So, what’s the point of this book? Business knowledge! This is mainly due to the fact that it talks about the evolution of the world after the emergence of cloud computing, big data, mobile device, and their applications, IoT devices, and more and more technologies. Revolutionized world business.
Book Name: The Elements of Statistical Learning
Author: Trevor Hastie
If you want to accelerate your machine learning career, you need to have a strong grasp of the basics, and modern topics. Statistics is the best way to learn elementary statistics to take your machine learning skills to the next level. This is a comprehensive book on machine learning. This book looks at everything from linear methods to neural networks, development, and random deforestation.
Book Name: Mining of Massive Datasets
Author: Jure Leskovec Anand Rajaraman
This is a great book based on Stanford courses on Large-scale data mining and network analysis. Focus data mining is on very large datasets. It is an important scale-level production-level model. Big companies like Google receive hundreds of million od search queries a day, so they’re especially interested in mining very large datasets.
Book Name: Storytelling with Data
Author: Cole Nussbaumer Knaflic
My recommendation goes back to the technical parts of data science but in the best ways! Everything we read as a story stays with us for a long time when we love it and find joy in the story. The author understands human nature perfectly and teaches us the concepts of data visualization in great stories.
Book Name: The Signal and the Noise
Author: Nate Silver
My best recommendation to you guys should be this book. This data is best suited for scientists who are trying to master this field both practically and technically. Below is one of the best books he has filed. The best-selling book demonstrates the power of big data analysis to make valuable predictions in highly discreet ways.
Book Name: Bayesian Methods for Hackers
Author: Cam Davidson-Pilon
This is a textbook of Bayesian statistics that takes the ‘’before understanding’’, ‘’second’’ approach to mathematics. Bayesian infusion is an important topic of machine learning that takes a different approach from classical reduction statistics. The Bayesian approach allows us to talk about things we already know. We can never be sure of a result, but with some forethought, we can have some confidence in a result.
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