Data Science is very hot and demanding field which contains methods and techniques from the other fields like statistics, machine learning, artificial intelligence, Bayesian and many more other fields. The main purpose of these fields is to generate meaningful insights from the collected data. So for this solution we come up with the list of some machine learning books so that we can get the knowledge about various fields as well and this field is gaining its popularity.
The person who is very good in understanding the computer algorithms and understand the statistics and mathematical ideas and applying these to the knowledge's from the computer science and mathematics into a particular application. Where somebody sees the value coming out from the data is called data science.
What is Data Science?
Data Science involves using automated methods to analyze massive amounts of data and to extract knowledge from them. And by combining aspects of statistics, computer science, applied mathematics and visualization, data science can turn the vast amounts of data the digital age generates into new insights and new knowledge.
Hence, the data science or data-driven science is about asking the right questions and exploring the data after this we do modeling of the data using various algorithms and finally communicating and visualizing the results thereof.
This book tells us about the core libraries which are mandate for working with data in Python basically IPython, NumPy, Pandas, Matplotlib, Scikit-Learn and related packages. The author of this book is Jake VanderPlas.
This book is perfect to learn about the concepts of Big Data and Machine Learning. The author of this book is Kareem Alkaseer.
Think Stats focuses on the very simple technique that how we can search real data sets and can answer the questions. This is one the most recommended book for data science. The author of this book is Allen B Downey.
This book is the introduction to Bayesian statistics using various computational methods. This book uses Python code instead of Math. The author of this book is Allen B Downey.
CIML is used for the introductory material which covers all the modern machine learning techniques like supervised learning, unsupervised learning, large margin methods, probabilistic modeling and learning theory etc. The author of this book is Hal Daume III.
The prime goal of this book is to get you familiar with the all-important ideas of unsupervised feature learning and deep learning. The author of this book is Andrew Ng, Chuan Yu Foo, Caroline Suen and Yifan Mai.
Hence, these all are the collection of very good books which every data scientists should read and a data scientist is someone who is better at statistics. Yes, Data Science is on pace and the hottest demanding role right now both form companies and employee's perspective hence it is the highest paid field to get into.