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Data science is an evolving subject that demands ongoing learning. Books continue to be among the best methods of acquiring in-depth knowledge and practical experience. Listed below are 15 books which deal with different elements of data science ranging from basic principles to sophisticated techniques.
This book is perfect for one who wishes to gain a deeper insight into statistical techniques in machine learning. Regression, classification, and neural networks are all included.
This book is useful for those who are interested in Bayesian techniques, and it describes statistical methods for pattern recognition.
Good for starters, this book elucidates statistical learning methods with the help of practical examples in R.
This is helpful for people who work with Python. It deals with common libraries like NumPy, Pandas, Matplotlib, and Scikit-learn.
This publication fills the knowledge gap between data science and its application in business, hence appealing to professionals wishing to use data science in business decision-making.
This hands-on book educates on machine learning through the usage of Python frameworks and deep learning techniques.
An in-depth guide to the basics and developments in deep learning.
This book deals with the real-world implementation of machine learning models.
A concise yet powerful introduction to machine learning, this book distills key ML concepts, from supervised and unsupervised learning to deep learning, in a highly accessible format. It is ideal for beginners and professionals looking for a quick reference.
Focusing on data visualization and communication, this book teaches how to present data-driven insights effectively. It emphasizes using charts, graphs, and narratives to make data more engaging and understandable.
This book bridges the gap between statistical theory and data science applications. It covers essential statistical concepts, hypothesis testing, regression, and data visualization techniques relevant to data scientists.
An introductory book that teaches data science fundamentals by building algorithms from the ground up using Python. It covers topics like probability, statistics, machine learning, and data wrangling.
This book focuses on the process of conducting data science projects effectively. It provides a conceptual framework for formulating hypotheses, analysing data, and deriving actionable insights.
A detailed introduction to Bayesian inference and probabilistic programming, this book demonstrates how to apply Bayesian methods using Python libraries like PyMC3. It is ideal for those interested in advanced statistical modelling.
A guide to understanding the principles of working with large-scale data systems effectively.
All of these books offer useful information about data science for varying levels of knowledge. They touch on everything from machine learning and statistics to deep learning and data visualization. For the beginner or those wanting to improve capabilities, these books offer structured learning and applied knowledge.
