by Apoorva Bellapu
March 12, 2022
The growing demand for data scientists indicates that you need to have strong knowledge about data.
When everyone is well aware that data is the most crucial aspect for a business to achieve its goals, who wouldn’t want to be part of such a promising industry? The very fact that organizations rely heavily on data to make informed decisions is proof enough of the importance of data. This ultimately sheds light on the demand for data science as a career. The growing demand for data scientists across the world indicates that you must have strong data knowledge to land a job in the magical world of data. On that note, here is a list of the top 10 must-read data science books for aspiring candidates.
Practical Statistics for Data Scientists – By Peter Bruce and Andrew Bruce
This book is a new delight for beginners as it covers a wide range of topics such as randomization, distribution, sampling, etc. from zero. The language is absolutely easy to understand. So, someone who has no knowledge can get the most out of this book.
Data Science and Big Data Analytics: discovering, analyzing, visualizing and presenting data
This book by John Wiley & Sons covers a number of activities and methods and tools that data scientists use. Whether it’s concepts, principles or practical applications, this book has it all. Using examples, you would be able to replicate using open source software.
Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville
If you aspire to enter the machine learning and data-driven deep learning paths in data science, then this book is for you! From fundamental practical aspects of data science to applications of machine learning, this book will help you navigate it all.
Introduction to Machine Learning with Python: A Guide for Data Scientists – By Andreas C. Müller and Sarah Guido
Machine learning is an important aspect of data science. With this in mind, Andreas C. Müller and Sarah Guido have put together this excellent guide. This book will help beginners understand the basics of ML and Python.
R for Data Science
R is an important language for data scientists. This is where the book – “R for Data Science” is a blessing. This book covers a wide range of topics, including data management, programming, data mining, data modeling, and communication, to name a few.
Python for Data Analysis – By Wes McKinney
Just like R, Python is also a popular programming language in data science. That’s why the “Python for Data Analysis” book is a complete guide for beginners eager to learn the concepts of Data Analytics with Python. This book will help you build real applications.
Data Science from Scratch: First Principles with Python by Joel Grus
Through this book, Joel Grus insists that aspiring data scientists must understand the ideas and principles before mastering the tools and modules. This book does just that – it shows how machine learning tools and algorithms work by implementing the principles from scratch.
Python Data Science Handbook – By Jake VanderPlas
For a beginner knowing the basics of Python, this book is simply ideal. This book not only gives you an overview of Python, but also teaches how one can work with Python libraries. Python libraries such as Pandas, Numpy, Matplotlib, Scikit-learn, etc. are adequately covered in this book.
Data Science for Dummies by Lillian Pierson
Another great book for someone aspiring to be a data scientist is – Data science for Dummies by Lillian Pierson. Topics like Data Science Basics, Big Data, Python, R, SQL, Data Visualization, Real-Time Analytics, IoT, are all covered in this book.
Understanding Machine Learning: From Theory to Algorithms – By Shai Shalev-Shwartz and Shai Ben-David
Since machine learning is an important concept in the field of data science, an aspiring data scientist must have strong knowledge about it. The book covers machine learning basics, algorithms in ML, additional learning models, and advanced theory.
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