10 Data Analyst books for every computer science student to read 

"Python for Data Analysis" by Wes McKinney 

This book is an excellent introduction to data analysis using Python, one of the most prominent data analysis programming languages. It discusses fundamental libraries such as pandas and NumPy and provides examples.

"Data Science for Business" by Foster Provost and Tom Fawcett 

This book is great if you want to learn about the practical applications of data analysis and machine learning in business. Those who are interested in addressing the divide between the fields of data science and business will find this book useful.

"Introduction to the Practice of Statistics" by David S. Moore, George P. McCabe, and Bruce A. Craig 

This book is commonly used in introductory statistics classes. It gives you a firm foundation in statistical principles and methods, which are necessary for data analysis.

"The Art of Data Science" by Roger D. Peng, Elizabeth Matsui, and Brian Caffo 

This book, based on a popular data science course at Johns Hopkins University, provides insights into the art of data analysis. It includes information on data visualization, data cleaning, and statistical modeling.

"Storytelling with Data" by Cole Nussbaumer Knaflic 

Visualization of data is a crucial component of data analysis. This book tells readers how to present data engaging and informative, making it accessible to a wider audience.

"Statistics" by Robert S. Witte and John S. Witte 

This comprehensive statistics textbook gives students a thorough understanding of statistical principles and procedures, making it ideal for advanced statistical analysis.

"Data Science for Dummies" by Lillian Pierson

This book is a good option if you're searching for a beginner-friendly introduction to data science. It covers essential concepts, tools, and techniques without overloading the reader with technical information.

"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron 

While this book is not primarily about data analysis, it is crucial for computer science students interested in machine learning, which is strongly related to data analysis. It goes over practical implementation utilizing well-known Python libraries.

"R for Data Science" by Hadley Wickham and Garrett Grolemund 

This book is a must-read for students who want to learn how to use R to analyze data. It shows how to use R, a powerful tool for statistical computing, to change, display, and model data.

"Statistical Learning with Sparsity"  

This book is designed for advanced students who are interested in statistical learning and machine learning with a focus on sparsity. It dives into the mathematical foundations of these subjects.

Top 9 books to read for ethical hackers who just start