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