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Top data analytics books

As industries are generating a large amount of data, there is a demand for skilled data professionals. However, while taking your initial steps toward a career as a data analyst, it is essential to know the basic concepts, ideas, and trends of data. You can learn the basic concepts of data analytics with some of the top books on data analytics listed in this article.

  1. Data Analytics using R

Written by Seema Acharya, Data Analytics using R is a perfect book for students who want to pursue a career in data analytics. This book is also helpful for IT professionals, data analysts, and decision-makers responsible for strategic initiatives.

With this book, you can learn the potential of the R language as statistical data analysis and visualization tool. Seema Acharya also introduces readers to several data mining algorithms and charts. She has explained many real-life case studies in the book, such as fraud detection, customer insights analysis, sales forecasting, and more.

Link to the book: Data Analytics using R

  1. Python for Data Analysis: Data Wrangling with Pandas, Numpy, and IPython

This book by Wes Mckinney is the perfect book to learn data analysis with Python programming language. You will learn to use the latest version of Pandas, IPython, and Numpy in the data analysis process.  

This book is a practical and modern introduction to data science tools in Python. With this book, you can learn to use the IPython shell and advanced features in Numpy. You can also learn to use the data analysis tools in Pandas library and other flexible tools to clean, transform, merge and reshape data. 

While reading this book, you can analyze and manipulate regular and irregular time series data and solve real-life data analysis problems with detailed examples.

Link to the book: Python for Data Analysis: Data Wrangling with Pandas, Numpy, and IPython

  1. Python for Data Analysis: Data Wrangling with Pandas, Numpy, and IPython Third Edition

Written by Wes Mckinney, Python for Data Analysis: Third Edition is a hands-on guide through practical case studies, showing you how to solve real-life data analysis problems. This book is an updated version of Python for Data Analysis: Data Wrangling with Pandas, Numpy, and IPython.

This book is updated with Python 3.10 and Pandas 1.4. The code examples and the datasets used in this book are readily available on GitHub.

Link to the book: Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter: Third Edition

  1. Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, and visualization, 2nd Edition

Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, and visualization, 2nd Edition by Stefanie Molin is another good book learning efficient data analysis and manipulation tasks using Pandas 1. x.

With this book, you can learn to use Pandas in different real-world problems with the help of step-by-step explanations. This book helps you analyze data, start with machine learning algorithms, and work with Python libraries such as Numpy, Matplotlib, Seaborn, and Sci-kit learn.

While reading this book, you can learn to use Pandas library to perform data wrangling for reshaping, cleaning, and aggregating data. You will also learn how to conduct exploratory data analysis with statistics and visualization of data patterns.

This book is suitable for data science beginners, Python developers, and data analysts who want to explore every data analysis stage with various datasets.

Link to the book: Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, and visualization, 2nd Edition

  1. Data Analysis and Machine Learning with Kaggle: How to compete on Kaggle and build a successful career in data science

Written by Konrad Banachewicz, Luca Massaron, and Anthony Goldbloom, Data Analysis and Machine Learning with Kaggle is one of the most recommended books for data analysis.

With this book, you can learn how Kaggle works and sharpen your skills with ensembling, AutoML, feature engineering, and adversarial validation. You will also be exposed to general techniques for approaching tasks based on image, text, tabular data, and reinforcement learning.

This is one of the best books for anyone new to Kaggle who wants to study data analysis or to perform better in Kaggle competition and secure jobs in data analysis.

Link to the book: Data Analysis and Machine Learning with Kaggle: How to compete on Kaggle and build a successful career in data science

  1. SQL for Data Analysis: Advanced Techniques for Transforming Data into Insights (Grayscale Indian Edition)

SQL for Data Analysis: Advanced Techniques for Transforming Data into Insights (Grayscale Indian Edition) by Cathy Tanimura is the perfect book to learn data analysis with SQL. It provides different ways to improve your SQL skills and solve real-life data analysis problems.

With this book, you can learn the basic steps to prepare your data for analysis. Using the SQL’s date and time manipulations, you can perform a time series analysis. While reading this book, you can also explore SQL’s powerful functions and operators that are useful for text analysis.

This book is suitable for people interested in learning data analysis with SQL language by using functions like joins, subqueries, regular expressions, and window functions.

Link to the book: SQL for Data Analysis: Advanced Techniques for Transforming Data into Insights (Grayscale Indian Edition)

  1. Microsoft Excel 2016 Data Analysis and Business Modelling

Written by Wayne L. Winston, the Microsoft Excel 2016 Data Analysis and Business Modelling book enables you to master data analysis and business modeling with Microsoft Excel 2016. With this book, you can also transform data into bottom-line results.

This book helps you use Excel’s new tools to ask the right questions and get accurate or actionable answers. It contains over 150 data analysis problems with solutions and a chapter of basic spreadsheet models to help you get started.

Link to the book: Microsoft Excel 2016 – Data Analysis and Business Modeling

  1. Data Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientists

Philipp K Janet’s book is one of the best resources for working with data in a business environment. With this book, you can learn what data contains, how to capture conceptual ideas in data, and then use this understanding in organizations with the help of business plans, metrics dashboards, and other applications.

This book uses graphics for describing data with one, two, or many variables. This book can teach you to create conceptual models using calculations and probability arguments.

With this book, you can learn to mine data using computationally intensive clustering and simulation. You also learn to use dimensionality reduction techniques and predictive analytics to conquer data analysis solutions.

Link to the book: Data Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientists

  1. Pandas for Everyone: Python Data Analysis, First Edition

Written by Daniel Y. Chen, Pandas for Everyone: Python Data Analysis, First Edition is one of the best books for learning data analysis with Python. Daniel Y. Chen explains every concept with relevant examples from modern data analysis.

With this book, you can learn the open-source Pandas library and use Python for rapidly automating and performing any data analysis task virtually, no matter how large or complex.

Pandas library helps you ensure your data’s accuracy, visualize it for effective decision making and reliably reproduce analyses across many datasets.

Link to the book: Pandas for Everyone: Python Data Analysis, First Edition

  1. Data Analytics using Python

The Data Analytics using Python book by Bharti Motwani aims to make students understand analytics applications in different domains with proper code and explanation. This book explains the basics of Python to machine learning concepts. It mainly focuses on deep learning models like MLP, RNN, and CNN to train models for text and image data and to develop chatbots. The book consists of different chapters, such as core libraries in Python, programming in Python, tools, and techniques used for data analysis in Python.

Dr. Bharti Motwani has over 22 years of experience in research, teaching, corporate, and consultancy. She has written many data analytics books and demonstrated proficiency in helping Ph.D. candidates, editing books, and reviewing journals.


Link to the book: Data Analytics using Python

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Manjiri Gaikwad
Manjiri Gaikwad
Manjiri is a computer science graduate from Cummins college of engineering, Pune. She is a simple calm and composed person, who loves to write.

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