Top Data Analytics Books to Read in 2024

In today’s data-driven world, data analytics plays a key role in helping organizations make better decisions, identify opportunities, and mitigate risks. Data analytics enables businesses to gain insights into customer preferences and market dynamics, enhancing overall performance. As such, the demand for competent analysts has increased significantly over the past few years. This article lists the top data analytics books one should read in 2024 to augment one’s skills and stay ahead in this rapidly evolving field.

Python for Data Analysis

“Python for Data Analysis” is a comprehensive guide to manipulating, processing, and cleaning datasets in Python. It covers the tools to load, clean, transform, merge, and reshape data, focusing on libraries like Pandas and Numpy. The book also teaches how to solve real-world problems with detailed examples.

Fundamentals of Data Analytics

This book is a guide to the data analytics process, providing a five-step framework to help readers start the journey of analyzing data. The book covers the data mining and machine learning principles and provides strategies to build a problem-solving mindset.

Data Analytics for Absolute Beginners

This book is aimed at beginners and provides an introduction to data, data visualization, business intelligence, and statistics. The book consists of numerous practical and visual examples, along with coding exercises in Python. It also covers some of the machine learning concepts like regression, classification, and clustering.

Everything Data Analytics

“Everything Data Analytics” is a beginner’s guide to data literacy that helps understand the process of turning data into insights. The book covers the process of data collection, management, and storage, along with the essential machine-learning algorithms necessary for analysis, like regression, classification, and clustering.

SQL for Data Analysis

“SQL for Data Analysis” covers improving one’s SQL skills and making the most of SQL as part of their workflow. The book provides some advanced techniques for transforming data into insights, covering topics like joins, window functions, subqueries, and regular expressions.

Advancing into Analytics

This is a practical guide for Excel users to help them gain an understanding of analytics and the data stack. The author covers the key statistical concepts with spreadsheets and helps Excel users transition to performing exploratory data analysis and hypothesis testing using Python and R.

Modern Data Analytics in Excel

This book covers the features of modern Excel and the powerful tools for analytics. The author teaches how to leverage tools like Power Query and Power Pivot to build repeatable data-cleaning processes and create relational data models and analysis measures. The book also covers using AI and Python for more advanced Excel reporting.

Data Visualization with Excel Dashboards and Reports

This book teaches how to analyze large amounts of data in Excel and report them in a meaningful way. It also teaches the fundamentals of data visualization and covers how to automate redundant reporting and analyses.

Data Analysis for Business, Economics, and Policy

This book is a practical guide to using tools to carry out data analysis to support better decision-making in business, economics, and policy. The book covers topics like data wrangling, regression analysis, and causal analysis, along with numerous case studies with real-world data.

Storytelling with Data

“Storytelling with Data” is a data visualization guide for business professionals. The book teaches how to convert the data into a high-impact visual story to resonate the message with the audience.

Fundamentals of Data Visualization

This book provides a guide to making informative and compelling figures that help convey a compelling story. The book also provides extensive examples of good and bad figures.

Data Visualization: A Practical Introduction

This book covers how to create compelling visualizations using R programming language, more specifically using the ggplot2 library. It covers topics like plotting continuous and categorical variables, grouping, summarizing, and transforming data for plotting, creating maps, and refining plots to make them more understandable.

Naked Statistics

“Naked Statistics” is a beginner-friendly book focusing on the underlying intuition driving statistical analysis. The book covers topics like inference, correlation, and regression analysis in a witty and funny manner, which simplifies the learning process.

The Art of Statistics

“The Art of Statistics” is a practical guide to using data and mathematics to understand real-world problems better. The book covers how to clarify questions and assumptions and interpret the results.

Essential Math for Data Science

This book teaches the mathematics essential for excelling in data science, machine learning, and statistics. It covers topics like calculus, probability, linear algebra, and statistics, as well as their applications in algorithms like linear regression and neural networks.

Practical Statistics for Data Scientists

This book covers how to apply statistical methods to data science using programming languages like Python and R. It emphasizes the importance of exploratory data analysis and also covers the underlying statistical concepts behind supervised and unsupervised machine learning algorithms. 

Business unIntelligence

This book talks about the ever-changing and complex business intelligence landscape in today’s world. It covers numerous new models that businesses can leverage to design support systems for future successful organizations.

Data Science for Business

This book covers how organizations can leverage data science to gain a competitive advantage. It talks about general concepts that are useful in extracting knowledge from data. The book also provides various real-world examples to explain different concepts.

The Model Thinker

This book guides how to organize, apply, and understand the data that is being analyzed to become a true data ninja. The book covers mathematical, statistical, and computational models such as linear regression and random walks and provides a toolkit for its readers to make them leverage data to their advantage.

Becoming a Data Head

“Becoming a Data Head” teaches how to think, speak, and understand data science and statistics. It also covers the recent trends in machine learning, text analytics, and artificial intelligence.

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