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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Top 10 Books to Master SQL Concepts in 2025

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 16 Dec, 2024
7 min read

Introduction

Structured Query Language (SQL) is the backbone of relational database management systems. It serves as a powerful tool for manipulating and retrieving data from large databases. As we step into 2025, the demand for SQL proficiency continues to rise across various industries, emphasizing the need for professionals to master its concepts thoroughly. This article will explore the top 10 books that stand out as indispensable guides to mastering SQL concepts in 2025.

Whether you are a seasoned developer, a data analyst, or a database administrator, having the right resources at your disposal is crucial for staying ahead in the dynamic world of SQL. These recommendations cover a wide spectrum of SQL topics, from performance optimization to practical applications, ensuring a comprehensive understanding of the language. So get ready to explore and build your expertise in SQL with the books listed below.

Table of Contents

Top 10 Books to Master SQL Concepts in 2025

1. SQL Performance Explained, by Markus Winand

Right on top of our list is Markus Winand’s “SQL Performance Explained”. It is regarded as a go-to resource for developers aspiring to optimize SQL queries and elevate database performance. Beyond the surface-level discussions, Winand delves into the intricacies of SQL performance tuning, providing practical insights and strategies. The book serves as a cornerstone for developers aiming to craft high-performance applications, with Winand’s lucid explanations and real-world examples making complex performance concepts accessible to developers of varying experience levels.

You can find the book here.

"SQL Performance Explained" by Markus Winand, SQL books
Source: Amazon

2. SQL Antipatterns: Avoiding the Pitfalls of Database Programming, by Bill Karwin

Bill Karwin’s “SQL Antipatterns,” is a seasoned guide, leading developers through the labyrinth of common pitfalls in database programming. Tailored for those aspiring to create robust and maintainable database solutions, Karwin’s book provides not just a mere list of antipatterns but a comprehensive exploration of their origins and pragmatic solutions. The real-world scenarios presented in the book offer actionable insights, making it an invaluable guide for developers keen on honing their skills and crafting resilient database architectures.

You can find the book here.

"SQL Antipatterns: Avoiding the Pitfalls of Database Programming" by Bill Karwin
Source: Amazon

3. Learning SQL, by Alan Beaulieu

In the realm of database management and programming, Alan Beaulieu’s “Learning SQL” shines as a guiding light for those taking their first steps. Beaulieu takes a user-friendly approach, leading readers through the foundational aspects of SQL with exceptional clarity and accessibility. More than a theoretical guide, this book actively nurtures practical skill development through hands-on exercises and real-world examples. For software developers entering the world of SQL, “Learning SQL” proves to be an indispensable companion, laying a solid foundation and unraveling the intricacies of this potent language in a way that feels natural and approachable.

You can find the book here.

"Learning SQL" by Alan Beaulieu
Source: Amazon

4. SQL Performance Tuning, by Peter Gulutzan and Trudy Pelzer

“SQL Performance Tuning” by Peter Gulutzan and Trudy Pelzer emerges as a comprehensive guide for developers striving to master the art and science of optimizing SQL performance. The authors provide practical advice and techniques, making this book an essential read for professionals engaged in large-scale database applications. With a focus on fine-tuning SQL queries, this resource equips developers to ensure optimal efficiency and speed in their database operations.

You can find the book here.

"SQL Performance Tuning" by Peter Gulutzan and Trudy Pelzer
Source: Amazon

5. SQL Cookbook, by Anthony Molinaro

Anthony Molinaro’s “SQL Cookbook” stands out as a unique and practical guide for developers seeking hands-on experience in overcoming specific challenges in database programming. Molinaro presents a collection of recipes designed to address common SQL problems, providing real-world solutions and examples. The book serves as an invaluable resource for developers looking to expand their skill set through practical applications. It also gives them a deeper understanding of how to overcome specific challenges in database programming.

You can find the book here.

"SQL Cookbook" by Anthony Molinaro
Source: Amazon

6. Head First SQL, by Lynn Beighley and Michael Morrison

“Head First SQL” by Lynn Beighley and Michael Morrison introduces a distinctive and engaging approach to teaching SQL concepts. Leveraging a visually rich format, this book aims to make learning SQL an enjoyable and effective experience. Tailored to accommodate different learning styles, “Head First SQL” ensures a holistic understanding of SQL principles through immersive learning experiences. The book is an ideal choice for those who prefer a more interactive and engaging learning journey.

You can find the book here.

"Head First PHP & MySQL" by Lynn Beighley and Michael Morrison
Source: Amazon

7. SQL Pocket Guide, by Jonathan Gennick

Jonathan Gennick’s “SQL Pocket Guide” stands as a compact yet comprehensive reference for SQL developers. Covering syntax, functions, and best practices, this guide serves as an indispensable resource for quick on-the-job assistance. The book caters to developers seeking a reliable and concise reference for SQL programming. For working professionals, it is more like having a handy guide at their disposal.

You can find the book here.

"Pocket Guide" by Jonathan Gennick
Source: Amazon

8. Pro SQL Server Internals, by Dmitri Korotkevitch

Tailored for those working within the Microsoft SQL Server ecosystem, “Pro SQL Server Internals” by Dmitri Korotkevitch is a must-read. This book provides an in-depth exploration of the inner workings of an SQL server, offering crucial insights for developers and database administrators navigating the Microsoft landscape. Korotkevitch’s expertise makes this book indispensable for anyone seeking a profound understanding of SQL server internals.

You can find the book here.

"Pro SQL Server Internals" by Dmitri Korotkevitch
Source: Amazon

9. SQL For Smarties, by Joe Celko

Joe Celko’s “SQL For Smarties” is a comprehensive guide that dives into advanced SQL concepts. Celko, a renowned expert in the field, introduces readers to intricate puzzles and challenges, providing sophisticated solutions that go beyond the basics. This book is ideal for developers looking to deepen their understanding of SQL, explore complex scenarios, and gain insights into optimizing queries with finesse.

You can find the book here.

"SQL For Smarties" by Joe Celko
Source: Amazon

10. Effective SQL: 61 Specific Ways to Write Better SQL, by John L. Viescas

“Effective SQL: 61 Specific Ways to Write Better SQL” is a practical handbook for developers seeking actionable tips to enhance their SQL skills. Viescas presents 61 specific strategies for optimizing SQL code, designing efficient databases, and avoiding common pitfalls. Each tip is concise and immediately applicable, making it a valuable resource for developers aiming to write more effective and maintainable SQL code. “Effective SQL” is a go-to guide for those looking to refine their SQL practices in a straightforward and practical manner.

You can find the book here.

"Effective SQL: 61 Specific Ways to Write Better SQL" by John L. Viescas
Source: Amazon

Conclusion

As we navigate the ever-evolving landscape of SQL in 2025, these 10 books promise to help learners master its concepts. From foundational knowledge to optimizing performance and hands-on practical applications, each book on this list caters to various aspects of SQL. Whether you’re an experienced professional looking to fine-tune your SQL skills or a beginner seeking to build a strong foundation, these books offer valuable insights and practical knowledge. So start reading and get ready to become an SQL expert in 2025.

As you embark on your SQL learning journey, do remember that reading alone isn’t enough. You need to hone the skills you learn through these books, by practicing on real-world problems. Analytics Vidhya’s Certified AI & ML BlackBelt Plus Program offers a variety of projects and assignments to practice your SQL skills. The guided projects in this mentorship program will help you get familiar with MySQL and lead you to SQL mastery. The assignments are designed to teach you to create and update reports as well as perform data analysis using SQL. Additionally, it also features assignments to help you master Apache Spark SQL using Python.

The knowledge from the books listed in this article, along with hands-on experience with our guided projects, will definitely make you a master of SQL. Let these resources be your trusted companions, propelling you towards becoming a proficient and confident SQL practitioner in 2025.

Frequently Asked Questions

Q1. Can you recommend advanced SQL books for experienced users?

Delve into advanced topics with books like “SQL Performance Explained” by Markus Winand and “High-Performance MySQL” by Baron Schwartz.

Q2. Are there any SQL books for self-paced learning?

Yes, consider titles like “SQL Practice Problems” by Sylvia Moestl Vasilik and “SQL Performance Explained” for practical, hands-on learning.

Q3.Which SQL books cover the latest trends and updates in 2025?

Stay current with books like “SQL Performance Explained” and “Pro SQL Server on Linux” by Bob Ward, addressing the latest trends and developments.

Q4. Do you have recommendations for SQL books with real-world examples?

Dive into practical applications with books like “SQL Performance Explained” and “SQL Practice Problems,” which provide real-world scenarios and examples.

K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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