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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

9 Best Statistics Books for Data Science in 2025

D
Deepsandhya Shukla Last Updated : 06 Dec, 2024
5 min read

Introduction

The role of statistics in the dynamic field of data science is foundational, acting as the critical toolset for analyzing and making sense of the vast data landscapes of today. With countless resources at our disposal, selecting the right guide to navigate the intricate world of statistics can be overwhelming. This guide aims to simplify that choice by presenting a handpicked selection of ten outstanding statistics books, each carefully chosen for its relevance and appeal to those passionate about data science. These books are distinguished not only by their comprehensive coverage of statistical fundamentals but also by their engaging approach and practical relevance to data science projects.

Statistics in Data Science and Its Significance

At the heart of data science lies statistics, a discipline that equips practitioners with the ability to make sound decisions, anticipate future trends, and effectively communicate their discoveries. It’s the key to distinguishing meaningful patterns from the cacophony of data, laying the groundwork for predictive modeling and the development of machine learning technologies. Therefore, a deep understanding of statistical methods is indispensable for anyone aspiring to thrive in the data science arena.

9 Best Statistics Books for Data Science

This curated list of statistics books is designed to cater to a wide range of readers interested in data science. Each book was carefully selected based on its unique approach and relevance to the field, ensuring that there is something valuable for every learner.

Here is a list of the best statistics books for data science:

  • “Naked Statistics: Stripping the Dread from the Data” by Charles Wheelan
  • “Practical Statistics for Data Scientists: 50 Essential Concepts” by Peter Bruce and Andrew Bruce
  • “The Art of Statistics: How to Learn from Data” by David Spiegelhalter
  • “The Signal and the Noise: Why So Many Predictions Fail—but Some Don’t” by Nate Silver
  • “Statistics for Data Science” by James D. Miller
  • “Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks” by Will Kurt
  • “Think Stats: Probability and Statistics for Programmers” by Allen B. Downey
  • “Statistics Done Wrong: The Woefully Complete Guide” by Alex Reinhart
  • Head First Statistics” by Dawn Griffiths

Let us dive deeper into the detail of each book.

1. “Naked Statistics: Stripping the Dread from the Data” by Charles Wheelan

"Naked Statistics: Stripping the Dread from the Data" by Charles Wheelan

Charles Wheelan’s “Naked Statistics” makes statistics accessible to everyone, stripping away the complexities and dread often associated with the subject. What sets this book apart is Wheelan’s ability to infuse humor and relatable examples into the discussion of various statistical concepts. It’s an ideal read for those who want to understand the essence of statistics without getting bogged down by mathematical formulas.

2. “Practical Statistics for Data Scientists: 50 Essential Concepts” by Peter Bruce and Andrew Bruce

"Practical Statistics for Data Scientists: 50 Essential Concepts" by Peter Bruce and Andrew Bruce

This book is a treasure trove for aspiring data scientists, presenting 50 essential statistical concepts with a focus on practical application. The Bruces use real-world examples to demonstrate how statistical methods apply to data science, differentiating it from more theoretical texts. Its hands-on approach helps readers grasp how to apply statistical principles to actual data science problems.

3. “The Art of Statistics: How to Learn from Data” by David Spiegelhalter

"The Art of Statistics: How to Learn from Data" by David Spiegelhalter

David Spiegelhalter’s book is a masterpiece that teaches readers how to extract meaningful insights from data. It stands out for its emphasis on understanding the ‘why’ behind statistical methods, rather than just the ‘how.’ This book is perfect for those looking to not just perform statistical analysis, but to understand the reasoning and intuition that underpin statistical decisions.

4. “The Signal and the Noise: Why So Many Predictions Fail—but Some Don’t” by Nate Silver

"The Signal and the Noise: Why So Many Predictions Fail—but Some Don’t” by Nate Silver

Nate Silver’s book is a fascinating exploration of the world of predictions, offering deep insights into how statistical principles can be used to forecast future events. It’s unique in its broad range of application areas, from politics to natural disasters, making it an engaging read for anyone interested in how statistics can be used to predict the unpredictable.

5. “Statistics for Data Science” by James D. Miller

"Statistics for Data Science" by James D. Miller

James D. Miller provides a concise yet comprehensive guide to statistics with a special focus on data science applications. What makes this book special is its balance between theory and practice, offering readers a solid foundation in statistical concepts along with insights on applying these principles to real-world data science challenges.

6. “Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks” by Will Kurt

"Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks" by Will Kurt

Will Kurt’s book takes a unique and engaging approach to teaching Bayesian statistics through fun and familiar examples. It’s an excellent choice for readers who want to dive into the world of Bayesian thinking without getting overwhelmed by technical jargon. The use of unconventional examples makes complex concepts more digestible and enjoyable to learn.

7. “Think Stats: Probability and Statistics for Programmers” by Allen B. Downey

"Think Stats: Probability and Statistics for Programmers" by Allen B. Downey

Allen B. Downey’s book is tailor-made for programmers looking to enhance their statistical knowledge. By leveraging programming to explain statistical concepts, this book offers a hands-on approach that sets it apart. It’s ideal for data scientists who prefer to learn by doing, offering code examples that readers can directly apply to their own projects.

8. “Statistics Done Wrong: The Woefully Complete Guide” by Alex Reinhart

"Statistics Done Wrong: The Woefully Complete Guide" by Alex Reinhart

Alex Reinhart’s book is a critical examination of the missteps often made in statistical analysis. It Focuses on statistical pitfalls to avoid, offering invaluable lessons. This book is a must-read for data scientists aiming to conduct rigorous and reliable statistical analysis.

9. “Head First Statistics” by Dawn Griffiths

"Head First Statistics" by Dawn Griffiths

Dawn Griffiths’ “Head First Statistics” adopts a visually rich format to make learning statistics an engaging and interactive experience. Its engaging format, with puzzles and vivid examples, offers a refreshing alternative to dry textbooks for readers. Its content suits visual learners particularly well.

Conclusion

Venturing into the realm of statistics within data science is an investment that promises to enhance your capabilities as a data scientist significantly. The books featured here provide a range of perspectives and approaches to mastering statistical concepts, ensuring there’s something for every learner. Whether starting fresh or seeking deeper knowledge, these choices will guide you to statistical expertise in data science.

If you wish to dive deeper into data science, you can learn more about it in our comprehensive course. Click on the link to explore our AI and ML Blackbelt+ program.

You can refer to our more articles here:

D
Deepsandhya Shukla

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