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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

30 Best Data Science Books to Read in 2025

Swati Sharma
Swati Sharma Last Updated : 03 Dec, 2024
15 min read

Data science is pervasive in today’s economy, with companies leveraging data at every stage for maximum efficiency. Understanding data preparation, big data’s significance, and automation processes contribute to data science’s future. Individuals must grasp fundamental algorithms and tools to evaluate data, comprehend trends, and make informed decisions. Even without prior background, the recommended data science books can guide beginners on their learning journey. Advancing skills and employing sophisticated algorithms in real-life scenarios are crucial for professionals too. This article lists 30 must-read data science books for 2025, covering topics such as mathematics, probability, statistical learning, programming, and machine learning to understand the discipline comprehensively.

Is it Possible to Self Learn Data Science?

Data science can be learned independently, yes. Because of the variety of internet resources, including free and paid courses, tutorials, books, and blogs, anyone with the discipline and commitment may master data science abilities. However, the method needs significant commitment because learning data science on your own may be challenging and time-consuming.

To start learning data science on your own, build a strong foundation in the principles of programming, statistical concepts, and data manipulation. Many online resources and platform courses, such as Analytics Vidhya and Coursera, can help you with this.

The key is to practice your skills by working on projects and challenges from the real world while being persistent, patient, and consistent in your learning. You can acquire mentorship, counsel, and support by joining online communities and attending data science-related events and meetings. Once you’ve established a solid foundation, you can look into more complicated topics like machine learning, deep learning, and data visualisation.

Data science jobs are one of the most lucrative and well-liked ones, and it’s expected to remain cutting-edge and challenging for another ten years or more.

Top 10 Books Data Science Beginners Should Read

I have always had an inclination towards video tutorials and lectures when learning something on my own via the internet. Like many of you, I discovered that it was easier to understand and avoid the discomfort of reading the available books. Until recently, I generally shared this opinion until I came across authors or publishers who removed the ” boring ” element from topic books and made them far more fascinating.

All of this started when one of my highly intelligent friends suggested that I read books because they provide more information and aid in developing reading and comprehension, two abilities that are crucial for everyone. I was first apprehensive about doing that unless he specified a few writers and publishers whose books are highly engaging and interactive. I was curious, “Does something like this actually exist?” So I gave it a shot to alleviate my doubts and found a new world of amazing novels I could read for hours.

I’m sharing with you the books and publishers whose works will cause you to think twice about giving up reading completely. There is nothing like opening your mind to a world of knowledge condensed into a few hundred pages. There is a magic and allure to books that I have never found in any other learning medium.

Data Science for Beginners, by Andrew Park

This data science handbook offers a strong foundational grasp of Python, data analysis, and machine learning for those who are completely new to the field. Each book offers tutorials and step-by-step instructions on how to use the well-liked Python programming language to build neural networks, interact with data, and learn the fundamentals.

Click here for the link to the Book

Data Science for Dummies (2nd Edition), by Lillian Pierson

Data Science for Dummies is a terrific starting point for those new to the topic. Lillian Pierson’s book covers the fundamentals of data science, including MPP platforms, Spark, machine learning, NoSQL, Hadoop, big data analytics, MapReduce, and artificial intelligence. Given that its target audience is made up of IT professionals and technology students, the term may be a little misleading. Instead of being a practical instruction manual, it provides a thorough review of data science that simplifies the complicated subject.

Click here for the link to the Book

Introduction to Probability

This is an introductory book that covers fundamental topics in probability. This book by J. Laurie Snell and Charles Miller Grinstead is a thorough text created with college graduates in mind. You may be asking why I said that. It’s because I want to emphasise that the best way to begin studying a subject is with a book designed for students who have never studied it before.

Click here for the link to the Book

R for Data Science by Hadley Wickham & Garrett Grolemund

The target audience for this book is anyone interested in or enthusiastic about using the R programming language. You should read this book if you’re thinking about picking up a new language to use for data science tasks or doing something else interesting or unusual in the field of data science. Everything will be explained to you in the books. Absolutely worth a look.

Click here for the link to the Book

Data Science from Scratch by Joel Grus

Beginning with a crash course on Python, the book takes you on to topics like data visualisation, probability, hypothesis testing, linear algebra, statistics, and many other data-related topics, along with machine learning, neural networks, recommender systems, network analysis, and other related topics. It’s a complete product. Therefore, you should read it.

Click here for the link to the Book

Probability: For the Enthusiastic Beginner

This book by David Morin is an excellent text for beginners. While it was intended for college students, everyone who wants to master probability from scratch will value the writing style. Combinatorics, the law of big numbers, the central limit theorem, the laws of probability, Bayes’ theorem, expectation value, variance, probability density, common distributions, correlation, and regression are all discussed.

Click here for the link to the Book

Build a Career in Data Science, by Emily Robinson and Jacqueline Nolis

It is not the same as preparing for a job to comprehend the foundational mathematics, theories, and technologies that makeup data science. ‘Build a Career in Data Science’ is more of a career manual than a typical book on data science, as the title suggests. The writers aimed to close the knowledge gap between college and getting your first job (or advancing in your current data science career). The lifecycle of a typical data science project, how to adjust to business needs, how to get ready for a management position, and even advice on handling challenging stakeholders are all covered in this book.

Click here for link to the Book

Naked Statistics: Stripping the Dread from Data (January 2014)

A good book by Charles Wheelan for laypersons on data and statistics. This book is for you if you want to learn data science but it’s been a while since your first math course. Ideally, it will assist you in gaining confidence and intuition regarding the practical applications of statistics.

Click here for link to the Book

Introduction to Machine Learning with Python: A Guide for Data Scientists

Knowledge of Machine Learning is critical for a data scientist. This book by Andreas C. Müller and Sarah Guido helps you cover the basics of Machine Learning. If you practice with the book for a substantial time, you can build machine learning models on your own. This book has all the examples with Python, but even if you do not have prior knowledge of Python programming language, you will be able to learn it through this book that very well serves as a python data science handbook. This book is for beginners to understand the basics of ML and Python.

Click here for link to the Book

Practical Statistics for Data Scientists

If you’re embarking on your data science journey, this book offers a thorough overview of essential concepts, providing a solid foundation for learning. It covers a wide range of topics, including randomization, sampling, distribution, and sample bias, without overwhelming the reader with unnecessary details. Each concept is explained clearly, accompanied by relevant examples that demonstrate their application in data science. Additionally, the book provides an overview of machine learning models, making it a valuable resource for beginners in the field. Whether you’re just starting out or looking to deepen your understanding of data science, this book is a must-read.

Click here for link to the Book

Top 20 Data Science books for Data Science Professionals

Smarter Data Science: Succeeding with Enterprise-Grade Data and AI Projects, by Neal Fishman, Cole Stryker, and Grady Booch

Data science is too frequently forced into a corner in the corporate world and doesn’t always show up when it’s most required. Even the smartest and most skilled data scientists won’t advance very far in their careers if they can’t have an effect on the rest of the company. These flaws are addressed in the book Smarter Data Science by examining the causes of data science projects’ frequent failures at the business level and suggesting solutions.

This book on data science is intended to assist directors, managers, IT specialists, and analysts in scaling their data science initiatives efficiently so that they are foreseeable, repeatable, and eventually advantageous to the entire enterprise. You’ll discover how to develop meaningful data science programmes and successfully win over everyone in your organisation.

Click here for the link to the Book

Essential Math for Data Science: Calculus, Statistics, Probability Theory, and Linear Algebra, by Hadrien Jean

While it is possible to enter the field of data science without having a thorough understanding of mathematics at its root, a data scientist who is truly effective and diverse should have a strong background in mathematics. Hadrien Jean’s Essential Math for Data Science aims to clarify the mathematics underpinning deep learning, machine learning, and data science. This book will assist you in developing mathematical fluency to increase your data science capabilities, whether you’re a data scientist without a background in mathematics or a developer looking to add data analysis to your arsenal.

The ‘Essential Math for Data Science book also discusses machine learning frameworks like TensorFlow and Keras and shows how Python and Jupyter may be used for plotting data and visualising space transformations.

Click here for the link to the Book

Storytelling with Data: A Data Visualization Guide for Business Professionals

Storytelling with Data is a book written by Cole Nussbaumer Knaflic. This book discusses the fundamentals of effective data visualisation and communication. Most of this book’s lessons are theoretical, but it includes several practical examples you may use in your next graph or presentation immediately.

This book also teaches the reader how to dig beyond standard tools to get to the essence of their data. It also discusses the topic of using your data to create a captivating and informative narrative. This book can be a compelling read for those interested in data science for business.

Click here for the link to the Book

The Hundred-Page Machine Learning Book

This book by Andriy Burkov is amazing. I struggled to find a book that could quickly convey challenging subjects and equations after reading many books that attempted to teach machine learning from numerous approaches and perspectives until Andriy Burkov managed to do it in roughly 100 pages. It is elegantly written, simple to comprehend, and has received the support of influential thinkers like Peter Norvig. Must I say more? Every data scientist, regardless of experience level, needs to read this book.

Click here for the link to the Book

Machine Learning

Tom Mitchell’s book on machine learning was the go-to resource for understanding the mathematics underlying various techniques and algorithms before all the hype. Before beginning, I’d advise brushing up on your math. Yet, you don’t need prior knowledge of AI or statistics to comprehend these ideas. It is absolutely worth adding to your collection.

Click here for the link to the Book

Deep Learning

What a wonderful group of writers: Ian Goodfellow, Yoshua Bengio, and Aaron Courville! The greatest resource for novices is generally agreed to be the book “Deep Learning.” It is organised into Deep Learning Research, Contemporary Practical Deep Learning Frameworks, and Applied Math and Machine Learning Fundamentals. It is currently the deep learning community’s most frequently mentioned book. This will be your buddy anytime you begin your Deep Learning trip.

Click here for the link to the Book

Statistics in Plain English

Timothy C. Urdan has developed a book for complete beginners that is wonderfully written and engaging. The explanations and writing style live up to the subtitle “Statistics in Simple English.” It’s so brilliant that you could recommend it to any non-technical person, and they would get the hang of these topics; It is that good!

Click here for the link to the Book

Data Science and Big Data Analytics

EMC education service has published a book titled Data Science and Big Data Analytics. One of the top data science books available on Amazon, it covers the range of techniques, approaches, and equipment data scientists employ. The book focuses on principles, concepts, and real-world examples. It applies to any industry, technological setting, and educational process. It supports and explains concepts with examples that readers can replicate using open-source software.

Click here for the link to the Book

Head First Statistics

Dawn Griffiths is the author of the book Head First Statistics. The author makes this often dull subject come to life by teaching you everything you need to know about statistics through readings packed with riddles, narratives, quizzes, and real-life illustrations. You can learn statistics from this book and utilize them to comprehend and support important issues. The book also covers the use of graphs and charts to visually demonstrate data. Last but not least, the book demonstrates how to compute probability, expectation, etc.

Click here for the link to the Book

Think Stats: Probability and Statistics for Programmers

This book by Allen B. Downey is at the top of most lists of books about data science. You can access resources like data files, codes, solutions, etc. Those familiar with Python’s fundamentals will find it extremely helpful. Examples from the real world are used to illustrate the language.

Click here for the link to the second edition of the book
Click here for a PDF of the first edition of the Book

Python for Data Analysis

Python is yet another popular programming language in data analytics. Moreover, data science relies on analytics. So, this book by Wes McKinney serves as a comprehensive introduction to data science for those learning the fundamentals of Data Analytics using Python. The book maintains a fast-paced yet simple style. It brilliantly organizes and arranges content for readers, offering a glimpse into the world of data scientists and analysts and their work types.

Click here for link to the Book

Hands-On Machine Learning

Aurélien Géron is the author of the Data Science book Hands-On Machine Learning. You can learn the theories, methods and machine learning algorithms for creating intelligent systems from this book. Also, you’ll master a variety of methods, working your way up to deep neural networks from simple linear regression. The only prerequisite is programming experience, and each chapter of this book helps you put what you’ve learned into practise.

Click here for the link to the Book

The Master Algorithm

If you’re looking for a technical book on AI, the Master Algorithm is definitely not it.  Instead, it is a superb book on how machine learning changes business, politics, science, and even warfare. It is a smart and stimulating book about where AI is at the moment and where it might lead the human race in the future. Will there ever be one algorithm (also known as “The Master Algorithm”) that can extract all knowledge from data? Come along with Pedro Domingos on his quest.

Click here for the link to the Book

Artificial Intelligence: A Modern Approach

This book, written by Stuart Russell and Peter Norvig, is the leading book in Artificial Intelligence. More than 1300 universities across more than 100 countries mention or cite this book. Given the authors’ backgrounds, the book’s 1100 pages are hardly unexpected. It can be regarded as the holy book of artificial intelligence because it covers the entire spectrum of AI components, including speech recognition, autonomous driving, machine translation, and computer vision.

Click here for the link to the Book

Artificial Intelligence for Humans

What fundamental algorithms are at the heart of artificial intelligence? The 222 pages of this book by Jeff Heaton include much technical information about that. This is the first book in a series on artificial intelligence approaches (dimensionality, distance metrics, clustering, error calculation, hill climbing, Nelder Mead, and linear regression). Moreover, there is an accompanying website with examples from the book and a GitHub repository containing the code.

Click here for the link to the Book

Natural Language Processing with Python

Steven Bird, Ewan Klein, and Edward Loper wrote this book in the collection, following the ‘learn-by-doing’ philosophy. You will learn Python ideas that you otherwise wouldn’t have and use the NLTK package to traverse the NLP world (Natural Language Toolkit).

Click here for the link to the Book

Foundations of Statistical Natural Language Processing

This text, which was published nearly two decades ago, is still a great introduction to natural language processing. It contains a fairly thorough overview of the more general NLP subtopics, including Probabilistic Parsing, Parts-of-Speech Tagging, and Text Categorization, among other things. The writers have given a thorough explanation of the language and mathematical underpinnings. Remember that this book by Christopher Manning and Hinrich Schutze is fairly comprehensive.

Click here for the link to the Book

Speech and Language Processing

This book strongly emphasizes real-world applications and scientific evaluation of natural language and speech. I chose to include this book so that we could look into speech recognition in addition to text and broaden our views. And why shouldn’t we? It’s a field of study that is growing at the moment, with numerous applications appearing every day. Jurafsky and Martin wrote this comprehensive book on computational linguistics and natural language processing; it comes straight from the masters.

Click here for the link to the Book

Business Analytics- The Science of Data-driven Decision Making

This fantastic, in-depth book provides comprehensive information by outlining both the theory and practical applications. The author takes a sophisticated approach to the subjects and gives several case studies that are simple to follow.The book provides all the information needed to begin data science, covering economics, statistics, and finance. It reflects extensive effort and experience, evident in the presentation of insights.

It effectively combines low-level and high-level concepts and contains statistical and analytical tools and machine-learning approaches. Towards the book’s end, you will also discover information regarding scholastic models and six sigma.

Click here for the link to the Book

An Introduction to Probability Theory and its Applications

It is a comprehensive guide to the theory and practical applications of probability theory, as stated in the book’s summary. If you truly want to go into the field of probability, I suggest reading this one by William Feller. It’s a pretty thorough manual; therefore, a beginner might not enjoy it. You can get away with reading other probability books described above if you’re learning probability just for the purpose of entering the data science field.

Click here for the link to the Book

Frequently Asked Questions

Q1. Which is the best book for data science beginners?

A. There are several excellent books for beginners, but one highly recommended book is “Python for Data Analysis” by Wes McKinney. This book introduces data analysis techniques using the Python programming language and focuses on practical examples. It covers essential libraries like NumPy, pandas, and Matplotlib, providing a solid foundation for data manipulation, exploration, and visualization.

Q2. How to learn data science?

A. To learn data science, you can follow these steps:
1. Master math & stats: probability, linear algebra, hypothesis testing.
2. Learn Python or R for data science programming.
3. Use pandas, NumPy, scikit-learn for data manipulation.
4. Explore supervised & unsupervised machine learning.
5. Visualize data with Matplotlib or ggplot.
6. Hands-on: real-world projects, Kaggle competitions.
7. Stay updated: blogs, webinars, data science communities.
8. Continuous learning: online courses, books, resources.

Q3. What are some key topics covered in data science books?

A. Data science books cover a wide range of topics, including mathematics, statistics, programming languages (such as Python and R), data visualization, machine learning algorithms, predictive modeling, data mining, optimization techniques, and software engineering principles. These books provide comprehensive guidance for beginners and professionals alike.

Q4. How can data engineering skills complement data science expertise?

A. Data engineering skills play a crucial role in data science by providing the infrastructure and tools necessary to collect, store, and process data efficiently. Data engineers design and implement data pipelines, databases, and data warehouses that enable data scientists to access and analyze data effectively.

Q5. Why are datasets important in data science?

A. Datasets are crucial in data science as they serve as the foundation for analysis, modeling, and decision-making. High-quality datasets enable data scientists to train predictive models, identify patterns, and extract insights that drive business decisions and innovation.

Happy Reading!

I hope that these Data Science books bring more shine to your skillset. Keep Growing, Keep Reading, and Keep Flourishing. In addition to being one of the most lucrative and well-liked careers to date, data science will likely continue to be innovative and difficult for another ten years or more. There will be many opportunities for well-paying data science employment opportunities that offer space for growth. You may access AV’s training and certification options online from any location, and they combine the benefits of self-paced tutorials and live instructor-led classes. Start right away

Swati Sharma
Swati Sharma

With a career spanning 5 years, Swati has worked with top institutions to curate content for them.
A software engineer, data science enthusiast, and a stickler for grammar who always jumps at every chance to explore food, life, and mysterious theories. A Potterhead with a passion for Psychology, she is always up for learning new theories on life. She finds herself amused with books, podcasts, and vlogs that add value to life.

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Responses From Readers

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Terence C
Terence C

I like the diversity and extensive recommendation of data science books as it covers a lot of relevant and applicable topics pertaining to the field. Ranging from topics such as probability to machine learning shows a wide range of utility needed for problem solving through Data Science methodology. It also helps that some of the books are already brushing up many peoples undergraduate careers such as statistics and calculus!

123

Ramesh
Ramesh

I liked the way the names of the books are mentioned along with the topics that they cover. But if you can provide us the order of books like which one to go through first and which one next and then follower which other one, that would be really helpful for newbies.

123

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