Data Science ebooks
Amazing Data Science Books That Will Boost Your Career in 2024
Data Science is in high demand with an estimated growth rate of 22% by 2030. It is a blend of math, statistics, programming, AI/ML, and domain knowledge. It works with large amounts of data to extract meaningful insights and solve real-world issues. To be competitive in this field, data professionals of all levels must regularly update their skills according to market needs.
The field is not limited to only one area or industry. The need for data scientists is across many industries, from healthcare to finance, retail to manufacturing. They are crucial in assisting businesses in leveraging data to enhance efficiency, growth, and innovation.
In this article, I have shared some of the best books to help you boost your career in 2024, covering topics from fundamentals to the latest techniques and applications.
these are my favorites too. They were incredibly helpful when I was a beginner. I’ve gained so much from them, and I believe they could be equally beneficial to you as well.
These books are written by experts and practitioners who have years of experience in this field. By reading these books, you will not only acquire concepts and skills but also get inspired by the stories and examples of successful data scientists.
Also check this out ↓
Here we go ^_^
The Top 10 Data Science books You Must Read in 2024
1. Python for Data Science Handbook
This book is recommended for beginners who want to learn concepts like data manipulation using pandas, visualization using matplotlib, machine learning, and web scraping, etc. you also get to know some libraries of Python, such as numpy, pandas, scikit-learn and many more, it is beautifully explained every point with proper guides to it to use them effectively for various tasks.
It provides tips and tricks for improving the performance and quality of a code, as well as some examples and exercises along with a jupyter notebook to help you practice and learn.
ebook link: Python Data Science Handbook — Jake VanderPlas
2. Data Analysis with Open Source Tools
It is very useful to learn how to analyze and model data with open-source tools. It covers topics such as graphical data exploration, scaling arguments, probability models, classical statistics, simulation, clustering, dimensionality reduction, and predictive analytics. with practical advice, and examples to help you apply the concepts and techniques to real-world problems.
It will teach intermediate to experienced programmers, how to work with data in a business environment. also emphasizes the importance of thinking about the results and the purpose of data analysis, rather than relying on tools to think for you.
ebook link: Data Analysis with Open Source Tools
3. Essential Math for Data Science
This book helps in learning the mathematical foundations, It teaches how to use Python code and libraries to explore topics such as calculus, linear algebra, probability, and statistics, and how they apply to techniques like linear regression, logistic regression, and neural networks.
The book provides clear explanations, supported by math diagrams and code samples, making complex topics easier to grasp.
It also provides practical insights into the state of data science and how to use those insights to maximize your career.
ebook link: Essential Math for Data Science
4. The Hundred-Page Machine Learning
This one is my favourite. It consists of a concise and elegant overview of the theory and practical concepts of machine learning. topics covered are supervised and unsupervised learning, support vector machines(SVM), neural networks, ensemble methods, gradient descent, cluster analysis, and dimensionality reduction. these are mainly designed for beginners.
This book is ideal for anyone who wants to learn the essentials of machine learning shortly and simply.
ebook link: The hundred-page machine learning book by Andriy Burkov’s
5. Storytelling with Data
Learn the art of data storytelling and visualization from this book. It covers the best principles and practices for effective storytelling by applying six key lessons, provides guidance on tools and processes of creating and delivering data stories, and highlights common pitfalls and mistakes, Additionally, it uses real-world examples from various industries to illustrate concepts also helps you understand the importance of design in data visualization.
This book is suitable for beginner and professional roles (such as data analysts) who want to improve their data storytelling skills in a visual way.
“There is a story in your data. But your tools don’t know what that story is. That’s where it takes you — the analyst or communicator of the information — to bring that story visually and contextually to life.”
ebook link: Storytelling-with-data-cole-Nussbaumer-knaflic
6. Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow
This book covers the fundamentals of machine learning, such as linear regression, classification, decision trees, ensemble learning, dimensionality reduction, and clustering, as well as more advanced topics like neural networks and deep learning, convolutional neural networks (CNN), recurrent neural networks (RNN), autoencoders, and generative models, among others.
Learn how to apply machine learning techniques to real-world problems and build intelligent systems, such as image recognition, NLP, and recommender systems, using TensorFlow’s high-level API and other tools.
The book follows a learning-by-doing approach, providing many code examples and Jupyter notebooks that can be downloaded from the author’s GitHub repository.
ebook link: Hands-on-Machine-Learning
7. Data Analysis and Machine Learning with Kaggle
The aim is for readers to teach how to win Kaggle competitions and build a successful career in data science. Kaggle is the largest platform for data science and machine learning enthusiasts, kaggle also offers short courses on various topics, data analysis, machine learning, feature engineering, data visualization, and AI.
This book also covers various aspects of Kaggle, such as its history, features, categories, benefits, and limitations. It also provides some examples of successful competitions and prominent Kagglers who have used Kaggle to advance their careers and create their products.
I also recommend checking out this ebook to learn: What is Kaggle? and how to participate in competitions. Here.
Practical tips and tricks to prepare data, create notebooks, submitting solutions, and collaborate with other participants.
ebook link: Data Analysis and Machine Learning with Kaggle
8. Practical Statistics for Data Scientists
This book will introduce to you the 5o+ statistical concepts and methods used by data scientists. It covers the most important topics such as exploratory data analysis (also called EDA), sampling, hypothesis testing, regression, classification, and clustering,
It will provide you with practical guidance on how to use those statistical concepts and also code samples of Python and R for better understanding and application and concepts.
ebook link: Practical Statistics for Data Scientists
9. Pattern Recognition and Machine Learning
This book is a classic when it comes to retain an in-depth understanding of pattern recognition in machine learning. It employs graphical methods to describe probability distributions and it properly illustrates the structure of complex problems. You will also get to know the topics like Bayesian inference, linear models, kernel methods, graphical models, latent variable models, etc. Additionally, it provides numerous mathematical examples to simplify the methods.
This book is suitable for students and practitioners who have some experience in mathematics and computer science and want to dive more into pattern recognition and machine learning.
ebook link: Bishop-Pattern-Recognition-and-Machine-Learning
10. Deep Learning with Python
The book is an comprehensive guide to help you understand the theoretical concepts and practical applications of deep learning by using Python. uses Keras framework as a tool. It covers a variety of topics including Keras, Tensors, neural networks, convolutional neural networks (CNNs), RNN, generative models, NLP, and computer vision and discusses the limitations and future of deep learning
This beautifully illustrates the concepts and techniques of deep learning and with a Python code examples to easily execute on a local machine or in the cloud.and mainly intended for readers who have some basic understanding of Python and machine learning,
ebook link: deep learning with python
Another amazing book on deep learning by Chris Bishop: link : Deep learning — Foundations and Concepts
let’s finish this article with this quote
“One of the main challenges of data science is to find the right balance between complexity and simplicity. Complexity can lead to overfitting and poor generalization, while simplicity can lead to underfitting and poor performance.”
You can download more ebooks from here: ⬇️ https://github.com/aniketpotabatti/Data-Science-EBooks
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