Back then, in the 2010s, web designers and programmers had fancy job titles and were paid pretty well salaries. But with the internet days, things have changed.
In this modern era of the 21st century, your browsing history is being recorded, your email data is stored, and Not surprisingly, I see my youtube watch history directly influencing my Instagram reel recommendations, leading me to spend more time scrolling. All this proves that now is the age of data science.
As we release tons of data onto the internet every day, we definitely need more Data scientists and ML engineers who can unlock the full potential of this data, making our lives even more seamless.
Collecting data and turning it into actionable decisions is something that today’s world demands. If you decide to fit in this growing demand and want to become a data professional, read on to find some of the best data science books.
Why Books when the Internet is a Flood of Resources?
It’s safe to say online resources are more effective than books, but this doesn’t always hold true because book readers are not extinct yet in this digital world.
Book reading and Online courses are two different worlds and not comparable. But we can count on some benefits of reading books over internet resources here.
Master the subject: When you are okay with general or practical information about a concept, then searching online is fine, but if you want to delve deeper into the subject, from its history to derivations, then the book flows well.
Get a real sense: Books are real! No matter how many virtual meetings you attend, you can never capture the charm of an in-person gathering. So, try holding a book and reading, you’ll sense the weight of the pages, the smell of ink, and notice your flowing fingertips over the words. Finally, you’ll love it.
Less distraction: I know you are on the internet to learn something, but a clickbait featuring your favorite TV show popped up before you, and you clicked on it. By the time you realized you were wasting your time, it was already late. This is not the case with books. You keep reading them until you are bored; no other way to distract you.
Accuracy: Books go through several fact-checks and editing tests before publishing, so these are more accurate and reliable.
Authority: Generally, books are written by expert professors and researchers in the field, while internet resources can be created by anyone. So, you can blindly trust many of the books.
Here is the list of the best data science books that will help you excel in your data science career.
Introduction to Probability
Pick this if you decide not to be a mediocre data scientist but rather to mark your name in this field because this Introduction to Probability book covers the detailed and advanced probability concepts that any data researcher needs.
Besides the concepts covered, the book also includes lots and lots of problems in probability with clean mathematics. Moreover, you’ll find detailed solutions for all the chapter-end exercises on the publisher’s website for free.
Anyway, I don’t recommend this book to someone beginning their career in data science or mathematics. You need a strong base in combinatorics or a good math foundation to teach yourself probability with this book.
But when you have a decent math foundation, then this is an excellent choice if you think of fully leveraging the fruits of learning probability in your Data Science career.
The Data Science Handbook
Data Science Handbook is meant to turn you into a unique data scientist with skills in data science, programming, and business understanding. With this book, you’ll get a crash course experience, but in a written format.
The book is written in plain English, which is well suited if you are new to data science.
In addition to covering classic ML concepts and algorithms, the book also touches on software engineering practices, computer memory, data structures, and databases.
Chapters on core technologies like Python, Big data prove this book to be on the technology side for data scientists and ML engineers solving real-world industry problems rather than targeting data researchers working on publishing their next journal.
Designing Data-Intensive Applications
This book is not just for data scientists or Analysts. It includes everything that a software engineer designing scalable real-world applications, a software architect exploring data-intensive applications, or a data engineer processing a high volume of data needs to make full use of data in modern applications.
Written by Martin Kleppmann, a researcher in distributed systems and security at the University of Cambridge.
The book covers Data models, Storage retrieval, Data encoding, Partitioning, Batch and Stream Processing, and many core concepts of building data-intensive modern applications.
If any of the following holds true for you, then this book is an ideal choice to scale your skills.
How to best apply the right tools to solve a given problem.
Want to build scalable data systems?
Optimize the performance of your data-intensive applications in production.
Enhance flexibility so that your apps can easily adapt to any new technology
Charles Wheelan shows us in Naked Statistics how informative data and the right statistical tools can help in building awesome recommender systems that suggest the next product you can add to your cart or accurate prediction systems that assist you in buying and selling stocks.
The book aims to train your mind to intuitively infer statistical analysis from the information you have. Topics like Descriptive statistics, Inference, Correlation, and Regression analysis in the text will help you achieve this.
The best part is the book Naked Statistics teaches you math like a story.
Bayesian Methods for Hackers
If you want to learn probabilistic programming from a Bayesian point of you, then this book is all you need. The term “Hackers” in the title might be misleading, so let’s consider hackers as individuals who like to explore and learn Bayesian complex approaches and methods.
Aurélien Géron, the author of this best data science book, teaches you how you can build an intelligent ML system using the two Python plug-and-play frameworks – Scikit-learn and TensorFlow.
This hands-on machine learning book shows you how you can build end-to-end machine learning systems, utilizing the full potential of sci-kit Learn while requiring minimal coding on your part. Also, you’ll get hands-on with TensorFlow training, building, and scaling neural net models.
It’s written in a friendly tone, and believe me, I never expected an ML book to be this easygoing, with fewer important math derivations and more interesting aspects of ML.
Deep Learning with Python
It’s common to find deep learning as a section or a chapter in many machine learning and data science books. But, you should note – both areas are vast subjects in their own way.
Then you are good to go with this big data book, written by Viktor Mayer-Schönberger, Internet Governance and Regulation professor in Oxford Internet Institute department at the University of Oxford.
The book begins with how industries, including the government, collect data on everything and how they use it. Then it moves on to discuss data privacy and the risks associated with it. Finally, it provides closure with the future possibilities and the limitations of big data.
Hands-On Data Analysis with Pandas
Anybody can import a library and call a function, but inventing insights from the raw data or showing you puzzling results in simple visuals is what sets data scientists apart. Not to mention, Pandas is the first tool you should know to perform such intuitive tasks.
Whether you are a novice or a skilled data wizard, this hands-on data analysis with Pandas book shows every single trick you need to explore, analyze, and manipulate data using Pandas. You’ll learn to summarize statistics in exploratory data analysis and find patterns through clear visualizations.
By working on end-chapter exercises, you’ll gradually develop skills to handle real-world data in your professional work. You can access all the files and codes in this book on GitHub.
Practical Data Science with Python
The author Nathan George starts this best practical data science book with Python programming and then takes you to the core data science concepts and codes them in Python. It walks you through every phase of data science, from data analysis to performance testing.
The Code implementations in the book are broken down into smaller and more digestible chunks, creating a conversational tone for you. More importantly, you can access every piece of code in this book on GitHub for free.
Pandas, SciPy, and sci-kit-learn are the major Python libraries and frameworks you’ll be using throughout the book.
R Programming for Data Science
After Python, R is gaining momentum in exploring advanced statistics of complex data. So, I’m here with another text recommendation if you want to step into Data Science using R.
No products found.
R programming for Data Science is officially available online for free. Trust me, open it either in Edge or your favorite PDF reader, and you’ll find absolutely no difference between its online copy and the glorious hardcover edition.
This book is not for you to learn data science or ML techniques. However, It’s solely written by Roger D. Peng, a professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, to equip you with R programming, a tool to handle any data source.
By the end of the book, you should be able to comfortably use R objects, R packages, functions, and regular expressions for data manipulation and analysis.
This is one of the best listicles on the internet to find some perfect books to take your data skills next level. Data Science is a vast domain. So I’ve included some specialized books in each area, like Machine learning, Python, Data analysis, and R programming, along with a few overall best data science books.
Next, Please explore these data science tools that should also help you become a better data scientist.
Srujana is a freelance tech writer with the four-year degree in Computer Science. Writing about various topics, including data science, cloud computing, development, programming, security, and many others comes naturally to her. She… read more
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