Data in the age of the internet is known to be the most precious asset for any organization. Using data, businesses may better understand their business models and make improvements that save time and money while ensuring their customers benefit from the changes.
As companies heavily rely on data, data science has emerged as one of the most promising fields in the tech industry. It involves analyzing large data sets and uncovering hidden patterns and trends that companies can leverage to make actionable decisions. To do so, companies hire data science professionals. As there is an immense need for data science professionals, many youngsters are interested in making their careers in it.
But the first question that comes to the mind of a new learner of data science is where to learn data science from. Well, the answer is plain and simple, books. Books are one of those mediums of learning which has an unprecedented potential to give benefits to its readers. The same goes for books about data science.
In this blog post, we shall inform you of some of the best data science books you can refer to start an incredible journey of learning data science. But before that, let us first briefly understand what data science is and its benefits.
So let’s begin.
Contents
What is Data Science?
It is the field of computer science that teaches you different methods of extracting data in order to find meaningful insights from it that are valuable for the business. It is quite a vast domain. This multidisciplinary domain blends techniques and practices from mathematics, artificial intelligence, statistics, and computer engineering, to analyze enormous amounts of data.
This analysis helps data scientists understand data in-depth. Also, it helps them to answer questions like what happened, why it happened, and what will happen in the future according to the data.
Every single modern organization has tons of data. In the internet age, we have devices that automatically collect data and store information for analysis purposes. Signup and payment portals are prime examples of how data is automatically captured for a better user experience.
Right now, every tech device present around us is creating data in vast quantities, and that too in a number of different formats, such as text, audio, video, and even in the form of images.
In order to make the generated data useful, data science is essential.
Benefits of Data Science
There is no doubt about data science being the revolutionary field that focuses on business growth. With the use of data science, we are able to segregate and specifically structure predictive data to exact valuable insights that will help your business.
The following are some remarkable benefits of data science that help every business enhance its operations:
- Improvement In Business Prediction
By using predictive analysis and data structuring, you may use your data to improve your business. When the predictive analysis is utilized to its fullest extent, it will assist in helping you make much clearer business decisions with the precision of choosing the best course of action for your company.
- Helps in Creating Plans
It becomes easy to understand all the aspects of an organization by analyzing data. After the data analysis, you can use the discovered patterns and trends to create strategic ideas and plans for the company’s growth.
- Company Makes Direct Goal-Based Actions Based on Trends
You can obtain a prescribed action map using data science, which aids in enhancing an organization’s performance across a number of areas. It does not work on a hypothesis; every result of data analysis comes out from the data provided by customers. As a result, data science also helps in increasing engagement with customers, which ultimately leads to an increase in profitability.
- Data Science Allows you to Identify Opportunities
When data scientists interact with the organization’s data, they get to question existing processes that lead to such type of data. This will help a data scientist to uncover additional areas of opportunities for your business where it can thrive and helps in increasing the company’s revenue.
- Designing Better Products
The main concern of any company is to develop its products in such a way that it attracts more customers. The products have to meet the customers’ requirements, which gives them maximum satisfaction. In order to find out what customers are looking for, companies have to look at their data to find the outline of the product their customers want from them.
- Mitigate Risks
It is possible to prevent fraud and ensure overall security with data science and analytics. It assists companies in identifying all possible threat anomalies and potential cyber-attacks that may threaten their performance and security.
- Better Campaigning
When businesses know what their customers are looking for, they are more effective in advertising campaigns. Otherwise, it may result in a waste of time as well as money when businesses run campaigns that do not generate any leads. When businesses have data about their customers, they are able to create better campaign ads.
10 Best Data Science Books for Beginners And Professionals
Let us now delve deeper into our list of the best data science books for beginners as well as experienced professionals.
1. Data Science From Scratch
By: Joel Grus
The first book on our list is Data Science from Scratch. It covers the concepts of libraries, frameworks, modules, and toolkits associated with data science in great detail. All these tools are great for getting a glimpse of what the world of data science is all about without actually diving deep into its algorithm and data models.
If you are someone who is good at mathematics and also has knowledge about programming skills, this book is for you. The author makes sure that you will be able to comprehend the core methods of data science by implementing mathematics and statistics, which you already know.
Also, it teaches about Python programming because the author of this book has used Python as the language to work with data science models. You will be collecting, exploring, cleaning, munging, and manipulating the data. The result of this process is utilized to create machine learning models like Naive Bayes, logistics regressions, decision trees, neural networks, k-nearest neighbors, and more.
All of this is present in this one book, and we recommend every data science enthusiast must read this once in their lifetime.
2. Designing Data-Intensive Applications
By: Martin Kleppmann
We know data holds the center stage of many challenges which businesses have to cross to keep themselves going. It is essential to resolve complex issues in the shortest time possible while maintaining scalability, consistency, maintainability, and efficiency in mind. Apart from this, finding out the suitable applications for your business is another place where you might find yourself confused.
Well, for all these problems, author Martin Kleppmann has come up with a comprehensive guide. With this book, you will be quickly navigating through the complex landscape of data science while examining its pros and cons for various technologies as well. `
This book also teaches how to implement data science models and use them effectively to extract important information from your data. Besides this, you will learn how to make informed decisions on the obtained results from data analysis. Quickly navigate the trade-offs for better consistency, finding the fault tolerance and learning about the complexity of the distributed systems on which the latest databases are created.
3. Data Science For Dummies
By: Lillian Pierson
There are plenty of data science jobs present right now, but only a few people have the required skills to get into the data science market. But with the help of this book, you can find yourself a perfect starting point to learn data science and acquire those high-paying jobs.
Here, in this book, you will learn methods used for managing large amounts of data sets while keeping yourself under hardware and software limitations. Also, this book showcases how to consistently create reports, merge a number of data resources, and more. At the end of this book, you will understand techniques useful for interpreting data so that even a layman can understand the working logic behind it.
The data science book for dummies covers all the fundamentals before it pushes you to work with relational databases along with unstructured data via visualization techniques which is helpful for you in terms of representing and summarizing data. You will also learn about supervised and unsupervised machine learning concepts that include regressions, model validation, and other forms of clustering techniques.
4. Practical Statistics For Data Scientists
By: Peter Bruce & Andrew Bruce
We know that statistics is the essential mathematical knowledge that one needs to have to master the field of data science. Yet, very few books talk about the need for statistics when describing concepts of data science.
In this book, Practical Statistics for Data Scientists, the authors take you on a journey where he explains various methods for applying statistics to data science models. They also tell you about the other side of the story and show how you should not always be dependent on statistics when finding out crucial insight from the data.
If you have a working knowledge of R programming along with exposure to statistics, reading this book will help in creating bridges in your knowledge gaps of data science learning.
Likewise, this book talks about principles of experimental design, which helps in finding definitive answers to the questions which arise when you are compiling data, how to use regression for the estimated outcome, and look for the anomalies present in your data.
Apart from that, with this data science book, you will learn how to perform random sampling in order to reduce bias and yield a much higher quality of datasets, even when the data you have is massive in nature.
5. Introduction to Machine Learning with Python: A Guide for Data Scientists
By: Andreas C. MÜller & Sarah Guido
Machine learning is considered to be an integral part of data science, and there is a lot of debate about whether machine learning is exclusive to large MNCs with massive budgets. But that’s not true at all; if you are someone who is a beginner in Python programming, having this book can easily make it possible for you to build your very own machine-learning solutions using Python. There is a lot of data present around us today, and with all this data, our imagination is the only thing stopping us from creating a machine-learning solution for our datasets.
Here, you will learn about all the necessary things that are going to help you build a successful machine-learning solution for your requirement using Python. Both the authors of this book are inclined to make readers focus more on the practical and data science-related aspects of machine learning algorithms rather than teaching you the math behind them.
This book covers the concepts of a pipeline for channel modeling, encapsulation of your workflow, methods that are required to work with text-based data, tex-specific processing techniques, and suggestions that will improve the working of machine learning in data science.
6. Business Data Science: Combining Machine Learning And Economics To Optimize, Automate, And Accelerate Business Decisions
By: Matt Taddy
The world of business analytics is changing each day as more and more data scientists are taking over. This book is the perfect combination of data science and the work that can be done using it in the business analytics field. It will guide you step-by-step on how to use machine learning to implement its best-in-class resources in your data science projects.
It doesn’t matter if you are a business owner or a student looking for ways to dive deep into data. You will find the information, insights, and tools present in this book to be valuable for your learning. Also, this learning will also help you or your business to exceed in the best way possible in this data-driven economy.
Besides all this, you will also be learning about building blocks of machine learning such as sparse regularization, out-of-sample validation, topic modeling, finding out where causation is more helpful than correlation, and solving data science problems via scripting in R programming language.
You can also read: Best Python Books
7. Python For Data Analysis
By: Wes McKinney
If you want to learn about all the crucial nuts and bolts that are required for manipulating, processing, cleaning, and crunching out data in Python, then this book is just made for you. It is also a practical and much more modern version of scientific computing in Python, which is tailored for data-intensive applications.
Here, you will learn about the specific parts of Python and its libraries which comes in handy in solving a broad set of data analysis problems. When reading this book, keep in mind that it is not written to expose any analytical methods via Python in the form of implementation language.
There are lots of case studies that showcase the practical ability of Python in solving data science problems. In addition to this, this book covers Python topics like IPython interactive shell, learning of basic and advanced level NumPy features, Getting started with the data analysis tools such as Pandas library, creating scatter plots, static or interactive visualizations, measuring data by points in time, fixed periods, or in intervals as well.
Lastly, this book also covers problem-solving in web analytics, social sciences, finance, and economics through detailed examples using Python as the programming language.
8. Big Data: A Revolution That Will Transform How We Live, Work, and Think
By: Viktor Mayer-Schönberger and Kenneth Cukier
Data science is the hottest technical skill you can learn right now, as it answers some of the most complicated questions in a business. This book is going to help you learn all the necessary skills that are required to get to the answers in the shortest time possible as well.
This book has also been nominated as Financial Times Book of the year. The reason is this is one of the first books that have ever talked about the concept of big data in business, along with its uses. The authors of this book are two of the leading personalities in the field of data science and industry.
Here, in this book, you will get to know how big data is influencing our daily lives and how it is implemented as well. On the other hand, this book does not shy away from explaining threats that are hidden in the use of big data technology, as it could inevitably end the privacy of a person as we know it.
The book is written in such a way that it is brilliantly clear, and even a person from a non-technical background can understand it with ease. Even though this book was written almost a decade ago, it is still considered to be a great way to introduce the importance of big data and data science to students.
9. Pattern Recognition And Machine Learning
By: Christopher M. Bishop
There is no doubt that pattern recognition came from the engineering field, while at the same time, machine learning was invented using concepts of computer science.
In this book, you will read about all the latest developments that have taken place over the last decade in both pattern recognition and machine learning. Also, you will see how these two fields intersect and work in data science solutions. This book is purely intended for Ph.D. students or undergraduates of advanced computer science.
If you are someone who has no prior knowledge of machine learning, steer clear from this book, as this one is not for you. You need to have an understanding of multivariate calculus along with basic linear algebra. This book provides a self-contained introduction to basic theories, but it is better to have their knowledge beforehand.
10. Data Science And Big Data Analytics: Discovering, Analyzing, Visualizing, And Presenting Data
By: EMC Education Services
The last book on our list is going to help you harness the power of data to find out hidden insights about your business. The book holds tons of information about activities, methods, and tools that a data scientist can use efficiently.
The content of this book focuses more on conceptual learning, principles, and practical applications which are applicable to any industry and any technological environment. The learnings of this book are supported with tons of examples that are easily replicable via open-source software.
Conclusion
So these were some of the most critical data science books that you can read to make yourself learn crucial concepts of data science and its working. We know there are a number of online courses available for data science. But nothing could beat the old-school method of learning from books. These books are written by well-known authors who have pretty extensive knowledge of the field of data science.
Make sure you first pick up the data science books that are beginner friendly and then slowly move to the more advanced data science books. It is vital to learn based on your skill level. Otherwise, it will be hard for you to continue learning as concepts become more complex.
We hope this article has helped you find the right book for you to start data science learning. If there’s any book you wish or believe to be present here, then let us know in the comment section below.
Till then, happy learning!
Frequently Asked Questions
1. What is data science?
It is the field of computers where data is analyzed to find out actionable insights. There are various tasks associated with data science. But the most common one is solving data analytics problems by determining the correct data sets and their variables. All this results in the reveal of great opportunities for organizations that help them make better customer decisions while also finding more profitable business models.
2. Can I learn data science from books?
To become a data scientist or to learn data science, books will be the best option for you. This is because books have the potential to grasp the reader’s attention and teach them some of the most important terms about the topic in an easy way. Moreover, you will learn at your own pace, which is impossible with online courses or master sessions.
3. Do I require coding skills to learn data science?
As of now, data science is considered to be one of the most popular career choices for those who are technically inclined with a strong knowledge of programming. Most of the data scientists you will see are people from the AI or ML industry who are strong in detecting patterns and working with complex computer-based algorithms to solve a problem. So Yes, you need to have a good amount of coding knowledge to learn data science.
4. Who can learn data science?
To learn data science, you don’t need a degree in the first place, and you can pick a book and start your journey. But the technical field of data science is not for those who are not good at problem-solving, mathematics, and programming. One needs to have a firm grip on the above-mentioned fields to be able to comprehend the topics of data science fully.
5. Which is the best data science book for beginners?
There are a number of data science books available in the market that claim to be great for beginners. But the one we have found to define its title for beginners truly is “Data Science from Scratch: First Principles with Python By Joel Grus.”
Sameeksha is a freelance content writer for more than half and a year. She has a hunger to explore and learn new things. She possesses a bachelor’s degree in Computer Science.
