With exponential growth over the past years, the data science field has become very popular in the IT sector. Many businesses have started adopting data science techniques in order to derive meaningful information to make precise business decisions. Because of this data science has become an in-demand skill and one of the most highly paid careers in the tech industry.
In order to be a successful business data scientist, it is crucial to understand and know how to use complex algorithms to build models, manipulate different datasets found from various sources, and be able to analyze and present findings to non-technical audiences. With so many resources available one can use them to learn more about data science but nothing beats reading data science books.
Although there are many data science books that are available out there, it is important to look for books that provide in-depth information, are easy to understand, and are very informative. This will come in handy especially when it comes to understanding how and when to apply certain algorithms to your models. With that in mind, we have curated a list of some of the best data science books one must read in order to enhance your knowledge.
One of the foundations of data science is Statistics. It is one of the most important key concepts and many people who venture into data science have little to no knowledge of statistics. In order to be proficient in your skills as a data scientist, this data science book will help you understand the important concepts, taking you from a complete beginner to an intermediate level. This data science book also covers important topics like EDA, Data distribution, and sampling which will teach you how to yield high-quality datasets. The data science book come with code snippets which gives you an opportunity to practically try out the illustrated examples.
This data science book is poised to introduce any beginner to machine learning with Python. Most concepts are explained in a manner that a layman can understand. It also caters to individuals who have no prior knowledge of Python, this will help you learn the language while reading through the book. The data science book also contains practical examples and easy-to-follow code snippets that one can try. It takes a complete novice and guides them to an intermediate level.
For those who have started out in data science, this data science book is a go-to reference book used to understand the functionalities of Python libraries used in data science. You will have in-depth knowledge of how different libraries like Pandas, Numpy, Matplotlib, and Scikit-learn work. The various topics covered in this data science book will help you transform your skills in data science and elevate your knowledge.
As the name suggests this data science book is poised to teach the reader more about Python and how to utilize Python libraries for statistical and data analysis. This data science book explains each concept as simple as possible and provides examples of how to clean, manipulate and process datasets using Python. The book is one of the best data science books that shows how one can solve real-world data challenges and learn the latest versions of NumPy, Pandas, and IPython.
Although this data science book is for medium-level individuals, it provides an in-depth knowledge of both basic and advanced principles applied in data science. This data science book gives clear-cut explanations using real-world cases that one can face when working and analyzing data. This data science book is unique because it focuses on business challenges and delves deeper into deep learning and machine while answering the how and why questions.
This is one of the most popular data science books recommended for anyone starting out in their data science career. The topics in this data science book cover the theory behind machine learning algorithms. It also teaches how individuals can apply different techniques such as deep neural networks to models. Each component is explained step by step in order to get an understanding of how models work behind the scenes.
If you are interested in picking up a programming language apart from Python to learn data science, this data science book serves that purpose. R for data science book explains key concepts of statistics, how to work with real-life data and teaches you how to clean and transform datasets. Not only does this data science book help you to deal with raw and messy datasets, but it also provides you with the right knowledge of how to process and transform your data without consuming a lot of time. This data science book does cover all the bases if you are looking to venture into R programming.
This data science book takes you through the fundamentals of data science and explains it in simple terms. It covers most of the problems one can encounter when working with real-life data and how to tackle these challenges. This data science book contains different concepts like statistics and big data and it is also accompanied by code examples one can practice during their learning process. If you are a beginner, this data science book will teach you different domains available in the data science ecosystem like natural language processing, network analysis, and recommender systems.
Headfirst is one of the best data science books that anyone looking to learn data science should start with. Covering more in-depth statistics like mean, median, and standard deviation, and probability concepts like regression and correlation, this data science book will give you a head start in pivoting your career. It’s also a great refresher for anyone who has done science at school. One advantage that this data science book holds is that it provides real-life examples and solutions. It also comes with good graphical content to help you understand concepts more clearly.
This data science book provides the reader with insight when it comes to building and understanding the architecture of models. It delves deep into developing the reader's mind to think more from a business perspective. The data science chapters also teach how to ask the data questions and how best to extract meaningful information from your datasets. This data science book is recommended if you are looking to develop an intuitive understanding when it comes to working with data sets and models in data science.
Every data science book published cannot cover all the information needed for career growth in one go, it is therefore essential to be able to read various books with some of them listed above in order to get a stable understanding of the data science industry. Data science is a new, fresh niche in the IT industry that has the potential to grow even more. With that in mind, more knowledge still remains undiscovered and yet to be explored with time. This in turn implies that for one to be constantly and effectively relevant in this field, one must always keep afloat with the latest developments in the data science field.
i. Can I learn data science by myself?
Ans. Yes, one can learn data science by themselves. There are plenty of self-paced data science courses available online from sites like Coursera and Udacity.
ii. How do I start data science?
Ans. The best way to start in data science is by learning Python programming. This will help you make your journey flawless as most popular data science libraries are written in python language.
iii. How can I learn data science from basic to advance?
Ans. The best way to move from basic to advanced in data science is by building meaningful projects. You can apply what you are currently learning to real-world problems in order to gain more expertise.
iv. Is data science a good career?
Ans. Yes, data science is indeed a good career. Data science skills are in high demand, have plenty of growth opportunities, and you can earn a good salary.
v. Is data science and AI the same?
Ans. No, data science and artificial intelligence are not the same. Data science focuses on analyzing, pre-processing, and training predictive models, while A.I is the implementation of predictive models.
Thulie is a technical writer, data scientist, and python programmer. She mostly focuses on writing beginner-friendly tech articles. When not writing, Thulie likes to watch anime series and gaming.