Must Read Data Science Books
This is the digital age and data is the soul of everything today. We just make billions of terabytes of data every day, and this unstructured data when processed comes up into a meaningful representation which is where data science and modelling comes into play. By gaining powerful insights we can do wonders. As an example, we can efficiently determine the shipping routes, digital ad placements to target specific audiences, improve the businesses by studying the market and detecting cyber-attacks. Data science and the positions which leverage data science are in high demand, making it a solid career of choice.
A Data Science Career is waiting for you if you are ready to juggle with the data, have sharp critical thinking skills, are a problem solver, apply mathematics and other hard skills to analyse large data sets, then data science career is here to welcome you with arms open. Even if you are not into this, the insights will supplement your knowledge in numerous roles within an organization.
The books below are here to help you if you are headed or heading into a data science career. We will discuss the best ones out there in the market, go through them and you may find these data below to level up your data skills!
Data Science for Beginners, by Andrew Park
This is a four-book data set for beginners. This provides a solid understanding of Python, data analysis, and machine learning. Step-by-step instructions and tutorials on leveraging the Python programming language to create neural networks, manipulate data and master the basics.
Python data science handbook by O’Reilly
Written in the best possible way as this is a comprehensive book for data science using python. If you want to become an expert in python and want to implement data science, this book is good to go with. It has wonderful codes. Covers all the important libraries like NumPy panda, math. lib. & Panda. The best part Is exploratory data analysis. You will like the smooth transition from Exploratory data analysis to machine learning. The machine learning chapter covers both the practical implementations of libraries and how they work. Advanced libraries like python graphic libraries are present as well. Many visual representations of the projects by graphs make this book more interesting.
Think Stats 2e by Allen B Downey
It covers all the basics of statistics. It uses data sets from the national institute for health. This book is enough to cover e. Modelling Distribution … Practicing things concerning statistics are covered here. With important and exhaustive topics around like PMS (Probability Mass function), Percentile, CDS. It also has a lot of examples for Correlation and causation, Nonlinear relationships, Covariance and more. A separate chapter for Hypothesis testing makes it more interesting. More examples and easy language make it a book to have for data scientists.
Essential Math for Data Science: Calculus, Statistics, Probability Theory, and Linear Algebra, by Hadrien Jean
We can’t understand data science without wholly understanding mathematics at the core, and it generally is expected to have a solid foundation in data science. This book strives to explain the mathematics at the core of data science, machine learning, and deep learning. Whether you’re a data scientist who finds mathematical background challenging or a developer keen on adding data analysis to your toolkit, this book is your one-stop solution for that. The book also demonstrates how Python and Jupyter can be leveraged to plot data and visualize space transformations and covers important machine learning libraries such as TensorFlow and Keras.
Deep learning An MIT press book by Ian Goodfellow, Yoshua Bengio and Aaron Courville
It is provided by MIT for free and is also available online on deep learning.org. Deep learning is an important aspect of data science. Mathematics becomes a bit challenging with It becomes a bit challenging if you are lacking in mathematics and this book does help you
A Common-Sense Guide to Data structures and Algorithms: Level Up Your Core Programming Skills (2nd Edition), by Jay Wengrow
This is a practical hands-on guide to data structures and algorithms, going way beyond the theory will help you vastly improve your programming skills. This teaches you how to use hash tables, trees, and graphs. To improve the efficacy of this teaching it has also been backed by practical exercises in each chapter so that you can practice what you have learned and move ahead. The algorithms and data structures are always presented as theoretical concepts but this book goes well beyond that and focuses on mastering these concepts so that you can apply them in real-time and run the code faster and more efficiently.
R for Data Science by Garrett Grolemund & Hadley Wickham
Comprehensive guide to doing data science with R. It has a special focus on data visualisation, data pre-processing and data manipulation modelling. It also has projects to give it a more practical touch. R language is strong for statistical figures and modelling. This might not find a place at par with Python but this is a work in progress. Learning data science with R has the benefits of getting a good hand in statistics. This book has justice to the language and the subject of Data Science.
Smarter Data Science: Succeeding with Enterprise-Grade Data and AI Projects, by Neal Fishman, Cole Stryker, and Grady Booch
Creating an impact on the organisation is what data scientists are expected to do. In any business environment, data science is often pushed aside and doesn’t always make its way or presence felt. The Smarter Data Science book addresses this shortcoming by exploring why some of the data science projects fail at the enterprise level and ways to fix them. This is designed to help the directors, managers, IT professionals and analysts to scale their data science programs so they’re predictable, repeatable, and finally benefit the entire organisation. This book will teach you how you can create valuably data initiatives and effectively get everyone on board at your organisation.
Data Science for Business by Foster Provost & Tom Fawcett
This is a must-read for business professions who are leaning towards and respect the jargon of ‘Data is the new gold. This book touches upon the aspect of how to achieve a competitive advantage and leverage the same in Businesses.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition by Aurelien Geron.
So, you have read till here and we have kept the best at the last. A reward, sort of! This is like the bible of data science. Whatever you start with, the basics of data science must be so strong that you can easily build up new technologies on your strong base. And! This book provides you with the same. Going through the content you will get to know it has all of the machine learning, the deep learning concepts. It also focuses on end to end projects and has examples as well. A machine learning algorithm is a must and this is provided vividly in the book. The most important thing about this book is they discuss every part and break the concepts into smaller blocks for a better understanding of the concepts. Understanding the lifecycle of the project is an equally important thing as getting your basics strong and this book helps you in that direction. The path which is given over here is in line with all the modules which are designed, helping you understand the life cycle the best way possible.
As it is said ‘Books are gifts that you open again and again, the books listed here will be of great value if the reader can derive them. Nothing can replace these books and they have an important role to play. Reading the best Data Science books is a smarter way to get acquainted with the subject or sharpen your skills.
Data science is a vast subject and it deserves all the focus created on emerging jobs in the market. Data is the new gold and so those working in these industries will be pioneers in bringing out the future of data science. Keep on learning and keep on growing.
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