Looking for the best Data Science Books? Data Science has emerged to become one of the most paid and highly reputed domains for professionals. As we see more and more companies adopting data science applications in their businesses, there is a surge in the requirement for skilled data science professionals. If you are considering making a move in this domain, or are a data science expert who wants to remain on top of things, here is a list of books for you to keep the ball rolling
- 1 Top 16 Rated Best Data Science Books To Read
- 2 Best Data Science Books For Novice
- 2.1 Introduction to Machine Learning with Python
- 2.2 Machine Learning For Dummies by John Paul Mueller and Luca Massaron
- 2.3 Practical Statistics for Data Scientists by Peter Bruce and Andrew Bruce
- 2.4 Introduction to Probability by Joseph K. Blitzstein and Jessica Hwang
- 2.5 Python for Data Analysis by Wes McKinney
- 2.6 Practical Statistics for Data Scientists
- 3 Best Data Science Books for Intermediate Level
- 4 Best Data Science Books for Advanced Level
- 5 Best Data Science Book for Data Mining
Top 16 Rated Best Data Science Books To Read
Before we get into the best books, let’s understand what is data science? Now that digitization is spreading its roots, the collection and storage of data depend on machines, making it an effortless task. However, fetching useful information from a bulk of data still needs a consummate scientist, who can decipher necessary information from data, which otherwise looks like a mess. This requirement has given rise to a field known as data science.
Steps To Become a Data Scientist
How to Become a Data Scientist is the biggest enigma that every aspiring Data Scientist faces. Currently, a lot of or almost all companies employ data scientists to make quicker and better decisions to accelerate the company’s progress. And you would be surprised to know that in the last eight years, the percent increase in the number of data scientists has touched a whopping 650 mark. Data scientists receive handsome paychecks across the world, and the need for them is predicted to increase in the future.
So, if you are someone who wants to pursue their career in data science or want to hone up their skill in the field, you must start with reading data science books. The books will introduce you to the concept of data science and upscale your ability if you are already a part of it.
The Difference Between Data Analytics And Data Science
While most people use both terms interchangeably, you need to know that there is a significant difference between Data Science and Data Analytics. As opposed to Data Analytics, which combines data to find nuggets of greatness, used to help reach an organization’s goals, Data Science connects information and data points to find relations that may turn out to be useful for the business. Therefore, there is also a significant difference between a data analyst and data scientist
Best Data Science Books For Novice
Introduction to Machine Learning with Python
This book is an ideal option for the people who want to kick start their career in Data Science and learn data science basics. With a friendly tone and perfect illustration of every concept, the book acts as a guiding force for a person who has no prior knowledge of data science, machine learning, and Python. After an in-depth study of the book, you would be able to build your own ML model without taking any pains. This is one of the best Python for data science books.
Machine Learning For Dummies by John Paul Mueller and Luca Massaron
Designed to deliver all the data science basics, this is one of the best books when it comes to data science for beginners. It will take the readers deep into theory and basic concepts of Machine Learning and introduces them to the programming languages and the other tools required to apply those concepts effectively
Practical Statistics for Data Scientists by Peter Bruce and Andrew Bruce
This book is ideal for absolute beginners. It covers a vast range of topics critical to the field of data science in an easy to understand language. You can learn a lot about statistics in data science and could cover in-depth on topics like randomization, distribution, sampling, etc. If you are starting from scratch, this book is for you.
Introduction to Probability by Joseph K. Blitzstein and Jessica Hwang
Next in line after statistics is probability. It holds immense importance in the field of data science and this book will introduce you to the concepts by taking examples from real-life problems. If you have studied basic probability in school, this book is a build upon it. If you are studying probability for the very first time, you just need to spend some extra time with it. This book covers core concepts and will help you build a strong foundation for data science.
Python for Data Analysis by Wes McKinney
Apart from Machine Learning, Python is also a popular programming language in Data Analytics. Also, data analytics is critical to data science. Hence this book is a complete guide for beginners in data science to learn the concepts of Data Analytics with Python. The book is fast-paced yet simple. You can expect to be building real applications within a week with the help of this book. It is amazingly structured and organized for the readers and gives a peek into the world of data analysts and data scientists, and the kind of work they indulge in their role.
Practical Statistics for Data Scientists
It is among one of the must-have data science books for beginners that imparts an overview of all the concepts that are necessary for excelling in data science. It covers all the topics and serves as a quick and easy reference for building ML models.
Best Data Science Books for Intermediate Level
Python Data Science Handbook by Jake VanderPlas
This book is a great recommendation for those who have covered the basics of Python and are ready to explore and work with Python libraries. Python Data Science Handbook is an in-depth guide into all standard Python libraries such as Pandas, Numpy, Matplotlib, Scikit-learn, and more.
R for Data Science by Hadley Wickham and Garret Grolemund
R is another popular programming language for Data Science applications. For those who have worked on Python, the next step is to implement data science applications on R as well. R for Data Science is the perfect book to pick up coding in R. It covers the concepts of data exploration, wrangling, programming, modeling, and communication.
Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David
This is a great book for those who want a deeper understanding of machine learning concepts and algorithms. It covers the foundation of Machine Learning, algorithms in ML, additional learning models, and advanced theory. This book provides a great reference for implementing machine learning algorithms yourself. An extensive theory behind algorithms helps enhance the understanding and application of the same.
Pattern Recognition and Machine Learning
You would be comfortable with the jargon used in the book if you have already read a few books on DS. If you are done with the basics of Data Science and want to take a deep dive into Machine Learning and mathematics, this book would be a great pick for you.
Best Data Science Books for Advanced Level
Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Deep learning is one of the hottest fields in machine learning.
Companies like Google, Facebook, and Amazon need highly skilled professionals with expertise in deep learning.
What is it that makes deep learning so powerful?
It automates one of the most difficult parts of machine learning, feature discovery.
Rather than spending hours of time manually engineering new features in creative ways, deep learning automates the process.
If you’re new to deep learning, this book is a must.
Even if you have some experience, those advanced deep learning practitioners will benefit as well.
This book is presented in an easy to read slide format with lots of bullets and pictures.
Here are some of the topics covered:
– Intro and explanation of the importance of deep learning
– Algorithms – backpropagation, convnets, recurrent neural nets
– Unsupervised deep learning
– Attention mechanisms
Practical Data Science with R
Want to know what businesses demand from data science, in that case, you must consider befriending this book. It has established itself as the best statistics book for data science. If you seriously want to be a data scientist, this book is your bible.
Data Science For Dummies by Lillian Pierson
MPP platforms, Spark, Machine Learning, NoSQL, Hadoop, Big Data Analytics, MapReduce, and Artificial Intelligence are everything you need to become a pro-Data Scientist. Data Science For Dummies explains all these concepts in a comprehensive manner and makes learning fun and effective.
Think Stats by Allen B. Downey
As a data scientist, it’s important that you have a solid grasp of probability and statistics.
Machine learning models are rooted in the fundamentals of probability theory.
You’ll frequently be asked basic probability and stats questions during interviews, so it doesn’t hurt to refresh yourself from time to time.
This book is geared towards programmers, so it takes more of an applied approach rather than conventional textbooks that focus on math and theory.
Sections are short and easy to read, so you’ll be able to quickly work through examples.
Some of the topics covered include:
– Descriptive statistics
– Cumulative distribution functions
– Continuous distributions
– Operation and distributions
– Hypothesis testing
Data Science For Dummies
Data Science For Dummies is a book written by Lillian Pierson.
This book is ideal for IT professionals and students who want a quick primer covering all areas of the expansive data science space.
The book covers topics like big data, data science, and data engineering, and how all of these areas are combined to offer great value. You will also learn about technologies, programming languages, and mathematical methods.
Best Data Science Book for Data Mining
Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeff Ullman
This is a great book developed from various Stanford courses on large scale data mining and network analysis.
The focus is on data-mining very large datasets.
This is important for implementing production level models at scale.
Large companies like Google receive hundreds of millions (or more) search queries per day, so they are especially interested in mining very large datasets.
Some topics covered in this book include:
– Mining data streams
– Link analysis
– Recommendation systems
– Mining social-network graphs
– Dimensionality reduction
– Large-scale machine learning
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Read also: Top Best Python Books 2020
Last update on 2020-09-25 / Affiliate links / Images from Amazon Product Advertising API