Submit a blog to KDnuggets -- Top Blogs Win A Reward

Topics: AI | Data Science | Data Visualization | Deep Learning | Machine Learning | NLP | Python | R | Statistics

KDnuggets Home » News » 2021 » Apr » Tutorials, Overviews » Data Science Books You Should Start Reading in 2021 ( 21:n16 )

Gold BlogData Science Books You Should Start Reading in 2021

Check out this curated list of the best data science books for any level.

By Przemek Chojecki, CEO Contentyze

Data science is undoubtedly one of the hottest career choices right now. Companies (many of whom have data science departments) are hiring data scientists around the board. It is a considerable thing to become a data scientist. It is also a fantastic opportunity to hone your expertise if you are already a statistician and want to step through the ranks.

This article discusses the most popular data science books for any level.

Data Science Books you should start reading in 2021


Beginner Data Science Level

If you are just beginning your quest on data science, you can check all these books out:

Data Science from scratch book


In the Data Science from Scratch book, the concepts are presented to the unfamiliar learners in Data Science. You don’t even have to know something about Python to start. I will strongly recommend that you get started with this book.

Introduction to Machine Learning with Python


If you are willing to learn about machine learning from a novice level and eager to read more about it, this book entitled “Introduction to Machine Learning with Python” is an excellent pick. Keep in mind that it is not expected that you already know Python.

Data Science Job


Then, to be an adequately qualified Data Scientist, you can take a look at my book named Data Science Job: How to Become a Data Scientist that goes through a comprehensive step-by-step guide of the entire procedure.

Through my knowledge of collaborating for several organizations as a project manager, a data science analyst, or a CTO, I was able to see the process of recruiting data scientists and developing data science teams. This book will inform you:

  • what is necessary to secure your first job as a data scientist,
  • what skills you should learn,
  • what you should demonstrate during a job interview,

and much more.


Intermediate Data Science Level

If you’ve read 1 or 2 data science books, and if you’ve done a few data science assignments yourself and now you are used to dealing with data, here are books that will deepen your knowledge of data science.

Python for Data Analysis


Python for Data Analysis suggests the usage of NumPy and pandas. Also, Python for Data Analysis is a marvelous resource that knowledgeable data scientists may appreciate. It gives an appropriate tour of Python from describing how the language functions too.

Python Data Science handbook


The Python Data Science Handbook is a perfect guide to all the standard Python libraries. The Pandas library, the Scikit-Learn library, and the NumPy math library are strongly recommended.

This detailed workbook will provide data scientists and data miners with active methods for dealing with data. Data scientists will enjoy the inclusion of multiple illustrations, the concise clarification of the algorithms behind each process, and the tools available on the companion website. This is hands down, the only detailed, up-to-date resource for scientific computation in Python.

You’ll learn how to use the following:

  • Jupyter and iPython are frameworks in which Python can be used for data processing.
  • Numpy: requires the construction of a data array that is dense and effective for data processing in Python.
  • Pandas combine robust vectors with the DataFrame to analyze and retrieve labeled/columnar data in Python.
  • Matplotlib is a plotting library in Python that provides versatile functionality in plots and data visualizations.
  • Scikit-learn: a popular machine learning library for Python that offers too complicated machine learning algorithms with very efficient implementations.

Python Machine Learning book


Python Machine Learning is somewhere between the intermediate and advanced stages of Machine Learning. It would cater to all the individuals who are specialists in the area and others who are not. It starts with a gentle introduction to machine learning and deep learning and then moves to more advanced ways. A fantastic book!

Hands-on Machine Learning with Scikit-Learn and TensorFlow (2nd edition)


Hands-on Machine Learning with Scikit-Learn and TensorFlow (2nd edition) is a treasure trove to get more results! It is a book that discusses all basics (classification processes, dimensionality reduction) and even gets into neural networks and deep learning.

Python for Finance book


If you are into finance and data science, Python for Finance is essential reading. The book emphasizes utilizing such data science methods to evaluate capital markets, and several excellent examples can be found demonstrating this. It is an incredibly realistic product that would often cater to those who do not regularly work in finance.


Expert Data Science Level

For those who are a little more experience with Data Science, you’d be best off reading many science research articles instead of reading books. This is because the approach is more realistic and incorporate deep learning in your programs to move beyond classical statistics.

Deep Learning with Python


The Deep Learning with Python book was authored by one of the Keras library developers, one of Python’s most famous machine learning libraries. The book begins with a practical approach because you can learn several helpful techniques straight away. It is often incredibly realistic because you will adopt it right away to activities right after the read. This is an utter must-read in deep learning.

Deep Learning


Deep Learning is a fantastic reference for deep learning algorithms. It contains a limited coding volume and excellent insight into how one can solve machine learning issues. Frequently quoted by experts in the area.

Machine Learning: a Probabilistic Perspective


If you’re into mathematics, then you’ll love Machine Learning: a Probabilistic Perspective. It is a veritable tour of the mathematics behind the machine learning process.

No, I don’t suggest reading it all at once. My advice is to enjoy a cup of coffee, sit down, and start reading it bit by bit.


How to become a data scientist in 2021?

That’s it. Hope these books will help you become a better data scientist!

If you are searching for the first entry-level data science work and don’t know where to go, sign up for my Data Science Job course. Once you’re inside, I can help you become a junior data scientist directly answering your questions, within our learning group.

If you’ve enjoyed this text, check out my other posts about becoming a Data Scientist:

Join my tech newsletter
Data science, AI, machine learning

Bio: Przemek Chojecki is the CEO at Contentyze, the text editor 2.0, a PhD in maths, and a Forbes 30 under 30.

Original. Reposted with permission.


Sign Up

By subscribing you accept KDnuggets Privacy Policy