Data science is undoubtedly one of the hottest career choices right now and in this blog we will look at the 10 data science books that you useful to learn the subject. Companies (many of whom have data science departments) are hiring data scientists around the board. As data volume is exploding, it is a wonderful career opportunity today to become a data scientist. It is also a fantastic opportunity to hone your expertise if you are already a statistician and want to grow through the ranks to get into data science space. We will discuss and recommend useful books at beginner, intermediate and advanced data science level.
Data Science Books for Beginner Data Science Level
If you are just beginning your quest on data science, you can check all these books out:
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.
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.
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 about :
- 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
Data Science Books for 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 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.
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 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) 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.
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.
Data Science Books for 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 incorporates deep learning in your programs to move beyond classical statistics.
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 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.
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. Our advice is to enjoy a cup of coffee, sit down, and start reading it bit by bit.
How to become a data scientist in 2024?
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 our Data Science course which not only helps you to acquire the necessary skills but also makes you work on data science and data analytics projects and internship.
Reference Article source: Data Science Books