Books I Recommend for Data Science
If you, like me, like to study from books and have resources that you can refer back to, then this article is for you. Here, I will share the books that I have read, used while studying, returned to and taken notes on many occasions, and had fun trying out examples.
I will update this article from time to time and add new books, so stay tuned :)
If you are ready, let’s get started.
Automate the Boring Stuff with Python
This book, which also includes Python basics, guides you on how to automate the tasks you do manually in your daily life. If you are new to coding and do not understand exactly how the projects you learn can come out of what you have learned, you can strengthen your foundation with the sample projects in this book and set sail for bigger jobs. There are various examples that you can learn from, from reading files to working with pdfs or sending e-mails. You can access the book for free from this link.
Python Data Science Handbook
In this book, you can learn how to use python for data science, the jupyter structure, libraries that form the basis of data science such as numpy and pandas. After showing how to manipulate data with pandas, the book teaches how to make visualizations for the analysis of this data and then machine learning. An enjoyable book that can be used as a reference book when you are just starting out.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
This book is the one that I enjoyed reading the most and tried the examples one by one. It takes you from machine learning to deep learning with tensorflow. The book is constantly updated so the examples do not become old. It contains very nice and accurate information about machine learning. Thanks to the examples in the book, you can get very familiar with both scikit-learn and keras & tensorflow libraries. I recommend you to follow the book in a practical way. It is a book where you can get your hands dirty.
Time Series Forecasting in Python
This book focuses on time series from beginning to end, and it is important that you have knowledge of Python and machine learning. Starting with statistical methods, it explains how to examine time series data, which tests to perform, and how to determine model parameters. It also shows how to model with deep learning, while also dedicating a section to Facebook’s open source library Prophet. The codes explained in each section are available collectively on Github, and there are also answers to the end-of-section questions. In the end-of-section questions, it gives a separate data set and asks you to try some of the operations done in that section yourself. You can get a lot of efficiency from your work when you follow the directions in the book.
Machine Learning Yearning Book By Andrew NG
This book explains in detail the points to be considered in a machine learning project and gives advice. It is theoretically very understandable and offers methods to be applied. You can access the book from this link.
Deep Learning
This book contains all the theoretical details of deep learning. It is a complete reference book on this subject. Some parts may seem a bit heavy, but as time passes and you progress in the sector, it starts to become more understandable. If you do not only use ready-made libraries but also want to improve yourself in theoretical subjects, this book is for you. You can access the book for free from this link.
BONUS
Bernard Marr’s book Big Data in Practical explains how 45 different companies use big data and make decisions based on big data. It talks about how management is done and the results obtained. It is a very good book that can be read and finished in one go.
