5 Data Science, Coding, Data Analytics Books Recommendation for beginners

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I know there are lots of similar articles out there, but I want to share my own list of books as a self taught data analyst and coder that I have read and found useful for my coding (in Python) and data journey.

I am not going to write a review of each book, but I will try to give a short description of each book and why I think it is useful.

As a disclaimer this is my personal list, so it is not a comprehensive list of all the books out there, but it is a list of books that I have found useful for my own journey.

I am going to categorize the article in 3 sections to help you find the book you are looking for your journey.

First of all I need to say that nowadays probably the best way to learn coding is by doing it. There are lots of free courses online, and you can find a lot of tutorials on YouTube.

However when I started my journey having a book reference at least to get the grasp of the language was very useful. I am going to list the books I have read and found useful for Python. Let’s begin!!

I would define this book as bible of python beginners. The way the author explains the concepts is very easy to understand and the book is very well structured. It is a very good book to start with python.

I have to say that it was finally the book that made me understand the concept of classes and objects in python. I have read other books before and I was not able to understand it.

I think the way the author explains the concepts is very good. Not only that, I finally understood concepts like inheritance, polymorphism, etc. I think this book is a must read for beginners. Get the book.

1.3 40 Algorithms Every Programmer Should Know by Imran Ahmad

To be a decent developer it is important to know the most used algorithms in computer science. This book explains in a very simple language the main algorithms for solving classic computer science problems. It covers the most important algorithms like sorting, searching, graph algorithms, etc.

It is a very good book to start with algorithms. I think it is a must read for beginners. Not only that, the book introduces the concept of Big O notation, which is very important to understand the efficiency of algorithms.

Furthermore, the book explains the most important data structures like arrays, linked lists, stacks, queues, trees, etc., and provides also explanation of the key machine learning algorithms like linear regression, logistic regression, k-means, algorithms to create recommendation systems (at least the basic ones), etc. I think this book is a must read for beginners. Get the book.

2.1 Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python by Peter Bruce, Andrew Bruce, and Peter Gedeck

This book is a very good book to start with statistics as well!! I have to say that I had to do 2 exams in statitisics to get my master degree, I barely passed the exams, and I wish I could have this book at the time (around 15 years ago :-( ). It covers 50+ concepts in statistics like probability, hypothesis testing, linear regression, logistic regression, etc.

It is a very good book to start with statistics. I think it is a must read for beginners. Not only that, the book provides also a clear explanation of how to implement an Exploratory Data Analysis (EDA) in R and Python. It really made me love statistics and having practical examples to implement the concepts the book was like a Eureka moment for me. Get the book.

3.1 Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic

There a myriad of books out there about data visualization, but I think this book (the book is without code examples) is the one and only needed to start with data visualization. The book illustrates fundamentals of data visualization and how to communicate effectively with data using storytelling techniques.

It gives a very good explanation of the most important data visualization techniques like bar charts, line charts, scatter plots, etc. It also explains how to create effective data visualizations using color, shape, size, etc.

For me working in the marketing sector, this book was a game changer, the storytelling with data is an important part of my job especially if I need to get the buy in of my stakeholders that most of the time are not data savvy. Get the book.

4.1 Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney

The book is a very good book to start with data analysis using Python. It covers the most important libraries for data analysis like Pandas, NumPy, Matplotlib, etc. It is a very good book to start with data analysis. I think it is a must read for beginners.

Not only that, the book provides also a clear explanation of how to implement an Exploratory Data Analysis (EDA) in Python. It really made me love data analysis and having practical examples to implement the concepts the book was like a Eureka moment for me. Get the book.

This book provides provides practical uses cases (using PowerBI and Microsoft Azure) to implement AI in business intelligence. It covers the most important concepts in AI like supervised learning, unsupervised learning, reinforcement learning, etc.

It is a very good book to start with AI in business intelligence. The practical use cases are including improved forecasting, automated classification, and AI-powered recommendations. And you’ll learn how to draw insights from unstructured data sources like text, image, and voice audio files.

I think it is a must read for who is inolved in companies where business intelligence is a key part of the business and wants to improve the business intelligence using AI. Get the book.

5.1 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron

I would consider this book the bible of machine learning. It might be a bit intimidating at the beginning because provides detailed and technical explanations of each machine learning model also with the math formulas, but it is a very good book to start with machine learning.

It covers the most important machine learning models like linear regression, logistic regression, decision trees, random forests, support vector machines, neural networks, etc. It gives a very good explanation of neural networks and deep learning.

It also provides a very good explanation of how to implement machine learning models in Python using Scikit-Learn, Keras, and TensorFlow. It is a very good book to start with machine learning. I think it is a must read for beginners. Get the book.

5.2 Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples by Andrew P. McMahon

This book is maybe for more intermediate data scientists that want to learn how to implement machine learning models in production. It covers the most important concepts in machine learning engineering like data pipelines, model monitoring, model serving, etc.

It provides a very good explanation of how to implement machine learning models in production using Python. It is a very good book to start with machine learning engineering. I think it is a must read for who wants to learn how to implement machine learning models in production.

It illustrates basic understanding of key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions.

The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Get the book.

As mentioned in the introduction this is not an exhaustive list of books, but only a personal suggestion of books from me, self learner, not with software engineering background, that helped me to start with data science and machine learning.

I hope that this list will help you to start with data science and machine learning. If you have any questions or suggestions, please feel free to comment below. Thank you for reading and happy learning!