5 Books Every Data Analyst Should Read
All data analysts need to keep learning for them to be effective at their craft.
Every skill from the basic programming, databases, and statistical tools to algebra and AI can come in handy in the life of a data analyst.
An understanding of the business world and industries you wish to target services would not be a waste of time.
But which five important books are recommended for every data analyst?
1) An Introduction to Probability Theory and its Applications by William Feller
It’s a must to understand basic probability theory and be familiar with statistical analytical tools for your data science career.
Whether you are new to statistics or not, you will find William Feller’s Introduction to probability a useful guide.
Also, it’s one of the best you can get for hands-on resources in data science among others.
2) The data science handbook: Advice and Insights from 25 Amazing Data Scientists by Carl Shan
Sometimes getting motivation could be a good way to start but it’s up to the data scientists to do the work.
If you are already working as a data analyst then you need to be inspired to keep going and this is a good resource to have.
It’s recommended to help the readers learn strategies and lessons to become better data analysts.
3) Thinking with Data: How to Turn Information into Insights by Max Shron
Thinking with data is a systematic book that takes you through the step of getting data and the reasoning and work processes in data science.
A good way to learn data science is by approaching it from the angle of problem-solving.
I chose Thinking with data as one of the five books to have because gives an insight into the habit of a data scientist.
4) Python Machine Learning By Example: The easiest way to get into machine learning by Yuxi Liu
So you have to hone your data analyst skills and you need to have programming skills.
Python is the top most used programming language in data science and it’s time to create better machine learning. What a better way to learn Python machine learning than learning by example?
One of the top useful lessons you can learn from this book is having a step by step guide to building your data model.
At the end of reading Python Machine Learning By Example, you should be able to build your AI systems with how to work as a data analyst. You get great lessons that cover a lot of topics with plenty of examples.
Truly, Python Machine Learning By Example is one of the easiest ways to learn machine learning.
5) Business analytics – the science of data-driven decision making by U Dinesh Kumar
Every data analyst especially top business executives need to be up to speed with the business perspectives and wealth of knowledge that can be derived from extensive case studies in data analytics.
While this book has very good practical lessons from basic data science concepts to advanced concepts, it’s a great reference resource every data scientists need to have.
Five maybe a small number for all the books every data analyst should have but this should be a good one to start with. You may want to get a specialized book on database systems as well.
Although there are good books on databases that you can buy, free online tutorials on database systems can give you a head start on your journey as a data analyst.