3 Best Books for Beginner Data Scientists
Improve your data analysis skills by getting these three key books
The internet is a treasure-trove of information on a variety of topics. Whether you want to learn guitar through Youtube videos or how to change a tire when you are stuck on the side of the road, the internet allows us to learn skills faster and easier than ever before.
I am a big supporter of using the internet to learn and improve your data analytics skills. There are loads of resources on personal blogs, Youtube, and my favorite site: Towards Data Science! However, I find that books are still an extremely useful medium for learning these skills.
Online resources are fragmented — written from different authors, expecting various levels of previous experience, and contain slight differences between them. This can make it difficult to make connections between these resources when you are first trying to learn analytics. That is why I think books are a great additional resource to use in your education.
I have compiled a list of three of my favorite books that I think provide a great foundation in data analytics. While this list is by no means exhaustive, I encourage you to take a look!
For Those Who Know How to Code:
Python for Data Analysis by Wes McKinney is a great book for those who are interested in using Python as their tool of choice. Python is an extremely powerful and flexible tool for data modeling, analysis, and prediction.
With the help of packages such as Pandas and Numpy, python is a great environment to learn the tools necessary to work as a data scientist. In addition, many companies use python in their workflow and it can be even used in production environments.
This book is very dense but packed with lots of good information and can be used as a reference for years to come.
For Those Who Know Introductory Statistics:
The cover of Applied Predictive Modeling may not look exciting — but you know what they say: “Don’t judge a book by its cover.” This book assumes you have a small statistics foundation and sits comfortably above the level of an introductory statistics course.
Don’t be afraid by this book's statistic nature, however. Applied Predictive Modeling contains treasure troves of heuristics and tips for various real-world projects. In addition to learning valuable algorithms and tools, the book explains why specific decisions were made and how to make them yourself. The authors also provide various real-world examples using messy and real data and explain what decisions were made and why.
If you wish to dig into predictive analytics in real-world scenarios, this is the book to get.
For Those That Feel At Home In Spreadsheets:
Starting your data science journey can be scary and overwhelming. Not only are data scientists analysts, but they oftentimes also programmers, presenters, and database administrators among other things. However, you don’t need to dive headfirst into Python or R if you don’t want to.
Data-Smart provides a great foundation for those that are new to programming and data science but want to provide value. If you are semi-comfortable in a spreadsheet application such as Excel (and want to stay that way for now) this book is great for you.
You may not be able to create complex models ready for production in a spreadsheet, but lots of valuable insights can be gained from these programs and you can learn to provide serious value to your organization.
Don’t Stop Here
Books are an amazing resource for learning new skills. No matter your background or goals, there is a book out there for you. However, while I tout the greatness of books, don’t let them be your only resource.
Watch youtube videos, connect with other data scientists, take training or classes, and of course read blogs and publications such as Towards Data Science. And most importantly, never stop learning!