From basic statistics to complex data sets only used by advanced computer scientists, data science has risen the charts in every industry. In previous years, everyone thought data was just an obscure topic requiring mastery from professionals in the tech industry. Now, data is everywhere. From that super bowl ticket purchase to a burger at MacDonald’s to an employee payroll – we all enter data points into vast computer intelligence databases every day.
One of the benchmarks needed to succeed in data science is the knowledge of Python. The programming language has been the most sort after among data professionals due to its excellent libraries, interactive community, clean datasets, and high versatility. Python has become a mainstay in the data industry.
Data scientists can use python in almost any field such as AI, Machine learning, Big Data, Server Side development, Automation, and many more.
You might be asking, is it really necessary to master python as a data science beginner?
Yes.
The early stages of data science are the perfect time to acquire as many valuable resources that will help your analysis in the future. It’s not a good idea to leave python out of your frameworks.
Python started off in 1991 as a scripting solution for basic web development, but now python is the driving language behind data science as a whole.
Personally, books helped me a lot when I got started with data. I have selected five essential books to kickstart and navigate your career in data using Python. Some of these are my personal favorites and are applicable to both newcomers and professionals.
1. Hands-On Data Structures and Algorithms with Python – By Dr. Basant Agarwal.
Data structures are critical components when analyzing data. They are versatile in solving a lot of problems, act like reusable codes, and allow you to use, store, and arrange data successfully. Data structures are sometimes tricky – they perform virtually any role in data analysis – most professionals often try to fit in one for a host of datasets.
That’s one of the major purposes of this book: to help you explore and then understand the analysis and design of python data structures.
After reading "Hands-On Data Structures and Algorithms with Python," you will be able to develop complex data structures, merge algorithms, sort insertion, practice concepts such as Big O notation, hash tables, stacks, and queues, algorithm design, modeling, and much more.
From a personal perspective, alongside model fitting – data structure is one of those topics where you don’t really grasp the concepts at first. Don’t rush it. The book is 400 pages long, take it bit by bit, and make sure you are able to understand and perform operations with each chapter before moving to the next. It’s a total game changer when you get familiar with data structures and algorithms with Python.
2. Introduction to Machine Learning with Python: A Guide for Data Scientists – By Andreas C. Müller and Sarah Guido.
When I started my data science career, I wasted a lot of time running away from programming. Worst decision ever. When I finally decided to mix programming with data a couple of years back, I started off with python. "Introduction to Machine Learning with Python" was the first book I read. Loved it ever since.
Understanding Machine learning is paramount for a professional in the data science industry. Building datasets, developing and working with models, utilizing frameworks, and managing databases are important aspects of working with data. Most of these aspects are designed and built with the concepts of machine learning.
This book breaks down the fundamentals of machine learning using python. So even if you don’t have previous knowledge of python, you will benefit massively from the various programming techniques explained in the book.
3. The Art of Data Science – by Roger D. Peng and Elizabeth Matsui.
The Art of Data Science dives completely into the fundamentals of data science. It navigates around the inner workings of data analysis, how to find data, tell stories with data, and filter it into efficient results.
Most times data doesn’t require a good mathematician – it just requires a good storyteller. A data scientist with forehand communication knowledge and good python skills will be able to explore any pool of data.
The Authors, Peng and Matsui, both have impressive portfolios in managing data projects as well as being responsible for the growth of the most successful data professionals in the industry.
In this book, they use their experiences to guide both beginners and advanced learners through various concepts of analyzing basic and complex datasets.
4. Automate the Boring Stuff With Python: Practical Programming— by Al Sweigart.
For data scientists and programmers who are finding it difficult to transition into python, this is my favorite pick for you. Prior to its release, lots of professionals expressed their strong need for a book with pure practical solutions to working with real-life data science projects.
If you are someone who learns by doing things in real-time, this book is for you.
It will teach you all the basic practical things like scrapping data on the web, filtering datasets, appending into XLS, interpreting a database, module selection, and sorting algorithms all using python.
After reading the book, it is highly recommended that you follow up on an online course, "Automate the Boring Stuff with Python Programming," created by the author on Udemy. It’s a good idea to get updated solutions on some tips discussed in the book.
5. Learning Python, 5th Edition – by Mark Lutz.
From beginners to professional developers, Mark Lutz carries everyone along in his complete, essential introduction to Python. A very powerful book for data scientists who want to understand the interworking of core Python and its diverse libraries.
It covers significant things you need to know about Python and its functions. You’ll be able to complete quizzes, and exercises and practice self-understanding tutorials. It gets you started with version 2.7 and then transits into 3.3.
Applicable takeaways
Programming in data science might seem like a daunting task, but with an easy-to-learn language such as Python, you are well on your way to mastery. Whether you are a beginner just starting out with python or a professional looking to expand your knowledge, these books will help you a lot. Some are only a few pages, others are long reads. It doesn’t matter, just ensure you get maximum value from each chapter.
