Defining Data Science is like defining nature. It should be easy as it is all around us, but expressing it in words is difficult. It is a lot more than what we perceive.
This world is data-driven. Today, data is not something to be managed but something to be valued. Data science includes a set of standards and calculations for finding valuable patterns from enormous data sets. It is largely connected to data mining and artificial intelligence, but more extensive in scope. It utilizes various speculations and procedures drawn from various fields such as computer science, statistics, and mathematics.
In this world of transformation, the demand for a data scientist is increasing, and a person with incredible mathematical acumen can become a good data scientist. Data scientists can identify customers' expectations by analysing their regular habits and hobbies. They help to figure out competitors and their strategies. They define short-term and long-term objectives and accordingly provide the responsibilities to different staff members. They measure the success of the strategies and introduce changes if needed. To state clearly, data scientists monetize the data. There had been examples that a few companies have earned millions using data science.
Prerequisites for becoming a good Data Scientist
So, to figure out if you can become a good data scientist or not, ask yourself these four questions:
- Do you enjoy challenges?
- Do you enjoy problem-solving?
- Do you have an interest in widening the creative areas of your mind?
- Do you like collecting data and analysing it?
If your answers are positive, then go ahead and enjoy this multidisciplinary field and become a reliable data scientist.
To understand more about data science, it is advisable to read related books or enroll in
data science certification course online. The books will build a foundation and help you in broadening your skills.
What to expect from a Data Science book?
Before giving you book recommendations, you must first understand what data science books should hold.
- The books should have clear, exact, and straightforward language.
- The books should instruct and make sense of the fundamental ideas of data science.
- The books should contain different informative materials including worksheets, model answers, learning activities, and various highlights that confirm the reader's engagement.
- The books should contain tasks for practice and active experience.
Now, let's cover the best books to follow to build a career as a data scientist.
What Is Data Science? by Mike Loukides- Published 2011
This book will give a
kick start to your data science journey. It gives consolidated details about the skills, tools, and software required to start the journey. It also highlights the importance of data science in the job market.
Data Science for Business: What you need to know about data mining and data-analytic thinking by Foster Provost- Published 2013
This book is simple, non-mathematical, and full of practical anecdotes. It knits together all the fundamentals of data science. Most importantly, it tells you how to think in a 'data-analytics' manner. The concluding chapters draw upon how to choose and apply the techniques that you have learned practically.
Data Science from Scratch: First Principles with Python by Joel Grus-Published 2015
As the book title suggests, it uses the 'from scratch' approach. It gives an intuitive explanation of the analysis of data and how to use algorithms. It also shows some famous data sets and how to implement the science of algorithms on those data. In addition to that, this book contains codes of Python language so it is a bonus for those who are learning this computer language.
Learning From Data: A Short Course by Yaser S. Abu-Mostafa- Published 2012
This book is excellent for
understanding Machine Learning. It provides theoretical as well as practical knowledge of Machine Learning. It does an incredible job at explaining how and when statistical methods work.
An Introduction to Statistical Learning: With Applications in R by Gareth James- Published 2013
This book is great for understanding the nuts and bolts of the algorithms. The best part of this book is that each chapter covers one specific algorithm and at the end of each chapter, there is R code that can help you apply the algorithm to real-life data. However, you need to have basic mathematics or statistical background to make the best use of this book.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie- Published 2001
This book contains relevant Machine Learning methods/tools that are extensively used in practice today. The chapters of this book are well-structured with liberal use of graphics.
Data Smart: Using Data Science to Transform Information into Insight by John W. Foreman- Published 2013
This book provides hands-on experience in data science. If you are looking for proper learning on clustering and advanced analytics, then this is a great book to consider. This is a challenging book, but the author has made it interesting by using his sense of humour.
Big Data: A Revolution That Will Transform How We Live, Work, and Think by Viktor Mayer-Schönberger- Published 2013
This book is a must-read for those who want to know the power that data carries along. Through this book, you can understand what big companies (Google, Facebook, Amazon, Twitter) in the world can do with our data- how they record our behaviour patterns, likes, and dislikes and appropriately use them.
Conclusion
Data Science is the most popular job profile that nearly everybody is discussing these days. Being a data scientist is not a cakewalk; however, with the right resources, one can build a successful career in this profile. And what's the best resource than books? Books are the traditional way of grasping knowledge on the topic. The books are written by the most experienced authors, and they share their knowledge and experience in the written words. The above books are some of the best books written by great authors. Read the reviews of the books and choose to read as many written materials to get a good hold of how data plays a significant role in this competitive world.
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