In the age where data is the new oil, the demand for data professionals continues to grow. Data science has become a cornerstone of many industries. For those seeking to enter this dynamic field, building a good foundation is essential.
Books are the ever-trendy source of knowledge, and in this article, we’ll take a look at eight must-have books that will teach you the basics of data science, making your journey into this fascinating world more accessible yet enjoyable and loveable.
Books have timeless power. They have the magical ability to transport us to different worlds and reveal the deepest secrets of knowledge and human experience. In a world saturated with digital information that we consume on a daily basis without even realizing it, books are a source of wisdom that would give us a lot, and ask for only one thing in return – a little time. According to Analytics Insights, these are the books that would be of great use to you if you choose to pursue a career in this field.
Top Books You Should Read
“Python for Data Analysis” by Wes McKinney
Wes McKinney’s book is a fantastic starting point for beginners. It focuses on the practical use of Python, one of the most popular programming languages in data science. You’ll learn how to work with data structures, perform data cleaning, and apply statistical analysis. The book also introduces the powerful Pandas library for data manipulation.
“Data Science for Business” by Foster Provost and Tom Fawcett
Data science isn’t just about algorithms; it’s also about understanding the business applications. This book delves into the practical aspects of data science for business, helping you grasp the value of data-driven decision-making.
“The Elements of Statistical Learning” by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
For a more in-depth understanding of the statistical foundations of data science, this book is a staple. It covers a wide range of statistical techniques and machine learning algorithms and is ideal for those looking to delve deeper into the mathematical aspects of data science.
“Data Science for Dummies” by Lillian Pierson
True to the “For Dummies” series’ tradition, this book breaks down complex data science concepts into easily digestible pieces. It’s an excellent choice for beginners who want to explore the basics and gain a holistic understanding of data science.
“Introduction to the Theory of Statistics” by Alexander McFarlane Mood, Franklin A. Graybill, and Duane C. Boes
This classic text is an introduction to the theoretical foundations of statistics. While it may be more mathematically rigorous, it’s a valuable resource for those who want to dive deep into the mathematical underpinnings of statistics and data analysis.
“Machine Learning Yearning” by Andrew Ng
Written by the co-founder of Google Brain and an AI pioneer, Andrew Ng’s book is focused on the practical aspects of machine learning. It’s a must-read for those interested in the hands-on application of machine learning techniques.
“Python Machine Learning” by Sebastian Raschka and Vahid Mirjalili
For those looking to harness the power of Python for machine learning, this book is an excellent resource. It covers key machine-learning concepts and practical implementation using Python libraries like Scikit-Learn.
“Storytelling with Data” by Cole Nussbaumer Knaflic
Data visualization is a crucial aspect of data science. This book guides you on how to convey data-driven insights through compelling and impactful data visualizations effectively.
