Best Books for Commerce Background Students Delving into Data Science
The journey from a commerce background into data science involves a solid understanding of statistical concepts. This article explores the best books that can help commerce students transition into data science with a strong foundation in statistics. We recommend resources that are practical, intuitive, and well-suited for both R and Python programming. Let's dive in!
Understanding the Basics
Welcome to Statistics How To!
Welcome to Statistics How To! is an invaluable resource for anyone looking to learn or refresh their knowledge of statistical concepts. This website provides detailed explanations with numerous intuitive examples, making it an excellent starting point for beginners. It covers a wide range of topics from basic probability to advanced statistical techniques, ensuring a comprehensive understanding of the subject.
Choosing the Right Tools
Once you have a solid understanding of statistical concepts from the aforementioned website, you should consider implementing these concepts using tools like R and Python. These languages are incredibly powerful for data analysis and manipulation, and mastering them is essential for a career in data science.
Top Recommended Books for Statistics in Data Science
Practical Statistics for Data Scientists: 50 Essential Concepts (R Developers)
Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce and Andrew Bruce is a comprehensive guide for R developers. This book covers essential statistical concepts and their practical applications in data science, making it an ideal resource for those who want to apply their knowledge to real-world problems. It is divided into 50 concise chapters, each focusing on a specific concept or technique.
All of Statistics: A Concise Course in Statistical Inference (R Developers)
All of Statistics: A Concise Course in Statistical Inference by Larry Wasserman is another excellent choice for R developers. While it is more advanced and concise, it provides a thorough understanding of statistical inference and its applications. This book is perfect for those who want to delve deeper into the theory behind statistical methods and how they can be applied in data science.
An Introduction to Statistics with Python: With Applications in the Life Sciences (Python Developers)
An Introduction to Statistics with Python: With Applications in the Life Sciences by Thomas Haslwanter is a great resource for Python developers. It provides a solid foundation in statistics and teaches how to apply these concepts using Python. The book is rich in practical examples and applications, making it easy to understand and follow.
Think Bayes: Bayesian Statistics in Python (Python Developers)
Think Bayes: Bayesian Statistics in Python by Allen B. Downey is another excellent book for Python developers. It offers a hands-on approach to learning Bayesian statistics, which is becoming increasingly important in data science. The book uses Python code to help readers understand and apply Bayesian methods.
Think Stats 2e (Python Developers)
Think Stats 2e by Allen B. Downey is a comprehensive guide for Python developers. This book covers statistical concepts in a practical and intuitive manner, with a focus on applying these concepts to real-world problems. It is well-suited for those who want to learn statistics through practical examples and exercises.
Conclusion
Selecting the right books is crucial when transitioning from a commerce background into data science. Whether you prefer R or Python, these books will help you build a strong foundation in statistics, a critical prerequisite for a successful career in data science. Welcome to the fascinating world of data science!