5 Best Books for Beginner Data Scientists
List of 5 best books for beginner data scientists. These books will give honest answers to questions such as: How to build a career in Data Science?How A.I. is used in the world’s most successful companies.How Data Science leaders actually work and the challenges they face.
1. Data Structures And Algorithms Made Easy
Reading books is a kind of enjoyment. Reading books is a good habit. We bring you different kinds of books. You can carry this book where ever you want. It is easy to carry. It can be an ideal gift to yourself and to your loved ones. Care instruction keeps away from fire.
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2. Practical Statistics for Data Scientists
Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.
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3. Data Scientist: The Definitive Guide to Becoming a Data Scientist
As our society transforms into a data-driven one, the role of the Data Scientist is becoming more and more important. If you want to be on the leading edge of what is sure to become a major profession in the not-too-distant future, this book can show you how. Each chapter is filled with practical information that will help you reap the fruits of big data and become a successful Data Scientist.
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4. Data Science Uncovering the Reality
This book will give you honest answers to questions such as: How to build a career in Data Science? How A.I. is used in the world’s most successful companies.How Data Science leaders actually work and the challenges they face.
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5. Machine Learning using Python
This book is written to provide a strong foundation in machine learning using Python libraries by providing real-life case studies and examples. It covers topics such as foundations of machine learning, introduction to Python, descriptive analytics, and predictive analytics. Advanced machine learning concepts such as decision tree learning, random forest, boosting, recommended systems, and text analytics are covered.
