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Foundations of Statistics for Data Scientists: With R and Python (Chapman & Hall/CRC Texts in Statistical Science)
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Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists.
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What Stands Out
Rincian Produk
- Comprehensive guide on statistics for data scientists
- Covers the use of R and Python
- Published by Chapman & Hall/CRC Texts in Statistical Science
- 1st Edition
- Provides essential foundation for understanding statistical concepts
- Ideal for data science professionals and students
| Publisher | Chapman and Hall/CRC |
| Publication date | November 30, 2021 |
| Edition | 1st |
| Language | English |
| Print length | 468 pages |
| ISBN-10 | 0367748452 |
| ISBN-13 | 978-0367748456 |
| Item Weight | 2.6 pounds (1.18 kg) |
| Dimensions | 7.1 x 1.2 x 9.9 inches (18 x 3 x 25.1 cm) |
| Part of series | Chapman & Hall/CRC Texts in Statistical Science |
Who Should Buy?
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Aspiring Data Scientists
Great for beginners wanting to understand statistical concepts essential for data analysis using R and Python.
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Statistical Professionals
Ideal for professionals seeking a comprehensive resource to solidify their statistical knowledge and programming skills.
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Academic Students
Useful for university students enrolled in statistics or data science courses, combining theory with practical programming exercises.
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Advanced Statisticians
Not suitable for those already advanced in statistics, as the content may be too basic or introductory.
DESKRIPSI PRODUK
About This Item
Introducing the Foundations of Statistics for Data Scientists: With R and Python Chapman & HallCRC Texts in Statistical Science 1st Edition! If you're a data scientist looking to enhance your statistical knowledge and skills, this textbook is a must-have for your collection. Created by Chapman & HallCRC Texts, known for their reliable and authoritative texts in statistical science, this book is designed to provide you with a solid foundation in statistics using popular programming languages R and Python. With its comprehensive coverage and clear explanations, it's suitable for both novice and advanced learners. By delving into the world of statistics, you'll discover powerful techniques and methodologies that can be applied in various domains such as e-commerce, digital marketing, and data analysis. With the growing significance of data-driven decision making, having a strong understanding of statistics is invaluable in today's competitive landscape. This textbook covers essential topics such as e-commerce platforms, online selling strategies, digital marketing for e-commerce, inventory management, customer experience in e-commerce, website design, payment gateways, tracking and analytics, conversion rate optimization, mobile e-commerce, social media marketing, SEO, shipping and fulfillment, marketplace selling, customer retention, competitive analysis, e-commerce trends, branding, product photography, customer reviews, customer segmentation, omnichannel marketing, pricing strategies, data analysis, customer support, content marketing, email marketing, loyalty programs, and cross-selling and upselling. Whether you're a data scientist looking to enhance your skill set or an e-commerce professional seeking to leverage statistical techniques for business growth, this book offers a comprehensive and practical approach.
The inclusion of R and Python code examples ensures that you can apply the concepts directly to your real-world projects. Stay ahead of the curve and take your statistical skills to new heights with the Foundations of Statistics for Data Scientists: With R and Python Chapman & HallCRC Texts in Statistical Science 1st Edition. Order your copy today and unlock the power of data-driven decision making in your endeavors.
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IDR 2743416
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Fitur & Manfaat
- Textbook designed for 1-2 term mathematical statistics course for students training to be data scientists
- Emphasis on using software to implement statistical methods and conduct simulations to illustrate key concepts
- Introduces modern topics relevant for data scientists, like Bayesian inference and generalized linear models
- Includes nearly 500 exercises grouped into Data Analysis and Applications and Methods and Concepts
- Appendices introduce R and Python and contain solutions for odd-numbered exercises
- Website has expanded appendices and all data sets from examples and exercises
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