Data Science From Scratch First Principles With Python(1st Edition)
Authors:
Joel Grus
In Stock: 1 Left
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Expected shipping within 2 - 3 DaysPopular items with books
Total Price:
$76.3
Solution Manual Includes
Price:
$9.99
Book details
ISBN: 1491904402, 9781491904404
Book publisher: O'Reilly Media
Book Price $76.3 : Data Science From Scratch: First Principles with Python by Joel Grus is an insightful guide that delves into the foundational concepts of data science using Python. This first edition is particularly crafted for beginners who aspire to grasp data science's essential components without relying heavily on pre-packaged libraries. The book emphasizes learning through implementing algorithms from the ground up, ensuring readers gain a comprehensive understanding of core data science principles such as statistics, probability, and linear algebra. Joel Grus effectively breaks down complex topics into manageable sections using Python code, making it a practical solution manual for self-learners. While the book refrains from providing an answer key for exercises, it encourages readers to engage interactively, fostering a deeper comprehension of topics. The table of contents is thoughtfully structured, beginning with straightforward programming concepts before advancing to intricate machine learning algorithms. Throughout, Grus integrates crucial methodologies including data visualization, data manipulation, and machine learning techniques such as neural networks and k-means clustering. This approach not only enhances computational skills but also equips learners with the ability to implement real-world data solutions. Since its release, the book has been well-received for its practical approach and clarity. It distinctly stands out for those seeking a bottom-up learning experience in data science, making it a preferred choice for technical education in programming and analytics. For students invested in data-centric courses, this book serves as a crucial reference, with its dense coverage of Python implementations in solving data-centric problems enriching their academic foundation. The manual offers a cheap alternative to expensive textbooks while retaining academic rigor.
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