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Data Science From Scratch: The #1 Data Science Guide For Everything A Data Scientist Needs To Know: Python, Linear Algebra, Statistics, Coding, Applications, Neural Networks, And Decision Trees
The use of data science adds a lot of value to businesses, and we will continue to see the need for data scientists grow.
Data Science From Scratch: The #1 Data Science Guide For Everything A Data Scientist Needs To Know: Python, Linear Algebra, Statistics, Coding, Applications, Neural Networks, And Decision Trees
Item #: 40285692

Data Science From Scratch: The #1 Data Science Guide For Everything A Data Scientist Needs To Know: Python, Linear Algebra, Statistics, Coding,

Item #: 40285692

BND 57

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The use of data science adds a lot of value to businesses, and we will continue to see the need for data scientists grow.
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What Stands Out

Comprehensive Coverage
This guide provides thorough insights into essential topics like Python, statistics, and neural networks, ensuring readers gain a well-rounded understanding of data science crucial for aspiring data scientists.
Practical Applications
Emphasizing real-world applications, the book equips readers with practical coding skills and problem-solving techniques that can be directly applied in professional settings, enhancing employability.
Beginner-Friendly
Written for all skill levels, this guide breaks down complex concepts into digestible parts, making it accessible for beginners while still offering value to advanced practitioners looking to reinforce their knowledge.

Product Details

Get your copy of Data Science From Scratch - the #1 guide for data scientists. Learn Python, Linear Algebra, Statistics, Coding, Neural Networks & Decision Trees. Available in hardcover. Shop at Ubuy Brunei
  • Comprehensive guide covering the fundamentals of data science and its importance
  • Provides the necessary prerequisites for starting a career in data science
  • Explores the role of hacking, coding, and various coding languages in data science
  • Focuses on the significance of Python and the use of linear algebra and statistics in data science
  • Covers different applications for data science and data manipulation techniques
  • Emphasizes the increasing demand for data scientists and the value they bring to businesses
Publisher Data Science
Publication date November 10, 2019
Language English
Print length 220 pages
ISBN-10 3903331554
ISBN-13 978-3903331556
Item Weight 1.09 pounds (490 grams)
Dimensions 6 x 0.63 x 9 inches (15.2 x 1.6 x 22.9 cm)

Who Should Buy?

Suitable For
  • Aspiring Data Scientists

    Ideal for beginners looking to build a solid foundation in data science concepts and practical applications.

  • Self-Learners

    Perfect for individuals who prefer to learn data science independently without formal classroom instruction.

  • Technical Professionals

    Beneficial for software developers or engineers needing a deeper understanding of data science methodologies and tools.

Not Suitable For
  • Advanced Practitioners

    Not suitable for experienced data scientists seeking advanced theories or cutting-edge techniques beyond fundamental concepts.

Product Description

Data Science From Scratch: The #1 Data Science Guide For Everything A Data Scientist Needs To Know: Python, Linear Algebra, Statistics, Coding, Applications, Neural Networks, And Decision Trees

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Computers & Technology Editorial Review

"Data Science From Scratch" promises to be a comprehensive guide for everything a data scientist needs to know. Unfortunately, the book falls short of its title. The author presents lists of generalizations without providing specific examples, making it difficult for readers to implement the skills. Additionally, proofreading is sloppy and there is a lack of detailed guidance for becoming a data scientist. Some readers find the book helpful for learning neural networks and algorithms, but overall, it is not recommended for those looking for a proper guide to data science.

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Pros

  • Easy-to-understand descriptions of neural networks and algorithms with program examples
  • Helpful resource for students learning Python

Cons

  • Lacks specific examples and implementation guidance

Product Price History

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