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Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics

Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics

Essential Math for Data Science: Take Control of Your Data with Fundamental
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Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics Paperback - 2022

by Thomas Nield

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1. Paperback. Good. Condition: Good; ISBN: 9781098102937; Edition: 1. Once purchased, we honor returns due to quality issues but won't accept reasons like 'changed my mind', 'find better price', 'order by mistake', etc
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Founded in 2023 in U.S. East Coast, TextbookSavvy Inc is a national distributor of used (pre-owned) textbooks and relevant education materials serving clients in the United States, the UK, and Canada.

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**Once order is placed, TextbookSavvy Inc may not accept returns for reasons such as "changed my mind", "school book requirement changes", "found a better price", "ordered the wrong book", etc. We only accept returns due to book quality issues, but return period / refund eligiblity will expire after 3 days from the day on which you acquire physical possession of the last good. As a used book seller, our books may contain light highlight or slight damages to its cover, but readability isn't impacted. Books may not contain access codes. **

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From the publisher

Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career.

Learn how to:

  • Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning
  • Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon
  • Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance
  • Manipulate vectors and matrices and perform matrix decomposition
  • Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks
  • Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market
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