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15 Math Concepts Every Data Scientist Should Know

You're reading from   15 Math Concepts Every Data Scientist Should Know Understand and learn how to apply the math behind data science algorithms

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781837634187
Length 510 pages
Edition 1st Edition
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Author (1):
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David Hoyle David Hoyle
Author Profile Icon David Hoyle
David Hoyle
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Essential Concepts
2. Chapter 1: Recap of Mathematical Notation and Terminology FREE CHAPTER 3. Chapter 2: Random Variables and Probability Distributions 4. Chapter 3: Matrices and Linear Algebra 5. Chapter 4: Loss Functions and Optimization 6. Chapter 5: Probabilistic Modeling 7. Part 2: Intermediate Concepts
8. Chapter 6: Time Series and Forecasting 9. Chapter 7: Hypothesis Testing 10. Chapter 8: Model Complexity 11. Chapter 9: Function Decomposition 12. Chapter 10: Network Analysis 13. Part 3: Selected Advanced Concepts
14. Chapter 11: Dynamical Systems 15. Chapter 12: Kernel Methods 16. Chapter 13: Information Theory 17. Chapter 14: Non-Parametric Bayesian Methods 18. Chapter 15: Random Matrices 19. Index 20. Other Books You May Enjoy

Exercises

The following is a series of exercises. Answers to all these exercises can be found in the Answers_to_Exercises_Chap10.ipynb Jupyter Notebook in this book’s GitHub repository:

  1. The Zachary Karate Club is a well-known network in the field of network science, so much so that a copy of the network is stored in the NetworkX package and can be accessed via the karate_club_graph() function. Use this function to create the karate club graph and then use the community.greedy_modularity_communities function to identify the communities within the graph. You can assume that there are two communities, so you should look at how to use the cutoff and best_n parameters of the community.greedy_modularity_communities function to ensure that only two communities are found. Which nodes do you think are at the center of each of the two communities found?
  2. Use the scale_free_graph function of the NetworkX package to create a scale-free graph with 10,000 nodes. Having generated...
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