Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781837634187
Length 510 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
David Hoyle David Hoyle
Author Profile Icon David Hoyle
David Hoyle
Arrow right icon
View More author details
Toc

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

Graphs and network data

In the introduction, we mentioned that much of the real-world data you will encounter as a data scientist is network data. However, not all real-world data is network data. So, how do we recognize when we are dealing with network data, and perhaps more importantly, how do we recognize when the network aspect of the data is relevant to how we analyze the data?

Network data is about relationships

In the introduction, we explained that we need to learn about network data because the things that produce the data are linked to each other. This tells us that network data is about relationships. Or rather, network data arises when we have relationships between many of the data-generating entities we are studying. This also gives us a useful rule-of-thumb for when we should take the network aspect of the data into account in our analysis:

  • If the relationships between the entities we are studying are strong, then we can’t ignore the network aspect...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image