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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

What is time series data?

The title of this section may seem a little odd. Is time series data somehow different from other sorts of data? The answer is yes. Look at the time series plotted in Figure 6.1. It is a plot of the UK’s monthly seasonally adjusted unemployment rate from February 1971 to December 2019 inclusive. The unemployment rate (percentage) is for all individuals aged 16 and over. I have taken the data from UK’s Office of National Statistics website – see point 2 in the Notes and further reading section. The data is publicly available and regularly updated.

Figure 6.1: Monthly UK seasonally-adjusted unemployment rate (percentage)

Figure 6.1: Monthly UK seasonally-adjusted unemployment rate (percentage)

The key here is the time aspect of the data. Each data point in a time series dataset is associated with a particular time point. This introduces two new key aspects to the data:

  • There is a natural ordering to the data, that is, we can order the data points by the timepoint, and...
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