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Practical Data Science with Python

You're reading from   Practical Data Science with Python Learn tools and techniques from hands-on examples to extract insights from data

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Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781801071970
Length 620 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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Nathan George Nathan George
Author Profile Icon Nathan George
Nathan George
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Table of Contents (30) Chapters Close

Preface 1. Part I - An Introduction and the Basics
2. Introduction to Data Science FREE CHAPTER 3. Getting Started with Python 4. Part II - Dealing with Data
5. SQL and Built-in File Handling Modules in Python 6. Loading and Wrangling Data with Pandas and NumPy 7. Exploratory Data Analysis and Visualization 8. Data Wrangling Documents and Spreadsheets 9. Web Scraping 10. Part III - Statistics for Data Science
11. Probability, Distributions, and Sampling 12. Statistical Testing for Data Science 13. Part IV - Machine Learning
14. Preparing Data for Machine Learning: Feature Selection, Feature Engineering, and Dimensionality Reduction 15. Machine Learning for Classification 16. Evaluating Machine Learning Classification Models and Sampling for Classification 17. Machine Learning with Regression 18. Optimizing Models and Using AutoML 19. Tree-Based Machine Learning Models 20. Support Vector Machine (SVM) Machine Learning Models 21. Part V - Text Analysis and Reporting
22. Clustering with Machine Learning 23. Working with Text 24. Part VI - Wrapping Up
25. Data Storytelling and Automated Reporting/Dashboarding 26. Ethics and Privacy 27. Staying Up to Date and the Future of Data Science 28. Other Books You May Enjoy
29. Index

Probability, Distributions, and Sampling

Life is full of uncertainty – we make decisions based on incomplete information all the time. Much of data science has to do with making decisions based on incomplete information. For example, should we show an advertisement for an exercise bike or an iPad to a website visitor? Loans provide another example; deciding on whether to give someone a loan based on their credit history and current income is a decision we might make with a machine learning algorithm. We will examine concepts of probability in this chapter, which lay the foundations for machine learning and statistical methods. Closely related to probability are sampling techniques and probability distributions. In this chapter, we'll cover:

  • Foundational probability concepts
  • Common probability distributions in data science
  • Useful sampling techniques for data science

Once we have these techniques down, it will improve our ability to apply...

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