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Essential Statistics for Non-STEM Data Analysts

You're reading from   Essential Statistics for Non-STEM Data Analysts Get to grips with the statistics and math knowledge needed to enter the world of data science with Python

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Product type Paperback
Published in Nov 2020
Publisher Packt
ISBN-13 9781838984847
Length 392 pages
Edition 1st Edition
Languages
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Author (1):
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Rongpeng Li Rongpeng Li
Author Profile Icon Rongpeng Li
Rongpeng Li
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Getting Started with Statistics for Data Science
2. Chapter 1: Fundamentals of Data Collection, Cleaning, and Preprocessing FREE CHAPTER 3. Chapter 2: Essential Statistics for Data Assessment 4. Chapter 3: Visualization with Statistical Graphs 5. Section 2: Essentials of Statistical Analysis
6. Chapter 4: Sampling and Inferential Statistics 7. Chapter 5: Common Probability Distributions 8. Chapter 6: Parametric Estimation 9. Chapter 7: Statistical Hypothesis Testing 10. Section 3: Statistics for Machine Learning
11. Chapter 8: Statistics for Regression 12. Chapter 9: Statistics for Classification 13. Chapter 10: Statistics for Tree-Based Methods 14. Chapter 11: Statistics for Ensemble Methods 15. Section 4: Appendix
16. Chapter 12: A Collection of Best Practices 17. Chapter 13: Exercises and Projects 18. Other Books You May Enjoy

Chapter 2: Essential Statistics for Data Assessment

In Chapter 1, Fundamentals of Data Collection, Cleaning, and Preprocessing, we learned about data collection, basic data imputation, outlier removal, and standardization. Hence, this will provide you with a good foundation to understand this chapter.

In this chapter, you are going to learn how to examine the essential statistics for data assessment. Essential statistics are also often referred to as descriptive statistics. Descriptive statistics provide simple, quantitative summaries of datasets, usually combined with descriptive graphics. For example, descriptive statistics can demonstrate the tendency of centralization or measures of the variability of features, and so on.

Descriptive statistics are important. Correctly represented descriptive statistics give you a precise summary of the datasets at your disposal. In this chapter, we will learn to extract information and make quantitative judgements from descriptive statistics...

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