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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

Non-tabular data

This is an elementary project. The knowledge points in this project can be found in Chapter 1, Fundamentals of Data Collection, Cleaning, and Preprocessing, and Chapter 2, Essential Statistics for Data Assessment.

The university dataset in the UCI machine learning repository is stored in a non-tabular format: https://archive.ics.uci.edu/ml/datasets/University. Please examine its format and perform the following tasks:

  1. Examine the data format visually and then write down some patterns to see whether such patterns can be used to extract the data at specific lines.
  2. Write a function that will systematically read the data file and store the data contained within in a pandas DataFrame.
  3. The data description mentioned the existence of both missing data and duplicate data. Identify the missing data and deduplicate the duplicated data.
  4. Classify the features into numerical features and categorical features.
  5. Apply min-max normalization to all the numerical...
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