Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Nov 2020
Publisher Packt
ISBN-13 9781838984847
Length 392 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Rongpeng Li Rongpeng Li
Author Profile Icon Rongpeng Li
Rongpeng Li
Arrow right icon
View More author details
Toc

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

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: " You can use plt.rc('ytick', labelsize='x-medium')."

A block of code is set as follows:

import pandas as pd 
df = pd.read_excel("PopulationEstimates.xls",skiprows=2) 
df.head(8)  margin: 0;

Any command-line input or output is written as follows:

$ pip install pandas

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: " seaborn is another popular Python visualization library. With it, you can write less code to obtain more professional-looking plots."

Tips or important notes

R is another famous programming language for data science and statistical analysis. There are also successful R packages. The counterpart of Matplotlib is the R ggplot2 package I mentioned above.

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 €18.99/month. Cancel anytime