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Python Data Analysis - Third Edition

You're reading from Python Data Analysis - Third Edition

Product type Book

Published in Feb 2021

Publisher Packt

ISBN-13 9781789955248

Pages 478 pages

Edition 3rd Edition

Languages

Concepts

Machine Learning

Authors (2):

Avinash Navlani

Ivan Idris

View More author details

Table of Contents (20) Chapters

Preface

1. Section 1: Foundation for Data Analysis

2. Getting Started with Python Libraries

3. NumPy and pandas

4. Statistics

5. Linear Algebra

6. Section 2: Exploratory Data Analysis and Data Cleaning

7. Data Visualization

8. Retrieving, Processing, and Storing Data

9. Cleaning Messy Data

10. Signal Processing and Time Series

11. Section 3: Deep Dive into Machine Learning

12. Supervised Learning - Regression Analysis

13. Supervised Learning - Classification Techniques

14. Unsupervised Learning - PCA and Clustering

15. Section 4: NLP, Image Analytics, and Parallel Computing

16. Analyzing Textual Data

17. Analyzing Image Data

18. Parallel Computing Using Dask

19. Other Books You May Enjoy

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Measuring central tendency

Central tendency is the trend of values clustered around the averages such as the mean, mode, and median values of data. The main objective of central tendency is to compute the center-leading value of observations. Central tendency determines the descriptive summary and provides quantitative information about a group of observations. It has the capability to represent a whole set of observations. Let's see each type of central tendency measure in detail in the coming sections.

Mean

The mean value is the arithmetic mean or average, which is computed by the sum of observations divided by the number of observations. It is sensitive to outliers and noise, with the result that whenever uncommon or unusual values are added to a group, its mean gets deviated from the typical central value. Assume x₁, x_², . . . , x _N is N observations. The formula for the mean of these values is shown here:

Let's compute the mean value of the communication skill score column...

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Authors (2)

Avinash Navlani

Avinash Navlani has over 8 years of experience working in data science and AI. Currently, he is working as a senior data scientist, improving products and services for customers by using advanced analytics, deploying big data analytical tools, creating and maintaining models, and onboarding compelling new datasets. Previously, he was a university lecturer, where he trained and educated people in data science subjects such as Python for analytics, data mining, machine learning, database management, and NoSQL. Avinash has been involved in research activities in data science and has been a keynote speaker at many conferences in India.

See other products by Avinash Navlani

Ivan Idris

Ivan Idris has an MSc in experimental physics. His graduation thesis had a strong emphasis on applied computer science. After graduating, he worked for several companies as a Java developer, data warehouse developer, and QA analyst. His main professional interests are business intelligence, big data, and cloud computing. Ivan Idris enjoys writing clean, testable code and interesting technical articles. Ivan Idris is the author of NumPy 1.5. Beginner's Guide and NumPy Cookbook by Packt Publishing.

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