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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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Understanding attributes and their types

Data is the collection of raw facts and statistics such as numbers, words, and observations. An attribute is a column or data field or series that represents the characteristics of an object and is also known as a variable, a feature, or a dimension. Statisticians use the term variable, while machine learning engineers prefer the term feature. The term dimension is used in data warehousing, while database professionals use the term attribute.

Types of attributes

The data type of attributes is more crucial for data analysis because certain situations require certain data types. The data type of attributes helps analysts select the correct method for data analysis and visualization plots. The following list shows the various attributes:

Nominal attributes: Nominal refers to names or labels of categorized variables. The value of a nominal attribute can be the symbol or name of items. The values are categorical, qualitative, and unordered in nature...

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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.

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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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