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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
Authors (2):
Avinash Navlani Avinash Navlani
Profile icon Avinash Navlani
Ivan Idris Ivan Idris
Profile icon Ivan Idris
View More author details
Toc

Table of Contents (20) Chapters close

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
Linear Algebra

Both linear algebra and statistics are the foundation for any kind of data analysis activity. Statistics help us to get an initial descriptive understanding and make inferences from data. In the previous chapter, we have understood descriptive and inferential statistical measures for data analysis. On the other side, linear algebra is one of the fundamental mathematical subjects that is the core foundation for any data professional. Linear algebra is useful for working with vectors and matrices. Most of the data is available in the form of either a vector or a matrix. In-depth knowledge of linear algebra helps data analysts and data scientists understand the workflow of machine learning and deep learning algorithms, giving them the flexibility to design and modify the algorithms as per your business needs. For example, if you want to work with principal component...

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