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Python Data Analysis

You're reading from   Python Data Analysis Perform data collection, data processing, wrangling, visualization, and model building using Python

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781789955248
Length 478 pages
Edition 3rd Edition
Languages
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Authors (2):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
Avinash Navlani Avinash Navlani
Author Profile Icon Avinash Navlani
Avinash Navlani
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Toc

Table of Contents (20) Chapters Close

Preface 1. Section 1: Foundation for Data Analysis
2. Getting Started with Python Libraries FREE CHAPTER 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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