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Practical Data Science with Python

You're reading from Practical Data Science with Python Learn tools and techniques from hands-on examples to extract insights from data

Product type Paperback

Published in Sep 2021

Publisher Packt

ISBN-13 9781801071970

Length 620 pages

Edition 1st Edition

Languages

Python

Tools

Excel

Concepts

Data Science

Author (1):

Nathan George

View More author details

Table of Contents (30) Chapters

Preface

1. Part I - An Introduction and the Basics

2. Introduction to Data Science FREE CHAPTER

3. Getting Started with Python

4. Part II - Dealing with Data

5. SQL and Built-in File Handling Modules in Python

6. Loading and Wrangling Data with Pandas and NumPy

7. Exploratory Data Analysis and Visualization

8. Data Wrangling Documents and Spreadsheets

9. Web Scraping

10. Part III - Statistics for Data Science

11. Probability, Distributions, and Sampling

12. Statistical Testing for Data Science

13. Part IV - Machine Learning

14. Preparing Data for Machine Learning: Feature Selection, Feature Engineering, and Dimensionality Reduction

15. Machine Learning for Classification

16. Evaluating Machine Learning Classification Models and Sampling for Classification

17. Machine Learning with Regression

18. Optimizing Models and Using AutoML

19. Tree-Based Machine Learning Models

20. Support Vector Machine (SVM) Machine Learning Models

21. Part V - Text Analysis and Reporting

22. Clustering with Machine Learning

23. Working with Text

24. Part VI - Wrapping Up

25. Data Storytelling and Automated Reporting/Dashboarding

26. Ethics and Privacy

27. Staying Up to Date and the Future of Data Science

28. Other Books You May Enjoy

29. Index

Test your knowledge

To test your knowledge of the clustering techniques we've learned, try clustering the full housing dataset or a bigger sample of the housing dataset, available in the GitHub repository for this book under Chapter17/data. You should be able to use some of the methods we learned (k-means, hierarchical clustering, DBSCAN) and be able to optimize the hyperparameters (such as the number of clusters) by using an elbow plot or other metrics, such as the silhouette score.

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Authors (1)

Nathan George

Nathan George

Nathan George is a data scientist at Tink in Stockholm, Sweden, and taught data science as a professor at Regis University in Denver, CO for over 4 years. Nathan has created online courses on Pythonic data science and uses Python data science tools for electroencephalography (EEG) research with the OpenBCI headset and public EEG data. His education includes the Galvanize data science immersive, a PhD from UCSB in Chemical Engineering, and a BS in Chemical Engineering from the Colorado School of Mines.

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