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

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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

Strategies for staying up to date

Everyone will develop their own strategy for staying up to date, and this will depend on your personality and situation in life. For me, I have been teaching and creating course materials frequently since I have worked as a professor for four years at the time of writing.

That has been a good way to stay up to date on data science developments and creating teaching materials (even something like writing a TDS or other blog post) can be a good way to stay current.

I also subscribe to The Batch newsletter from deeplearning.ai, and peruse Kaggle, Twitter, and YouTube when I can. Through working on my own projects and code for teaching and work, I also learn about new data science developments. Occasionally I will visit specific blogs, like Google's blogs and KDnuggets. Students and colleagues will also send me resources to check out. One last method I've used is the Google News app, which suggests news stories based on your interests...